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Sample records for chl improve predictions

  1. Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

    Science.gov (United States)

    Aksoy, A.; Yuzugullu, O.

    2017-12-01

    Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

  2. Light Quality Affects Chloroplast Electron Transport Rates Estimated from Chl Fluorescence Measurements.

    Science.gov (United States)

    Evans, John R; Morgan, Patrick B; von Caemmerer, Susanne

    2017-10-01

    Chl fluorescence has been used widely to calculate photosynthetic electron transport rates. Portable photosynthesis instruments allow for combined measurements of gas exchange and Chl fluorescence. We analyzed the influence of spectral quality of actinic light on Chl fluorescence and the calculated electron transport rate, and compared this with photosynthetic rates measured by gas exchange in the absence of photorespiration. In blue actinic light, the electron transport rate calculated from Chl fluorescence overestimated the true rate by nearly a factor of two, whereas there was closer agreement under red light. This was consistent with the prediction made with a multilayer leaf model using profiles of light absorption and photosynthetic capacity. Caution is needed when interpreting combined measurements of Chl fluorescence and gas exchange, such as the calculation of CO2 partial pressure in leaf chloroplasts. © Crown copyright 2017.

  3. Dualities in CHL-models

    Science.gov (United States)

    Persson, Daniel; Volpato, Roberto

    2018-04-01

    We define a very general class of CHL-models associated with any string theory S (bosonic or supersymmetric) compactified on an internal CFT C× Td . We take the orbifold by a pair (g, δ) , where g is a (possibly non-geometric) symmetry of C and δ is a translation along T n . We analyze the T-dualities of these models and show that in general they contain Atkin–Lehner type symmetries. This generalizes our previous work on N=4 CHL-models based on heterotic string theory on T 6 or type II on K3× T2 , as well as the ‘monstrous’ CHL-models based on a compactification of heterotic string theory on the Frenkel–Lepowsky–Meurman CFT V\

  4. Metabolic engineering of the Chl d-dominated cyanobacterium Acaryochloris marina: production of a novel Chl species by the introduction of the chlorophyllide a oxygenase gene.

    Science.gov (United States)

    Tsuchiya, Tohru; Mizoguchi, Tadashi; Akimoto, Seiji; Tomo, Tatsuya; Tamiaki, Hitoshi; Mimuro, Mamoru

    2012-03-01

    In oxygenic photosynthetic organisms, the properties of photosynthetic reaction systems primarily depend on the Chl species used. Acquisition of new Chl species with unique optical properties may have enabled photosynthetic organisms to adapt to various light environments. The artificial production of a new Chl species in an existing photosynthetic organism by metabolic engineering provides a model system to investigate how an organism responds to a newly acquired pigment. In the current study, we established a transformation system for a Chl d-dominated cyanobacterium, Acaryochloris marina, for the first time. The expression vector (constructed from a broad-host-range plasmid) was introduced into A. marina by conjugal gene transfer. The introduction of a gene for chlorophyllide a oxygenase, which is responsible for Chl b biosynthesis, into A. marina resulted in a transformant that synthesized a novel Chl species instead of Chl b. The content of the novel Chl in the transformant was approximately 10% of the total Chl, but the level of Chl a, another Chl in A. marina, did not change. The chemical structure of the novel Chl was determined to be [7-formyl]-Chl d(P) by mass spectrometry and nuclear magnetic resonance spectroscopy. [7-Formyl]-Chl d(P) is hypothesized to be produced by the combined action of chlorophyllide a oxygenase and enzyme(s) involved in Chl d biosynthesis. These results demonstrate the flexibility of the Chl biosynthetic pathway for the production of novel Chl species, indicating that a new organism with a novel Chl might be discovered in the future.

  5. CHL1 is involved in human breast tumorigenesis and progression

    Energy Technology Data Exchange (ETDEWEB)

    He, Li-Hong [Medical Department of Breast Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Key Laboratory of Breast Cancer Prevention and Treatment of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Ma, Qin [Department of Oncology, The General Hospital of Tianjin Medical University, Tianjin (China); Shi, Ye-Hui [Medical Department of Breast Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Key Laboratory of Breast Cancer Prevention and Treatment of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Ge, Jie; Zhao, Hong-Meng [Key Laboratory of Breast Cancer Prevention and Treatment of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Breast Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Li, Shu-Fen [Medical Department of Breast Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Key Laboratory of Breast Cancer Prevention and Treatment of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Tong, Zhong-Sheng, E-mail: 83352162@qq.com [Medical Department of Breast Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China); Key Laboratory of Breast Cancer Prevention and Treatment of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin (China)

    2013-08-23

    Highlights: •CHL1 is down-regulation in breast cancer tissues. •Down-regulation of CHL1 is related to high grade. •Overexpression of CHL1 inhibits breast cancer cell proliferation and invasion in vitro. •CHL1 deficiency induces breast cancer cell proliferation and invasion both in vitro and in vivo. -- Abstract: Neural cell adhesion molecules (CAM) play important roles in the development and regeneration of the nervous system. The L1 family of CAMs is comprised of L1, Close Homolog of L1 (CHL1, L1CAM2), NrCAM, and Neurofascin, which are structurally related trans-membrane proteins in vertebrates. Although the L1CAM has been demonstrated play important role in carcinogenesis and progression, the function of CHL1 in human breast cancer is limited. Here, we found that CHL1 is down-regulated in human breast cancer and related to lower grade. Furthermore, overexpression of CHL1 suppresses proliferation and invasion in MDA-MB-231 cells and knockdown of CHL1 expression results in increased proliferation and invasion in MCF7 cells in vitro. Finally, CHL1 deficiency promotes tumor formation in vivo. Our results may provide a strategy for blocking breast carcinogenesis and progression.

  6. CHL1 is involved in human breast tumorigenesis and progression

    International Nuclear Information System (INIS)

    He, Li-Hong; Ma, Qin; Shi, Ye-Hui; Ge, Jie; Zhao, Hong-Meng; Li, Shu-Fen; Tong, Zhong-Sheng

    2013-01-01

    Highlights: •CHL1 is down-regulation in breast cancer tissues. •Down-regulation of CHL1 is related to high grade. •Overexpression of CHL1 inhibits breast cancer cell proliferation and invasion in vitro. •CHL1 deficiency induces breast cancer cell proliferation and invasion both in vitro and in vivo. -- Abstract: Neural cell adhesion molecules (CAM) play important roles in the development and regeneration of the nervous system. The L1 family of CAMs is comprised of L1, Close Homolog of L1 (CHL1, L1CAM2), NrCAM, and Neurofascin, which are structurally related trans-membrane proteins in vertebrates. Although the L1CAM has been demonstrated play important role in carcinogenesis and progression, the function of CHL1 in human breast cancer is limited. Here, we found that CHL1 is down-regulated in human breast cancer and related to lower grade. Furthermore, overexpression of CHL1 suppresses proliferation and invasion in MDA-MB-231 cells and knockdown of CHL1 expression results in increased proliferation and invasion in MCF7 cells in vitro. Finally, CHL1 deficiency promotes tumor formation in vivo. Our results may provide a strategy for blocking breast carcinogenesis and progression

  7. Mammalian ChlR1 has a role in heterochromatin organization

    International Nuclear Information System (INIS)

    Inoue, Akira; Hyle, Judith; Lechner, Mark S.; Lahti, Jill M.

    2011-01-01

    The ChlR1 DNA helicase, encoded by DDX11 gene, which is responsible for Warsaw breakage syndrome (WABS), has a role in sister-chromatid cohesion. In this study, we show that human ChlR1 deficient cells exhibit abnormal heterochromatin organization. While constitutive heterochromatin is discretely localized at perinuclear and perinucleolar regions in control HeLa cells, ChlR1-depleted cells showed dispersed localization of constitutive heterochromatin accompanied by disrupted centromere clustering. Cells isolated from Ddx11 -/- embryos also exhibited diffuse localization of centromeres and heterochromatin foci. Similar abnormalities were found in HeLa cells depleted of combinations of HP1α and HP1β. Immunofluorescence and chromatin immunoprecipitation showed a decreased level of HP1α at pericentric regions in ChlR1-depleted cells. Trimethyl-histone H3 at lysine 9 (H3K9-me3) was also modestly decreased at pericentric sequences. The abnormality in pericentric heterochromatin was further supported by decreased DNA methylation within major satellite repeats of Ddx11 -/- embryos. Furthermore, micrococcal nuclease (MNase) assay revealed a decreased chromatin density at the telomeres. These data suggest that in addition to a role in sister-chromatid cohesion, ChlR1 is also involved in the proper formation of heterochromatin, which in turn contributes to global nuclear organization and pleiotropic effects. -- Highlights: → New role for ChlR1 (DDX11), a cohesinopathy gene, in heterochromatin organization. → Loss of ChlR1 altered heterochromatin localization and centromere clustering. → Reduced ChlR1 levels also reduced HP1α and H3K9-me3 binding to pericentric DNA. → Decreased DNA methylation was found in pericentric repeats of Ddx11 -/- embryos. → These findings will aid in understanding the pathogenesis of Warsaw breakage syndrome.

  8. Can a Satellite-Derived Estimate of the Fraction of PAR Absorbed by Chlorophyll (FAPAR(sub chl)) Improve Predictions of Light-Use Efficiency and Ecosystem Photosynthesis for a Boreal Aspen Forest?

    Science.gov (United States)

    Zhang, Qingyuan; Middleton, Elizabeth M.; Margolis, Hank A.; Drolet, Guillaume G.; Barr, Alan A.; Black, T. Andrew

    2009-01-01

    Gross primary production (GPP) is a key terrestrial ecophysiological process that links atmospheric composition and vegetation processes. Study of GPP is important to global carbon cycles and global warming. One of the most important of these processes, plant photosynthesis, requires solar radiation in the 0.4-0.7 micron range (also known as photosynthetically active radiation or PAR), water, carbon dioxide (CO2), and nutrients. A vegetation canopy is composed primarily of photosynthetically active vegetation (PAV) and non-photosynthetic vegetation (NPV; e.g., senescent foliage, branches and stems). A green leaf is composed of chlorophyll and various proportions of nonphotosynthetic components (e.g., other pigments in the leaf, primary/secondary/tertiary veins, and cell walls). The fraction of PAR absorbed by whole vegetation canopy (FAPAR(sub canopy)) has been widely used in satellite-based Production Efficiency Models to estimate GPP (as a product of FAPAR(sub canopy)x PAR x LUE(sub canopy), where LUE(sub canopy) is light use efficiency at canopy level). However, only the PAR absorbed by chlorophyll (a product of FAPAR(sub chl) x PAR) is used for photosynthesis. Therefore, remote sensing driven biogeochemical models that use FAPAR(sub chl) in estimating GPP (as a product of FAPAR(sub chl x PAR x LUE(sub chl) are more likely to be consistent with plant photosynthesis processes.

  9. The CEBAF control system for the CHL

    International Nuclear Information System (INIS)

    Keesee, M.S.; Bevins, B.S.

    1996-01-01

    The CEBAF Central Helium Liquefier (CHL) control system consists of independent safety controls located at each subsystem, CAMAC computer interface hardware, and a CEBAF-designed control software called Thaumaturgic Automated Control Logic (TACL). The paper describes how control software was interfaced with the subsystems of the CHL. Topics of configuration, editing, operator interface, datalogging, and internal logic functions are presented as they relate to the operational needs of the helium plant. The paper also describes the effort underway to convert from TACL to the Experimental Physics and Industrial Control System (EPICS), the new control system for the CEBAF accelerator. This software change will require customizing EPICS software to cryogenic process control

  10. Induction of malignant transformation in CHL-1 cells by exposure to tritiated water

    International Nuclear Information System (INIS)

    Zou Shu'ai; Wang Hui

    1992-01-01

    The induction of neoplastic transformation in CHL-1 cells by low-dose-rate exposure to tritiated water was reported. CHL-1 cells were exposed to tritiated water (9.25 x 10 5 - 3.7 x 10 6 Bq/mL) for 24-96 hours and the accumulated doses were estimated to be 0.055-0.88 Gy, respectively. Neoplastic transformation was found in all exposed cell groups. The morphological study and transplantation test was carried out for demonstration malignancy of the transformed cells and the results show that they are with the morphology and behaviour for malignant tumour cells. For CHL-1 cells exposed to various doses of tritiated water, transformation rates were found to be from 3.28% to 13.0% at dose of 0.055-0.88 Gy. In order to estimate RBE of tritium for malignant transformation in CHL-1 cells, the induction of malignant transformation in CHL-1 cells by exposure to 137 Cs gamma-rays was carried out at dose rates of 0.359 Gy/24 hr and transformation rates for irradiated CHL-1 cells were found to be from 2.59% to 13.4%. Based on these data, RBE of tritium for malignant transformation in CHL-1 cells was estimated to be 1.6

  11. Evolution of light-harvesting complex proteins from Chl c-containing algae

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    Puerta M Virginia

    2011-04-01

    Full Text Available Abstract Background Light harvesting complex (LHC proteins function in photosynthesis by binding chlorophyll (Chl and carotenoid molecules that absorb light and transfer the energy to the reaction center Chl of the photosystem. Most research has focused on LHCs of plants and chlorophytes that bind Chl a and b and extensive work on these proteins has uncovered a diversity of biochemical functions, expression patterns and amino acid sequences. We focus here on a less-studied family of LHCs that typically bind Chl a and c, and that are widely distributed in Chl c-containing and other algae. Previous phylogenetic analyses of these proteins suggested that individual algal lineages possess proteins from one or two subfamilies, and that most subfamilies are characteristic of a particular algal lineage, but genome-scale datasets had revealed that some species have multiple different forms of the gene. Such observations also suggested that there might have been an important influence of endosymbiosis in the evolution of LHCs. Results We reconstruct a phylogeny of LHCs from Chl c-containing algae and related lineages using data from recent sequencing projects to give ~10-fold larger taxon sampling than previous studies. The phylogeny indicates that individual taxa possess proteins from multiple LHC subfamilies and that several LHC subfamilies are found in distantly related algal lineages. This phylogenetic pattern implies functional differentiation of the gene families, a hypothesis that is consistent with data on gene expression, carotenoid binding and physical associations with other LHCs. In all probability LHCs have undergone a complex history of evolution of function, gene transfer, and lineage-specific diversification. Conclusion The analysis provides a strikingly different picture of LHC diversity than previous analyses of LHC evolution. Individual algal lineages possess proteins from multiple LHC subfamilies. Evolutionary relationships showed

  12. Procurement and commissioning of the CHL refrigerator at CEBAF

    International Nuclear Information System (INIS)

    Chronis, W.C.; Arenius, D.M.; Bevins, B.S.; Ganni, V.; Kashy, D.H.; Keesee, M.M.; Reid, T.R.; Wilson, J.D.

    1996-01-01

    The CEBAF Central Helium Liquefier (CHL) provides 2K refrigeration to the 338 superconducting niobium cavities in two 400 MeV linacs and one 45 MeV injector. The CHL consists of three first stage and three second stage compressors, a 4.5K cold box, a 2K cold box, liquid and gaseous helium storage, liquid nitrogen storage, and transfer lines. Figure 1 presents a block diagram of the CHL refrigerator. The system was designed to provide 4.8 kW of primary refrigeration at 2K, 12 kW of shield refrigeration at 45K for the linac cryomodules, and 10 g/s of liquid flow for the end stations. In April 1994, stable 2K operation of the previously uncommissioned cold compressors was achieved. The cold compressors are a cold vacuum pump with an inlet temperature of circa 3.0K. These compressors operate on magnetic bearing,s and therefore eliminate the possibility of contamination due to any air leaks into the system. Operational data and commissioning experience as they relate to the warm gaseous helium compressors, turbines, instrumentation and control, and the cold compressors are presented

  13. The Q Motif Is Involved in DNA Binding but Not ATP Binding in ChlR1 Helicase.

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    Hao Ding

    Full Text Available Helicases are molecular motors that couple the energy of ATP hydrolysis to the unwinding of structured DNA or RNA and chromatin remodeling. The conversion of energy derived from ATP hydrolysis into unwinding and remodeling is coordinated by seven sequence motifs (I, Ia, II, III, IV, V, and VI. The Q motif, consisting of nine amino acids (GFXXPXPIQ with an invariant glutamine (Q residue, has been identified in some, but not all helicases. Compared to the seven well-recognized conserved helicase motifs, the role of the Q motif is less acknowledged. Mutations in the human ChlR1 (DDX11 gene are associated with a unique genetic disorder known as Warsaw Breakage Syndrome, which is characterized by cellular defects in genome maintenance. To examine the roles of the Q motif in ChlR1 helicase, we performed site directed mutagenesis of glutamine to alanine at residue 23 in the Q motif of ChlR1. ChlR1 recombinant protein was overexpressed and purified from HEK293T cells. ChlR1-Q23A mutant abolished the helicase activity of ChlR1 and displayed reduced DNA binding ability. The mutant showed impaired ATPase activity but normal ATP binding. A thermal shift assay revealed that ChlR1-Q23A has a melting point value similar to ChlR1-WT. Partial proteolysis mapping demonstrated that ChlR1-WT and Q23A have a similar globular structure, although some subtle conformational differences in these two proteins are evident. Finally, we found ChlR1 exists and functions as a monomer in solution, which is different from FANCJ, in which the Q motif is involved in protein dimerization. Taken together, our results suggest that the Q motif is involved in DNA binding but not ATP binding in ChlR1 helicase.

  14. Chl1 DNA helicase regulates Scc2 deposition specifically during DNA-replication in Saccharomyces cerevisiae.

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    Soumya Rudra

    Full Text Available The conserved family of cohesin proteins that mediate sister chromatid cohesion requires Scc2, Scc4 for chromatin-association and Eco1/Ctf7 for conversion to a tethering competent state. A popular model, based on the notion that cohesins form huge ring-like structures, is that Scc2, Scc4 function is essential only during G1 such that sister chromatid cohesion results simply from DNA replisome passage through pre-loaded cohesin rings. In such a scenario, cohesin deposition during G1 is temporally uncoupled from Eco1-dependent establishment reactions that occur during S-phase. Chl1 DNA helicase (homolog of human ChlR1/DDX11 and BACH1/BRIP1/FANCJ helicases implicated in Fanconi anemia, breast and ovarian cancer and Warsaw Breakage Syndrome plays a critical role in sister chromatid cohesion, however, the mechanism through which Chl1 promotes cohesion remains poorly understood. Here, we report that Chl1 promotes Scc2 loading unto DNA such that both Scc2 and cohesin enrichment to chromatin are defective in chl1 mutant cells. The results further show that both Chl1 expression and chromatin-recruitment are tightly regulated through the cell cycle, peaking during S-phase. Importantly, kinetic ChIP studies reveals that Chl1 is required for Scc2 chromatin-association specifically during S-phase, but not during G1. Despite normal chromatin enrichment of both Scc2 and cohesin during G1, chl1 mutant cells exhibit severe chromosome segregation and cohesion defects--revealing that G1-loaded cohesins is insufficient to promote cohesion. Based on these findings, we propose a new model wherein S-phase cohesin loading occurs during DNA replication and in concert with both cohesion establishment and chromatin assembly reactions--challenging the notion that DNA replication fork navigates through or around pre-loaded cohesin rings.

  15. Global Lakes Sentinel Services: Evaluation of Chl-a Trends in Deep Clear Lakes

    Science.gov (United States)

    Cazzaniga, Ilaria; Giardino, Claudia; Bresciani, Mariano; Poser, Kathrin; Peters, Steef; Hommersom, Annelies; Schenk, Karin; Heege, Thomas; Philipson, Petra; Ruescas, Ana; Bottcher, Martin; Stelzer, Kerstin

    2016-08-01

    The aim of this study is the analysis of trend in the trophic level evolution in clear deep lakes which, being characterised by good quality state, are important socio- economic resources for their regions. The selected lakes are situated in Europe (Garda, Maggiore, Constance and Vättern), North America (Michigan) and Africa (Malawi and Tanganyika) and cover a range of eco- regions (continental, perialpine, boreal, rift valley) distributed globally.To evaluate trophic level tendency we mainly focused on chlorophyll-a concentrations (chl-a) which is a direct proxy of trophic status. The chl-a concentrations were obtained from 5216 cloud-free MERIS imagery from 2002 to 2012.The 'GLaSS RoIStats tool' available within the GLaSS project was used to extract chl-a in a number of region of interests (ROI) located in pelagic waters as well as some few other stations depending on lakes morphology. For producing the time-series trend, these extracted data were analysed with the Seasonal Kendall test.The results overall show almost stable conditions with a slight increase in concentration for lakes Maggiore, Constance, and the Green Bay of Lake Michigan; a slight decrease for lakes Garda and Tanganyika and absolutely stable conditions for lakes Vättern and Malawi.The results presented in this work show the great capability of MERIS to perform trend tests analysis on trophic status with focus on chl-a concentration. Being chl-a also a key parameter in water quality monitoring plans, this study also supports the managing practices implemented worldwide for using the water of the lakes.

  16. Improvement of the Uranium Sequestration Ability of a Chlamydomonas sp. (ChlSP Strain) Isolated From Extreme Uranium Mine Tailings Through Selection for Potential Bioremediation Application

    Science.gov (United States)

    Baselga-Cervera, Beatriz; Romero-López, Julia; García-Balboa, Camino; Costas, Eduardo; López-Rodas, Victoria

    2018-01-01

    The extraction and processing of uranium (U) have polluted large areas worldwide, rendering anthropogenic extreme environments inhospitable to most species. Noticeably, these sites are of great interest for taxonomical and applied bioprospection of extremotolerant species successfully adapted to U tailings contamination. As an example, in this work we have studied a microalgae species that inhabits extreme U tailings ponds at the Saelices mining site (Salamanca, Spain), characterized as acidic (pH between 3 and 4), radioactive (around 4 μSv h−1) and contaminated with metals, mainly U (from 25 to 48 mg L−1) and zinc (from 17 to 87 mg L−1). After isolation of the extremotolerant ChlSP strain, morphological characterization and internal transcribed spacer (ITS)-5.8S gene sequences placed it in the Chlamydomonadaceae, but BLAST analyses identity values, against the nucleotide datasets at the NCBI database, were very low (microalgae growth curve; ChlSG cells removed close to 4 mg L−1 of U in 24 days. These findings open up promising prospects for sustainable management of U tailings waters based on newly evolved extremotolerants and outline the potential of artificial selection in the improvement of desired features in microalgae by experimental adaptation and selection. PMID:29662476

  17. Identification of the chlE gene encoding oxygen-independent Mg-protoporphyrin IX monomethyl ester cyclase in cyanobacteria.

    Science.gov (United States)

    Yamanashi, Kaori; Minamizaki, Kei; Fujita, Yuichi

    2015-08-07

    The fifth ring (E-ring) of chlorophyll (Chl) a is produced by Mg-protoporphyrin IX monomethyl ester (MPE) cyclase. There are two evolutionarily unrelated MPE cyclases: oxygen-independent (BchE) and oxygen-dependent (ChlA/AcsF) MPE cyclases. Although ChlA is the sole MPE cyclase in Synechocystis PCC 6803, it is yet unclear whether BchE exists in cyanobacteria. A BLAST search suggests that only few cyanobacteria possess bchE. Here, we report that two bchE candidate genes from Cyanothece strains PCC 7425 and PCC 7822 restore the photosynthetic growth and bacteriochlorophyll production in a bchE-lacking mutant of Rhodobacter capsulatus. We termed these cyanobacterial bchE orthologs "chlE." Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Contrasting Chl-a responses to the tropical cyclones Thane and Phailin in the Bay of Bengal

    Digital Repository Service at National Institute of Oceanography (India)

    Vidya, P.J.; Das, S.; ManiMurali, R.

    cyclone (8-14 October 2013), and both occurred during the post-monsoon season. The present study examined the effect of cyclone intensity difference on the chlorophyll a (Chl-a) production in the BoB. Two and seven times Chl-a enhancement was observed...

  19. MicroRNA-Mediated Regulation of ITGB3 and CHL1 Is Implicated in SSRI Action

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    Keren Oved

    2017-11-01

    Full Text Available Background: Selective serotonin reuptake inhibitor (SSRI antidepressant drugs are the first-line of treatment for major depressive disorder (MDD but are effective in <70% of patients. Our earlier genome-wide studies indicated that two genes encoding for cell adhesion proteins, close homolog of L1 (CHL1 and integrin beta-3 (ITGB3, and microRNAs, miR-151a-3p and miR-221/222, are implicated in the variable sensitivity and response of human lymphoblastoid cell lines (LCL from unrelated individuals to SSRI drugs.Methods: The microRNAs miR-221, miR-222, and miR-151-a-3p, along with their target gene binding sites, were explored in silico using miRBase, TargetScan, microRNAviewer, and the UCSC Genome Browser. Luciferase reporter assays were conducted for demonstrating the direct functional regulation of ITGB3 and CHL1 expression by miR-221/222 and miR-151a-3p, respectively. A human LCL exhibiting low sensitivity to paroxetine was utilized for studying the phenotypic effect of CHL1 regulation by miR-151a-3p on SSRI response.Results: By showing direct regulation of CHL1 and ITGB3 by miR-151a-3p and miR-221/222, respectively, we link these microRNAs and genes with cellular SSRI sensitivity phenotypes. We report that miR-151a-3p increases cell sensitivity to paroxetine via down-regulating CHL1 expression.Conclusions: miR-151a-3p, miR-221/222 and their (here confirmed respective target-genes, CHL1 and ITGB3, are implicated in SSRI responsiveness, and possibly in the clinical response to antidepressant drugs.

  20. Connecting active to passive fluorescence with photosynthesis: a method for evaluating remote sensing measurements of Chl fluorescence.

    Science.gov (United States)

    Magney, Troy S; Frankenberg, Christian; Fisher, Joshua B; Sun, Ying; North, Gretchen B; Davis, Thomas S; Kornfeld, Ari; Siebke, Katharina

    2017-09-01

    Recent advances in the retrieval of Chl fluorescence from space using passive methods (solar-induced Chl fluorescence, SIF) promise improved mapping of plant photosynthesis globally. However, unresolved issues related to the spatial, spectral, and temporal dynamics of vegetation fluorescence complicate our ability to interpret SIF measurements. We developed an instrument to measure leaf-level gas exchange simultaneously with pulse-amplitude modulation (PAM) and spectrally resolved fluorescence over the same field of view - allowing us to investigate the relationships between active and passive fluorescence with photosynthesis. Strongly correlated, slope-dependent relationships were observed between measured spectra across all wavelengths (F λ , 670-850 nm) and PAM fluorescence parameters under a range of actinic light intensities (steady-state fluorescence yields, F t ) and saturation pulses (maximal fluorescence yields, F m ). Our results suggest that this method can accurately reproduce the full Chl emission spectra - capturing the spectral dynamics associated with changes in the yields of fluorescence, photochemical (ΦPSII), and nonphotochemical quenching (NPQ). We discuss how this method may establish a link between photosynthetic capacity and the mechanistic drivers of wavelength-specific fluorescence emission during changes in environmental conditions (light, temperature, humidity). Our emphasis is on future research directions linking spectral fluorescence to photosynthesis, ΦPSII, and NPQ. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  1. Wideband aural acoustic absorbance predicts conductive hearing loss in children.

    Science.gov (United States)

    Keefe, Douglas H; Sanford, Chris A; Ellison, John C; Fitzpatrick, Denis F; Gorga, Michael P

    2012-12-01

    This study tested the hypothesis that wideband aural absorbance predicts conductive hearing loss (CHL) in children medically classified as having otitis media with effusion. Absorbance was measured in the ear canal over frequencies from 0.25 to 8 kHz at ambient pressure or as a swept tympanogram. CHL was defined using criterion air-bone gaps of 20, 25, and 30 dB at octaves from 0.25 to 4 kHz. A likelihood-ratio predictor of CHL was constructed across frequency for ambient absorbance, and across frequency and pressure for absorbance tympanometry. Performance was evaluated at individual frequencies and for any frequency at which a CHL was present. Absorbance and conventional 0.226-kHz tympanograms were measured in children of age three to eight years with CHL and with normal hearing. Absorbance was smaller at frequencies above 0.7 kHz in the CHL group than the control group. Based on the area under the receiver operating characteristic curve, wideband absorbance in ambient and tympanometric tests were significantly better predictors of CHL than tympanometric width, the best 0.226-kHz predictor. Accuracies of ambient and tympanometric wideband absorbance did not differ. Absorbance accurately predicted CHL in children and was more accurate than conventional 0.226-kHz tympanometry.

  2. Photosystem Trap Energies and Spectrally-Dependent Energy-Storage Efficiencies in the Chl d-Utilizing Cyanobacterium, Acaryochloris Marina

    Science.gov (United States)

    Mielke, Steven P.; Kiang, Nancy Y.; Blankenship, Robert E.; Mauzerall, David

    2012-01-01

    Acaryochloris marina is the only species known to utilize chlorophyll (Chl) d as a principal photopigment. The peak absorption wavelength of Chl d is redshifted approx. 40 nm in vivo relative to Chl a, enabling this cyanobacterium to perform oxygenic phototrophy in niche environments enhanced in far-red light. We present measurements of the in vivo energy-storage (E-S) efficiency of photosynthesis in A. marina, obtained using pulsed photoacoustics (PA) over a 90-nm range of excitation wavelengths in the red and far-red. Together with modeling results, these measurements provide the first direct observation of the trap energies of PSI and PSII, and also the photosystem-specific contributions to the total E-S efficiency. We find the maximum observed efficiency in A. marina (40+/-1% at 735 nm) is higher than in the Chl a cyanobacterium Synechococcus leopoliensis (35+/-1% at 690 nm). The efficiency at peak absorption wavelength is also higher in A. marina (36+/-1% at 710 nm vs. 31+/-1% at 670 nm). In both species, the trap efficiencies are approx. 40% (PSI) and approx. 30% (PSII). The PSI trap in A. marina is found to lie at 740+/-5 nm, in agreement with the value inferred from spectroscopic methods. The best fit of the model to the PA data identifies the PSII trap at 723+/-3 nm, supporting the view that the primary electron-donor is Chl d, probably at the accessory (ChlD1) site. A decrease in efficiency beyond the trap wavelength, consistent with uphill energy transfer, is clearly observed and fit by the model. These results demonstrate that the E-S efficiency in A. marina is not thermodynamically limited, suggesting that oxygenic photosynthesis is viable in even redder light environments.

  3. Microbial community dynamics during the bioremediation process of chlorimuron-ethyl-contaminated soil by Hansschlegelia sp. strain CHL1.

    Directory of Open Access Journals (Sweden)

    Liqiang Yang

    Full Text Available Long-term and excessive application of chlorimuron-ethyl has led to a series of environmental problems. Strain Hansschlegelia sp. CHL1, a highly efficient chlorimuron-ethyl degrading bacterium isolated in our previous study, was employed in the current soil bioremediation study. The residues of chlorimuron-ethyl in soils were detected, and the changes of soil microbial communities were investigated by phospholipid fatty acid (PLFA analysis. The results showed that strain CHL1 exhibited significant chlorimuron-ethyl degradation ability at wide range of concentrations between 10μg kg-1 and 1000μg kg-1. High concentrations of chlorimuron-ethyl significantly decreased the total concentration of PLFAs and the Shannon-Wiener indices and increased the stress level of microbes in soils. The inoculation with strain CHL1, however, reduced the inhibition on soil microbes caused by chlorimuron-ethyl. The results demonstrated that strain CHL1 is effective in the remediation of chlorimuron-ethyl-contaminated soil, and has the potential to remediate chlorimuron-ethyl contaminated soils in situ.

  4. Microbial Community Dynamics during the Bioremediation Process of Chlorimuron-Ethyl-Contaminated Soil by Hansschlegelia sp. Strain CHL1

    Science.gov (United States)

    Yang, Liqiang; Li, Xinyu; Li, Xu; Su, Zhencheng; Zhang, Chenggang; Zhang, Huiwen

    2015-01-01

    Long-term and excessive application of chlorimuron-ethyl has led to a series of environmental problems. Strain Hansschlegelia sp. CHL1, a highly efficient chlorimuron-ethyl degrading bacterium isolated in our previous study, was employed in the current soil bioremediation study. The residues of chlorimuron-ethyl in soils were detected, and the changes of soil microbial communities were investigated by phospholipid fatty acid (PLFA) analysis. The results showed that strain CHL1 exhibited significant chlorimuron-ethyl degradation ability at wide range of concentrations between 10μg kg-1 and 1000μg kg-1. High concentrations of chlorimuron-ethyl significantly decreased the total concentration of PLFAs and the Shannon-Wiener indices and increased the stress level of microbes in soils. The inoculation with strain CHL1, however, reduced the inhibition on soil microbes caused by chlorimuron-ethyl. The results demonstrated that strain CHL1 is effective in the remediation of chlorimuron-ethyl-contaminated soil, and has the potential to remediate chlorimuron-ethyl contaminated soils in situ. PMID:25689050

  5. Too much food may cause reduced growth of blue mussels (Mytilus edulis) - Test of hypothesis and new 'high Chl a BEG-model'

    Science.gov (United States)

    Larsen, Poul S.; Lüskow, Florian; Riisgård, Hans Ulrik

    2018-04-01

    Growth of the blue mussel (Mytilus edulis) is closely related to the biomass of phytoplankton (expressed as concentration of chlorophyll a, Chl a), but the effect of too much food in eutrophicated areas has so far been overlooked. The hypothesis addressed in the present study suggests that high Chl a concentrations (> about 8 μg Chl a l-1) result in reduced growth because mussels are not evolutionarily adapted to utilize such high phytoplankton concentrations and to physiologically regulate the amount of ingested food in such a way that the growth rate remains high and constant. We first make a comparison of literature values for actually measured weight-specific growth rates (μ, % d-1) of small (20 to 25 mm) M. edulis, either grown in controlled laboratory experiments or in net bags in Danish waters, as a function of Chl a. A linear increase up to about μ = 8.3% d-1 at 8.1 μg Chl a l-1 fits the "standard BEG-model" after which a marked decrease takes place, and this supports the hypothesis. A "high Chl a BEG-model", applicable to newly settled post-metamorphic and small juvenile (non-spawning) mussels in eutrophicated Danish and other temperate waters, is developed and tested, and new data from a case study in which the growth of mussels in net bags was measured along a Chl a gradient are presented. Finally, we discuss the phenomenon of reduced growth of mussels in eutrophicated areas versus a possible impact of low salinity. It is concluded that it is difficult to separate the effect of salinity from the effect of Chl a, but the present study shows that too much food may cause reduced growth of mussels in eutrophicated marine areas regardless of high or moderate salinity above about 10 psu.

  6. CHARACTERIZATION OF PB2+ UPTAKE AND SEQUESTRATION IN PSEUDOMONAS AERUGINOSA CHL004

    Science.gov (United States)

    In laboratory studies, the soil isolate Pseudomonas aeruginosa CHL004 (Vesper et al 1996) has been found to concentrated Pb2+ in the cytoplasm by formation of particles that contain Pb2+ and phosphorus. Upon examination of the washed lyophilized cells grown in the presence of lea...

  7. Effect of 0.4 mT power frequency magnetic field on F-actin assembly of CHL cells

    International Nuclear Information System (INIS)

    Chu Keping; Cai Zhiyin; Zhang Yukun; Xia Nuohong

    2007-01-01

    Objective: To investigate the effect of 0.4mT power frequency magnetic field on the microfilament (F- actin) assembly of Chinese hamster lung (CHL) cells. Methods: F-actin were marked with immunohistochemical method, then observed under a confocal microscope. The content of ECFRs in the preparation of the detergent-insoluble cytoskeleton was measured with Western-blotting. Results: The stress fiber's of CHL cells decreased after exposure to 0.4mT power frequency magnetic field for 30min, as well as after treatment with epidermal growth factor (ECF) of 50nM. Filopodias appeared at the periphery after exposure to magnetic field as well as treatment with EGF. The EGF receptor mass associated with the detergent-insoluble cytoskeleton increased after exposure to magnetic field as well as treatment with EGF. Conclusion: 0.4mT power frequency magnetic field induced assembly of F-actin in CHL cells. The change induced by magnetic field would be related to clustering of EGFR induced by magnetic field and passing the signal down. (authors)

  8. Disruption of the ndhF1 gene affects Chl fluorescence through state transition in the Cyanobacterium Synechocystis sp. PCC 6803, resulting in apparent high efficiency of photosynthesis.

    Science.gov (United States)

    Ogawa, Takako; Harada, Tetsuyuki; Ozaki, Hiroshi; Sonoike, Kintake

    2013-07-01

    In Synechocystis sp. PCC 6803, the disruption of the ndhF1 gene (slr0844), which encodes a subunit of one of the NDH-1 complexes (NDH-1L complex) serving for respiratory electron transfer, causes the largest change in Chl fluorescence induction kinetics among the kinetics of 750 disruptants searched in the Fluorome, the cyanobacterial Chl fluorescence database. The cause of the explicit phenotype of the ndhF1 disruptant was examined by measurements of the photosynthetic rate, Chl fluorescence and state transition. The results demonstrate that the defects in respiratory electron transfer obviously have great impact on Chl fluorescence in cyanobacteria. The inactivation of NDH-1L complexes involving electron transfer from NDH-1 to plastoquinone (PQ) would result in the oxidation of the PQ pool, leading to the transition to State 1, where the yield of Chl fluorescence is high. Apparently, respiration, although its rate is far lower than that of photosynthesis, could affect Chl fluorescence through the state transition as leverage. The disruption of the ndhF1 gene caused lower oxygen-evolving activity but the estimated electron transport rate from Chl fluorescence measurements was faster in the mutant than in the wild-type cells. The discrepancy could be ascribed to the decreased level of non-photochemical quenching due to state transition. One must be cautious when using the Chl fluorescence parameter to estimate photosynthesis in mutants defective in state transition.

  9. Somatic growth of mussels Mytilus edulis in field studies compared to predictions using BEG, DEB, and SFG models

    Science.gov (United States)

    Larsen, Poul S.; Filgueira, Ramón; Riisgård, Hans Ulrik

    2014-04-01

    Prediction of somatic growth of blue mussels, Mytilus edulis, based on the data from 2 field-growth studies of mussels in suspended net-bags in Danish waters was made by 3 models: the bioenergetic growth (BEG), the dynamic energy budget (DEB), and the scope for growth (SFG). Here, the standard BEG model has been expanded to include the temperature dependence of filtration rate and respiration and an ad hoc modification to ensure a smooth transition to zero ingestion as chlorophyll a (chl a) concentration approaches zero, both guided by published data. The first 21-day field study was conducted at nearly constant environmental conditions with a mean chl a concentration of C = 2.7 μg L- 1, and the observed monotonous growth in the dry weight of soft parts was best predicted by DEB while BEG and SFG models produced lower growth. The second 165-day field study was affected by large variations in chl a and temperature, and the observed growth varied accordingly, but nevertheless, DEB and SFG predicted monotonous growth in good agreement with the mean pattern while BEG mimicked the field data in response to observed changes in chl a concentration and temperature. The general features of the models were that DEB produced the best average predictions, SFG mostly underestimated growth, whereas only BEG was sensitive to variations in chl a concentration and temperature. DEB and SFG models rely on the calibration of the half-saturation coefficient to optimize the food ingestion function term to that of observed growth, and BEG is independent of observed actual growth as its predictions solely rely on the time history of the local chl a concentration and temperature.

  10. Spatial-temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components.

    Science.gov (United States)

    Hou, Xiyong; Li, Mingjie; Gao, Meng; Yu, Liangju; Bi, Xiaoli

    2013-01-01

    Annual normalized difference vegetation index (NDVI) and chlorophyll-a (Chl-a) concentration are the most important large-scale indicators of terrestrial and oceanic ecosystem net primary productivity. In this paper, the Sea-viewing Wide Field-of-view Sensor level 3 standard mapped image annual products from 1998 to 2009 are used to study the spatial-temporal characters of terrestrial NDVI and oceanic Chl-a concentration on two sides of the coastline of China by using the methods of mean value (M), coefficient of variation (CV), the slope of unary linear regression model (Slope), and the Hurst index (H). In detail, we researched and analyzed the spatial-temporal dynamics, the longitudinal zonality and latitudinal zonality, the direction, intensity, and persistency of historical changes. The results showed that: (1) spatial patterns of M and CV between NDVI and Chl-a concentration from 1998 to 2009 were very different. The dynamic variation of terrestrial NDVI was much mild, while the variation of oceanic Chl-a concentration was relatively much larger; (2) distinct longitudinal zonality was found for Chl-a concentration and NDVI due to their hypersensitivity to the distance to shoreline, and strong latitudinal zonality existed for Chl-a concentration while terrestrial NDVI had a very weak latitudinal zonality; (3) overall, the NDVI showed a slight decreasing trend while the Chl-a concentration showed a significant increasing trend in the past 12 years, and both of them exhibit strong self-similarity and long-range dependence which indicates opposite future trends between land and ocean.

  11. Too much food may cause reduced growth of blue mussels (Mytilus edulis) – Test of hypothesis and new ‘high Chl a BEG-model’

    DEFF Research Database (Denmark)

    Larsen, Poul S.; Lüskow, Florian; Riisgård, Hans Ulrik

    2018-01-01

    Growth of the blue mussel (Mytilus edulis) is closely related to the biomass of phytoplankton (expressed as concentration of chlorophyll a, Chl a), but the effect of too much food in eutrophicated areas has so far been overlooked. The hypothesis addressed in the present study suggests that high Chl...... a concentrations (> about 8 μg Chl a l−1) result in reduced growth because mussels are not evolutionarily adapted to utilize such high phytoplankton concentrations and to physiologically regulate the amount of ingested food in such a way that the growth rate remains high and constant. We first make a comparison...... the effect of Chl a, but the present study shows that too much food may cause reduced growth of mussels in eutrophicated marine areas regardless of high or moderate salinity above about 10 psu....

  12. Bergmann glia and the recognition molecule CHL1 organize GABAergic axons and direct innervation of Purkinje cell dendrites.

    Directory of Open Access Journals (Sweden)

    Fabrice Ango

    2008-04-01

    Full Text Available The geometric and subcellular organization of axon arbors distributes and regulates electrical signaling in neurons and networks, but the underlying mechanisms have remained elusive. In rodent cerebellar cortex, stellate interneurons elaborate characteristic axon arbors that selectively innervate Purkinje cell dendrites and likely regulate dendritic integration. We used GFP BAC transgenic reporter mice to examine the cellular processes and molecular mechanisms underlying the development of stellate cell axons and their innervation pattern. We show that stellate axons are organized and guided towards Purkinje cell dendrites by an intermediate scaffold of Bergmann glial (BG fibers. The L1 family immunoglobulin protein Close Homologue of L1 (CHL1 is localized to apical BG fibers and stellate cells during the development of stellate axon arbors. In the absence of CHL1, stellate axons deviate from BG fibers and show aberrant branching and orientation. Furthermore, synapse formation between aberrant stellate axons and Purkinje dendrites is reduced and cannot be maintained, leading to progressive atrophy of axon terminals. These results establish BG fibers as a guiding scaffold and CHL1 a molecular signal in the organization of stellate axon arbors and in directing their dendritic innervation.

  13. Remote detection of water stress conditions via a diurnal photochemical reflectance index (PRI) improves yield prediction in rainfed wheat

    Science.gov (United States)

    Magney, T. S.; Vierling, L. A.; Eitel, J.

    2014-12-01

    Employing remotely sensed techniques to quantify the existence and magnitude of midday photosynthetic downregulation using the photochemical reflectance index (PRI) may reveal new information about plant responses to abiotic stressors in space and time. However, the interpretation and application of the PRI can be confounded because of its sensitivity to several variables changing at the diurnal (e.g., irradiation, shadow fraction) and seasonal (e.g., leaf area, chlorophyll and carotene pigment concentrations, irradiation) time scales. We explored different techniques to correct the PRI for variations in canopy structure and relative chlorophyll content (ChlR) using highly temporally resolved (frequency = five minutes) in-situ radiometric measurements of PRI and the Normalized Difference Vegetation Index (NDVI) over eight soft white spring wheat (Triticum aestivum L.)field plots under varying nitrogen and soil water conditions over two seasons. Our results suggest that the influence of seasonal variation in canopy ChlR and LAI on the diurnally measured PRI (PRIdiurnal) can be minimized using simple correction techniques, therefore improving the strength of PRI as a tool to quantify abiotic stressors such as daily changes in soil volumetric water content (SVWC), and vapor pressure deficit (VPD). PRIdiurnal responded strongly to available nitrogen, and linearly tracked seasonal changes in SVWC, VPD, and stomatal conductance (gc). Utilizing the PRI as an indicator of stress, yield predictions significantly over greenness indices such as the NDVI. This study provides insight towards the future interpretation and scaling of PRI to quantify rapid changes in photosynthesis, and as an indicator of plant stress.

  14. Improvement of the Uranium Sequestration Ability of a Chlamydomonas sp. (ChlSP Strain) Isolated From Extreme Uranium Mine Tailings Through Selection for Potential Bioremediation Application.

    Science.gov (United States)

    Baselga-Cervera, Beatriz; Romero-López, Julia; García-Balboa, Camino; Costas, Eduardo; López-Rodas, Victoria

    2018-01-01

    The extraction and processing of uranium (U) have polluted large areas worldwide, rendering anthropogenic extreme environments inhospitable to most species. Noticeably, these sites are of great interest for taxonomical and applied bioprospection of extremotolerant species successfully adapted to U tailings contamination. As an example, in this work we have studied a microalgae species that inhabits extreme U tailings ponds at the Saelices mining site (Salamanca, Spain), characterized as acidic (pH between 3 and 4), radioactive (around 4 μSv h -1 ) and contaminated with metals, mainly U (from 25 to 48 mg L -1 ) and zinc (from 17 to 87 mg L -1 ). After isolation of the extremotolerant ChlSP strain, morphological characterization and internal transcribed spacer (ITS)-5.8S gene sequences placed it in the Chlamydomonadaceae , but BLAST analyses identity values, against the nucleotide datasets at the NCBI database, were very low (tailings waters based on newly evolved extremotolerants and outline the potential of artificial selection in the improvement of desired features in microalgae by experimental adaptation and selection.

  15. Chronic mild stress impairs latent inhibition and induces region-specific neural activation in CHL1-deficient mice, a mouse model of schizophrenia.

    Science.gov (United States)

    Buhusi, Mona; Obray, Daniel; Guercio, Bret; Bartlett, Mitchell J; Buhusi, Catalin V

    2017-08-30

    Schizophrenia is a neurodevelopmental disorder characterized by abnormal processing of information and attentional deficits. Schizophrenia has a high genetic component but is precipitated by environmental factors, as proposed by the 'two-hit' theory of schizophrenia. Here we compared latent inhibition as a measure of learning and attention, in CHL1-deficient mice, an animal model of schizophrenia, and their wild-type littermates, under no-stress and chronic mild stress conditions. All unstressed mice as well as the stressed wild-type mice showed latent inhibition. In contrast, CHL1-deficient mice did not show latent inhibition after exposure to chronic stress. Differences in neuronal activation (c-Fos-positive cell counts) were noted in brain regions associated with latent inhibition: Neuronal activation in the prelimbic/infralimbic cortices and the nucleus accumbens shell was affected solely by stress. Neuronal activation in basolateral amygdala and ventral hippocampus was affected independently by stress and genotype. Most importantly, neural activation in nucleus accumbens core was affected by the interaction between stress and genotype. These results provide strong support for a 'two-hit' (genes x environment) effect on latent inhibition in CHL1-deficient mice, and identify CHL1-deficient mice as a model of schizophrenia-like learning and attention impairments. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Kinetics of gas to particle conversion in the NH/sub 3/-Chl system

    Energy Technology Data Exchange (ETDEWEB)

    Luria, M; Cohen, B

    1980-01-01

    Particle formation in the reaction of NH/sub 3/ and Chl under 1 atm of N/sub 2/ and at 25/sup 0/C was studied in a flow reactor. The critical concentration below which NO particle can be formed was found to be 3.5 x 10/sup +14/ molecule/CM/sup 3/ for (NH/sub 3/)=(HCl). Above this concentration, gas-particle conversion percentage increases rapidly to approach 100%.

  17. GUN4-Porphyrin Complexes Bind the ChlH/GUN5 Subunit of Mg-Chelatase and Promote Chlorophyll Biosynthesis in Arabidopsis[W

    Science.gov (United States)

    Adhikari, Neil D.; Froehlich, John E.; Strand, Deserah D.; Buck, Stephanie M.; Kramer, David M.; Larkin, Robert M.

    2011-01-01

    The GENOMES UNCOUPLED4 (GUN4) protein stimulates chlorophyll biosynthesis by activating Mg-chelatase, the enzyme that commits protoporphyrin IX to chlorophyll biosynthesis. This stimulation depends on GUN4 binding the ChlH subunit of Mg-chelatase and the porphyrin substrate and product of Mg-chelatase. After binding porphyrins, GUN4 associates more stably with chloroplast membranes and was proposed to promote interactions between ChlH and chloroplast membranes—the site of Mg-chelatase activity. GUN4 was also proposed to attenuate the production of reactive oxygen species (ROS) by binding and shielding light-exposed porphyrins from collisions with O2. To test these proposals, we first engineered Arabidopsis thaliana plants that express only porphyrin binding–deficient forms of GUN4. Using these transgenic plants and particular mutants, we found that the porphyrin binding activity of GUN4 and Mg-chelatase contribute to the accumulation of chlorophyll, GUN4, and Mg-chelatase subunits. Also, we found that the porphyrin binding activity of GUN4 and Mg-chelatase affect the associations of GUN4 and ChlH with chloroplast membranes and have various effects on the expression of ROS-inducible genes. Based on our findings, we conclude that ChlH and GUN4 use distinct mechanisms to associate with chloroplast membranes and that mutant alleles of GUN4 and Mg-chelatase genes cause sensitivity to intense light by a mechanism that is potentially complex. PMID:21467578

  18. Monitoring and predicting eutrophication of Sri Lankan inland waters using ASTER satellite data

    Science.gov (United States)

    Dahanayaka, D. D. G. L.; Wijeyaratne, M. J. S.; Tonooka, H.; Minato, A.; Ozawa, S.; Perera, B. D. C.

    2014-10-01

    This study focused on determining the past changes and predicting the future trends in eutrophication of the Bolgoda North lake, Sri Lanka using in situ Chlorophyll-a (Chl-a) measurements and Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) satellite data. This Lake is located in a mixed land use area with industries, some agricultural lands, middle income and high income housing, tourist hotels and low income housing. From March to October 2013, water samples from five sampling sites were collected once a month parallel to ASTER overpass and Chl-a, nitrate and phosphate contents of each sample were measured using standard laboratory methods. Cloud-free ASTER scenes over the lake during the 2000-2013 periods were acquired for Chl-a estimation and trend analysis. All ASTER images were atmospherically corrected using FLAASH software and in-situ Chl-a data were regressed with atmospherically corrected three ASTER VNIR band ratios of the same date. The regression equation of the band ratio and Chl-a content with the highest correlation, which was the green/red band ratio was used to develop algorithm for generation of 15-m resolution Chl-a distribution maps. According to the ASTER based Chl-a distribution maps it was evident that eutrophication of this lake has gradually increased from 2008-2011. Results also indicated that there had been significantly high eutrophic conditions throughout the year 2013 in several regions, especially in water stagnant areas and adjacent to freshwater outlets. Field observations showed that this lake is receiving various discharges from factories. Unplanned urbanization and inadequacy of proper facilities in the nearby industries for waste management have resulted in the eutrophication of the water body. If the present trends of waste disposal and unplanned urbanization continue, enormous environmental problems would be resulted in future. Results of the present study showed that information from satellite remote

  19. Somatic growth of mussels Mytilus edulis in field studies compared to predictions using BEG, DEB, and SFG models

    DEFF Research Database (Denmark)

    Larsen, Poul Scheel; Filgueira, Ramón; Riisgård, Hans Ulrik

    2014-01-01

    Prediction of somatic growth of blue mussels, Mytilus edulis, based on the data from 2 field-growth studies of mussels in suspended net-bags in Danish waters was made by 3 models: the bioenergetic growth (BEG), the dynamic energy budget (DEB), and the scope for growth (SFG). Here, the standard BEG...... at nearly constant environmental conditions with a mean chl a concentration of C=2.7μgL−1, and the observed monotonous growth in the dry weight of soft parts was best predicted by DEB while BEG and SFG models produced lower growth. The second 165-day field study was affected by large variations in chl...... a and temperature, and the observed growth varied accordingly, but nevertheless, DEB and SFG predicted monotonous growth in good agreement with the mean pattern while BEG mimicked the field data in response to observed changes in chl a concentration and temperature. The general features of the models were that DEB...

  20. Mutation of Gly195 of the ChlH subunit of Mg-chelatase reduces chlorophyll and further disrupts PS II assembly in a Ycf48-deficient strain of Synechocystis sp. PCC 6803

    Directory of Open Access Journals (Sweden)

    Tim Crawford

    2016-07-01

    Full Text Available Biogenesis of the photosystems in oxygenic phototrophs requires co-translational insertion of chlorophyll a. The first committed step of chlorophyll a biosynthesis is the insertion of a Mg2+ ion into the tetrapyrrole intermediate protoporphyrin IX, catalyzed by Mg-chelatase. We have identified a Synechocystis sp. PCC 6803 strain with a spontaneous mutation in chlH that results in a Gly195 to Glu substitution in a conserved region of the catalytic subunit of Mg-chelatase. Mutant strains containing the ChlH Gly195 to Glu mutation were generated using a two-step protocol that introduced the chlH gene into a putative neutral site in the chromosome prior to deletion of the native gene. The Gly195 to Glu mutation resulted in strains with decreased chlorophyll a. Deletion of the PS II assembly factor Ycf48 in a strain carrying the ChlH Gly195 to Glu mutation did not grow photoautotrophically. In addition, the ChlH-G195E:ΔYcf48 strain showed impaired PS II activity and decreased assembly of PS II centers in comparison to a ΔYcf48 strain. We suggest decreased chlorophyll in the ChlH-G195E mutant provides a background to screen for the role of assembly factors that are not essential under optimal growth conditions.

  1. Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production

    Science.gov (United States)

    Zhang, Qingyuan; Middleton, Elizabeth M.; Cheng, Yen-Ben; Huemmrich, K. Fred; Cook, Bruce D.; Corp, Lawrence A.; Kustas, William P.; Russ, Andrew L.; Prueger, John H.; Yao, Tian

    2016-01-01

    The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to e. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol-(sup -1) PPFD and coefficient of variation (CV) of 16

  2. Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP

    KAUST Repository

    Houborg, Rasmus; Cescatti, Alessandro; Migliavacca, Mirco; Kustas, W.P.

    2013-01-01

    This study investigates the utility of in situ and satellite-based leaf chlorophyll (Chl) estimates for quantifying leaf photosynthetic capacity and for constraining model simulations of Gross Primary Productivity (GPP) over a corn field in Maryland, U.S.A. The maximum rate of carboxylation (V-max) represents a key control on leaf photosynthesis within the widely employed C-3 and C-4 photosynthesis models proposed by Farquhar et al. (1980) and Collatz et al. (1992), respectively. A semi-mechanistic relationship between V-max(5) (V-max normalized to 25 degrees C) and Chl is derived based on interlinkages between V-max(25), Rubisco enzyme kinetics, leaf nitrogen, and Chl reported in the experimental literature. The resulting linear V-max(25) - Chl relationship is embedded within the photosynthesis scheme of the Community Land Model (CLM), thereby bypassing the use of fixed plant functional type (PFT) specific V-max(25) values. The effect of the updated parameterization on simulated carbon fluxes is tested over a corn field growing season using: (1) a detailed Chl time-series established on the basis of intensive field measurements and (2) Chl estimates derived from Landsat imagery using the REGularized canopy reFLECtance (REGFLEC) tool. Validations against flux tower observations demonstrate benefit of using Chl to parameterize V-max(25) to account for variations in nitrogen availability imposed by severe environmental conditions. The use of V-max(25) that varied seasonally as a function of satellite-based Chl, rather than a fixed PFT-specific value, significantly improved the agreement with observed tower fluxes with Pearson's correlation coefficient (r) increasing from 0.88 to 0.93 and the root-mean-square-deviation decreasing from 4.77 to 3.48 mu mol m(-2) s(-1). The results support the use of Chl as a proxy for photosynthetic capacity using generalized relationships between V-max(25) and Chl, and advocate the potential of satellite retrieved Chl for constraining

  3. Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP

    KAUST Repository

    Houborg, Rasmus

    2013-08-01

    This study investigates the utility of in situ and satellite-based leaf chlorophyll (Chl) estimates for quantifying leaf photosynthetic capacity and for constraining model simulations of Gross Primary Productivity (GPP) over a corn field in Maryland, U.S.A. The maximum rate of carboxylation (V-max) represents a key control on leaf photosynthesis within the widely employed C-3 and C-4 photosynthesis models proposed by Farquhar et al. (1980) and Collatz et al. (1992), respectively. A semi-mechanistic relationship between V-max(5) (V-max normalized to 25 degrees C) and Chl is derived based on interlinkages between V-max(25), Rubisco enzyme kinetics, leaf nitrogen, and Chl reported in the experimental literature. The resulting linear V-max(25) - Chl relationship is embedded within the photosynthesis scheme of the Community Land Model (CLM), thereby bypassing the use of fixed plant functional type (PFT) specific V-max(25) values. The effect of the updated parameterization on simulated carbon fluxes is tested over a corn field growing season using: (1) a detailed Chl time-series established on the basis of intensive field measurements and (2) Chl estimates derived from Landsat imagery using the REGularized canopy reFLECtance (REGFLEC) tool. Validations against flux tower observations demonstrate benefit of using Chl to parameterize V-max(25) to account for variations in nitrogen availability imposed by severe environmental conditions. The use of V-max(25) that varied seasonally as a function of satellite-based Chl, rather than a fixed PFT-specific value, significantly improved the agreement with observed tower fluxes with Pearson\\'s correlation coefficient (r) increasing from 0.88 to 0.93 and the root-mean-square-deviation decreasing from 4.77 to 3.48 mu mol m(-2) s(-1). The results support the use of Chl as a proxy for photosynthetic capacity using generalized relationships between V-max(25) and Chl, and advocate the potential of satellite retrieved Chl for

  4. Modeled long-term changes of DIN:DIP ratio in the Changjiang River in relation to Chl-α and DO concentrations in adjacent estuary

    Science.gov (United States)

    Wang, Jianing; Yan, Weijin; Chen, Nengwang; Li, Xinyan; Liu, Lusan

    2015-12-01

    In the past four decades (1970-2013), nitrogen and phosphorous inputs to the Changjiang River basin, mainly from human activities, have increased 3-fold and 306-fold, respectively. The riverine nutrient fluxes to the estuary have also grown exponentially. Dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorous (DIP) fluxes of the Changjiang River increased by 338% and 574% during 1970-2013 period, and red tides and benthic hypoxia have been observed in the outflow region of the Changjiang River in the East China Sea (ECS). We assumed that time series changes in the DIN:DIP ratio from the Changjiang River could have a significant impact on Chlorophyll-α (Chl-α) concentration in the surface sea water and dissolved oxygen (DO) concentration in the bottom sea water of the Changjiang estuary. Our study showed that the DIN:DIP ratio from the Changjiang River increased from 76 to 384 between 1970 and 1985, and decreased from 255 to 149 between 1986 and 2013. The observed Chl-α concentration increased by 146% from 1992 to 2010 in the Changjiang estuary, and was negatively related to the DIN:DIP ratio in 1992-2010. Bottom sea water DO concentration decreased by 24.6% during 1992-2010 and a "low oxygen zone" (122°∼123°E, 32°∼33°N) was observed during summer since 1999. The anthropogenically enhanced nutrient inputs dominated river DIN and DIP fluxes and influenced Chl-α concentrations as well as bottom DO concentrations in the estuary. Scenarios emphasizing global collaboration and proactive environmental problem-solving may result in reductions in the river nutrient exports and in Chl-α and DO concentration in the Changjiang estuary by 2050.

  5. Audiovisual biofeedback improves motion prediction accuracy.

    Science.gov (United States)

    Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

    2013-04-01

    The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

  6. The Cellular DNA Helicase ChlR1 Regulates Chromatin and Nuclear Matrix Attachment of the Human Papillomavirus 16 E2 Protein and High-Copy-Number Viral Genome Establishment.

    Science.gov (United States)

    Harris, Leanne; McFarlane-Majeed, Laura; Campos-León, Karen; Roberts, Sally; Parish, Joanna L

    2017-01-01

    In papillomavirus infections, the viral genome is established as a double-stranded DNA episome. To segregate the episomes into daughter cells during mitosis, they are tethered to cellular chromatin by the viral E2 protein. We previously demonstrated that the E2 proteins of diverse papillomavirus types, including bovine papillomavirus (BPV) and human papillomavirus 16 (HPV16), associate with the cellular DNA helicase ChlR1. This virus-host interaction is important for the tethering of BPV E2 to mitotic chromatin and the stable maintenance of BPV episomes. The role of the association between E2 and ChlR1 in the HPV16 life cycle is unresolved. Here we show that an HPV16 E2 Y131A mutant (E2 Y131A ) had significantly reduced binding to ChlR1 but retained transcriptional activation and viral origin-dependent replication functions. Subcellular fractionation of keratinocytes expressing E2 Y131A showed a marked change in the localization of the protein. Compared to that of wild-type E2 (E2 WT ), the chromatin-bound pool of E2 Y131A was decreased, concomitant with an increase in nuclear matrix-associated protein. Cell cycle synchronization indicated that the shift in subcellular localization of E2 Y131A occurred in mid-S phase. A similar alteration between the subcellular pools of the E2 WT protein occurred upon ChlR1 silencing. Notably, in an HPV16 life cycle model in primary human keratinocytes, mutant E2 Y131A genomes were established as episomes, but at a markedly lower copy number than that of wild-type HPV16 genomes, and they were not maintained upon cell passage. Our studies indicate that ChlR1 is an important regulator of the chromatin association of E2 and of the establishment and maintenance of HPV16 episomes. Infections with high-risk human papillomaviruses (HPVs) are a major cause of anogenital and oropharyngeal cancers. During infection, the circular DNA genome of HPV persists within the nucleus, independently of the host cell chromatin. Persistence of infection

  7. Effects of the combinations of caffeine with 137Cs-gamma rays or tritiated water on the proliferation and malignant transformation CHL-1 cells

    International Nuclear Information System (INIS)

    Zou Shuai; Wang Shoufang

    1989-01-01

    The effects of the combinations of caffeine with 137 Cs-gamma rays or tritiated water on the proliferation and malignant transformation in vitro in CHL-1 cells were observed in experiments. At the concentrations of caffeine from 1 mmol/L to 2 mmol/L, the dose ranges of 137 Cs-gamma rays from 0.837 Gy and to 2.51 Gy and of tritium-beta radiation from 0.837 Gy to 0.528 Gy, the cell proliferation of CHL-1 cells was found to be inbigited when cells were exposed to caffeine, gamma and beta radiations, respectively, as well as when they were exposed to various combinations of caffeine with the two latters. The degree of inhibition of cell proliferation was dependent upon the concentration of caffeine and on the doses of radiation. In the transformation experiments, cell malignant transformation rates for all treated groups were higher than that for contol group and the rates for irradiated plus caffeine-treated groups were higher than those for corresponding single-agent-treated ones. After the subcutaneous injection of transformed cells into irradiated mice, tumours in size of about 2 mm 3 were found in some animals and the tumour cells were identical with in-vitro-transformed CHL-1 cells histopathologically

  8. The chlorophyll-deficient golden leaf mutation in cucumber is due to a single nucleotide substitution in CsChlI for magnesium chelatase I subunit.

    Science.gov (United States)

    Gao, Meiling; Hu, Liangliang; Li, Yuhong; Weng, Yiqun

    2016-10-01

    The cucumber chlorophyll-deficient golden leaf mutation is due to a single nucleotide substitution in the CsChlI gene for magnesium chelatase I subunit which plays important roles in the chlorophyll biosynthesis pathway. The Mg-chelatase catalyzes the insertion of Mg(2+) into the protoporphyrin IX in the chlorophyll biosynthesis pathway, which is a protein complex encompassing three subunits CHLI, CHLD, and CHLH. Chlorophyll-deficient mutations in genes encoding the three subunits have played important roles in understanding the structure, function and regulation of this important enzyme. In an EMS mutagenesis population, we identified a chlorophyll-deficient mutant C528 with golden leaf color throughout its development which was viable and able to set fruits and seeds. Segregation analysis in multiple populations indicated that this leaf color mutation was recessively inherited and the green color showed complete dominance over golden color. Map-based cloning identified CsChlI as the candidate gene for this mutation which encoded the CHLI subunit of cucumber Mg-chelatase. The 1757-bp CsChlI gene had three exons and a single nucleotide change (G to A) in its third exon resulted in an amino acid substitution (G269R) and the golden leaf color in C528. This mutation occurred in the highly conserved nucleotide-binding domain of the CHLI protein in which chlorophyll-deficient mutations have been frequently identified. The mutant phenotype, CsChlI expression pattern and the mutated residue in the CHLI protein suggested the mutant allele in C528 is unique among mutations identified so far in different species. This golden leaf mutant not only has its potential in cucumber breeding, but also provides a useful tool in understanding the CHLI function and its regulation in the chlorophyll biosynthesis pathway as well as chloroplast development.

  9. Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales

    Science.gov (United States)

    Fergus, C. Emi; Finley, Andrew O.; Soranno, Patricia A.; Wagner, Tyler

    2016-01-01

    The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a

  10. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Directory of Open Access Journals (Sweden)

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  11. Arctic Ocean CO2 uptake: an improved multiyear estimate of the air-sea CO2 flux incorporating chlorophyll a concentrations

    Science.gov (United States)

    Yasunaka, Sayaka; Siswanto, Eko; Olsen, Are; Hoppema, Mario; Watanabe, Eiji; Fransson, Agneta; Chierici, Melissa; Murata, Akihiko; Lauvset, Siv K.; Wanninkhof, Rik; Takahashi, Taro; Kosugi, Naohiro; Omar, Abdirahman M.; van Heuven, Steven; Mathis, Jeremy T.

    2018-03-01

    We estimated monthly air-sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014. This was done by mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll a concentration (Chl a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. We applied new algorithms for extracting Chl a from satellite remote sensing reflectance with close examination of uncertainty of the obtained Chl a values. The overall relationship between pCO2w and Chl a was negative, whereas the relationship varied among seasons and regions. The addition of Chl a as a parameter in the SOM process enabled us to improve the estimate of pCO2w, particularly via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net annual Arctic Ocean CO2 uptake of 180 ± 130 Tg C yr-1. Seasonal to interannual variation in the CO2 influx was also calculated.

  12. Broad-scale predictability of carbohydrates and exopolymers in Antarctic and Arctic sea ice

    Science.gov (United States)

    Underwood, Graham J. C.; Aslam, Shazia N.; Michel, Christine; Niemi, Andrea; Norman, Louiza; Meiners, Klaus M.; Laybourn-Parry, Johanna; Paterson, Harriet; Thomas, David N.

    2013-01-01

    Sea ice can contain high concentrations of dissolved organic carbon (DOC), much of which is carbohydrate-rich extracellular polymeric substances (EPS) produced by microalgae and bacteria inhabiting the ice. Here we report the concentrations of dissolved carbohydrates (dCHO) and dissolved EPS (dEPS) in relation to algal standing stock [estimated by chlorophyll (Chl) a concentrations] in sea ice from six locations in the Southern and Arctic Oceans. Concentrations varied substantially within and between sampling sites, reflecting local ice conditions and biological content. However, combining all data revealed robust statistical relationships between dCHO concentrations and the concentrations of different dEPS fractions, Chl a, and DOC. These relationships were true for whole ice cores, bottom ice (biomass rich) sections, and colder surface ice. The distribution of dEPS was strongly correlated to algal biomass, with the highest concentrations of both dEPS and non-EPS carbohydrates in the bottom horizons of the ice. Complex EPS was more prevalent in colder surface sea ice horizons. Predictive models (validated against independent data) were derived to enable the estimation of dCHO concentrations from data on ice thickness, salinity, and vertical position in core. When Chl a data were included a higher level of prediction was obtained. The consistent patterns reflected in these relationships provide a strong basis for including estimates of regional and seasonal carbohydrate and dEPS carbon budgets in coupled physical-biogeochemical models, across different types of sea ice from both polar regions. PMID:24019487

  13. Broad-scale predictability of carbohydrates and exopolymers in Antarctic and Arctic sea ice.

    Science.gov (United States)

    Underwood, Graham J C; Aslam, Shazia N; Michel, Christine; Niemi, Andrea; Norman, Louiza; Meiners, Klaus M; Laybourn-Parry, Johanna; Paterson, Harriet; Thomas, David N

    2013-09-24

    Sea ice can contain high concentrations of dissolved organic carbon (DOC), much of which is carbohydrate-rich extracellular polymeric substances (EPS) produced by microalgae and bacteria inhabiting the ice. Here we report the concentrations of dissolved carbohydrates (dCHO) and dissolved EPS (dEPS) in relation to algal standing stock [estimated by chlorophyll (Chl) a concentrations] in sea ice from six locations in the Southern and Arctic Oceans. Concentrations varied substantially within and between sampling sites, reflecting local ice conditions and biological content. However, combining all data revealed robust statistical relationships between dCHO concentrations and the concentrations of different dEPS fractions, Chl a, and DOC. These relationships were true for whole ice cores, bottom ice (biomass rich) sections, and colder surface ice. The distribution of dEPS was strongly correlated to algal biomass, with the highest concentrations of both dEPS and non-EPS carbohydrates in the bottom horizons of the ice. Complex EPS was more prevalent in colder surface sea ice horizons. Predictive models (validated against independent data) were derived to enable the estimation of dCHO concentrations from data on ice thickness, salinity, and vertical position in core. When Chl a data were included a higher level of prediction was obtained. The consistent patterns reflected in these relationships provide a strong basis for including estimates of regional and seasonal carbohydrate and dEPS carbon budgets in coupled physical-biogeochemical models, across different types of sea ice from both polar regions.

  14. Drivers and variability of the Chl fluorescence emission spectrum from the leaf through the canopy

    Science.gov (United States)

    Magney, T. S.; Frankenberg, C.; Grossman, K.; Koehler, P.; North, G.; Porcar-Castell, A.; Stutz, J.; Fisher, J.

    2017-12-01

    Recent advances in the retrieval of solar induced chlorophyll fluorescence (SIF) from remote sensing platforms provide a significant step towards mapping instantaneous plant photosynthesis across space and time. However, our current understanding of the variability and controls on the shape of the chlorophyll fluorescence (ChlF) spectrum is limited. To address these uncertainties, we have developed instrumentation to make highly resolved spectral measurements of SIF from both leaf and canopy scales. At the leaf scale, we simultaneously collected active (PAM) and passive (675-850 nm) fluorescence with photosynthesis across a range of species and conditions; and at the canopy scale, diurnal and seasonal Fraunhofer-based SIF retrievals across the red and far-red spectrum are made at four different flux tower sites (Costa Rica, Iowa (2), and Colorado). From both of these scales we are able to determine (1) the variability in steady-state spectra across species and individuals; and (2) the environmental, functional, and structural controls on SIF. Here we report on the sensitivity of SIF spectra from a singular value decomposition analysis; and present on the mechanisms - pigment concentration, species, non-photochemical and photochemical quenching, and environmental conditions - controlling SIF variability. Further, we will discuss how an improved understanding of leaf-level variability can inform canopy level SIF, and ultimately how such information may enable proper interpretation of satellite retrievals.

  15. An improved MODIS standard chlorophyll-a algorithm for Malacca Straits Water

    International Nuclear Information System (INIS)

    Lah, N Z Ab; Reba, M N M; Siswanto, Eko

    2014-01-01

    The Malacca Straits has high productivity of nutrients as a result to potential primary production. Yet, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua has shown an overestimation of Chl-a retrieval in the case-2 water of Malacca Straits. In an update to the previous study, this paper presents the second validation exercise of MODIS OC3M algorithm using the reprocessed MODIS data (R2013) and locally tuned the algorithm with respect to two in-sit stations located at northern and southern part of Malacca Straits. The result shows the OC3M retrieved in the case-2 (south station) water remarkably overestimated in-situ Chl-a, but it is underestimated in the case-1 (north station). Local tuning was employed by iterative regression at the fourth-order polynomial to improve the accuracy of Chl-a retrieval. As a result, locally tuned OC3M algorithm give robust statistical performance and can be applied best for both case-1 and case-2 water in Malacca Straits

  16. Growth-Prediction Model for Blue Mussels (Mytilus edulis) on Future Optimally Thinned Farm-Ropes in Great Belt (Denmark)

    DEFF Research Database (Denmark)

    Larsen, Poul Scheel; Riisgård, Hans

    2016-01-01

    as a new Danish, smaller-sized consumer product. Field data for actual growth (from Day 0 = 14 June 2011) showed that size of ‘mini-mussel’ was reached on Day 109 (Oct 1) and length 38 mm on Day 178 (Dec 9) while the corresponding predictions using the extended model were Day 121 (Oct 13) and Day 159 (Nov...... 20). Similar results were obtained by use of the ad hoc BEG model which also demonstrated the sensitivity of growth prediction to levels of chl a concentration, but less to temperature. The results suggest that it is possible (when the conditions are optimal, i.e., no intraspecific competition...... competition may deviate from the potential (optimal) growth under specified chl a and temperature conditions, and this implies that the effect of thinning to optimize the individual growth by eliminating intraspecific competition can be rationally evaluated....

  17. Chlorophyll-a retrieval in the Philippine waters

    Science.gov (United States)

    Perez, G. J. P.; Leonardo, E. M.; Felix, M. J.

    2017-12-01

    results to a significant improvement on calculated RMSE of satellite-derived Chl-a values. Meanwhile, it was observed that the blue-green band ratio has low Chl-a predictive capability in turbid waters. A more accurate estimation was found using the NIR and red band ratios for turbid waters with covarying Chl-a concentration and low sediment load.

  18. Bio-Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images

    Science.gov (United States)

    Shulman, Igor; Gould, Richard W.; Frolov, Sergey; McCarthy, Sean; Penta, Brad; Anderson, Stephanie; Sakalaukus, Peter

    2018-03-01

    An ensemble-based approach to specify observational error covariance in the data assimilation of satellite bio-optical properties is proposed. The observational error covariance is derived from statistical properties of the generated ensemble of satellite MODIS-Aqua chlorophyll (Chl) images. The proposed observational error covariance is used in the Optimal Interpolation scheme for the assimilation of MODIS-Aqua Chl observations. The forecast error covariance is specified in the subspace of the multivariate (bio-optical, physical) empirical orthogonal functions (EOFs) estimated from a month-long model run. The assimilation of surface MODIS-Aqua Chl improved surface and subsurface model Chl predictions. Comparisons with surface and subsurface water samples demonstrate that data assimilation run with the proposed observational error covariance has higher RMSE than the data assimilation run with "optimistic" assumption about observational errors (10% of the ensemble mean), but has smaller or comparable RMSE than data assimilation run with an assumption that observational errors equal to 35% of the ensemble mean (the target error for satellite data product for chlorophyll). Also, with the assimilation of the MODIS-Aqua Chl data, the RMSE between observed and model-predicted fractions of diatoms to the total phytoplankton is reduced by a factor of two in comparison to the nonassimilative run.

  19. Improving Earth/Prediction Models to Improve Network Processing

    Science.gov (United States)

    Wagner, G. S.

    2017-12-01

    The United States Atomic Energy Detection System (USAEDS) primaryseismic network consists of a relatively small number of arrays andthree-component stations. The relatively small number of stationsin the USAEDS primary network make it both necessary and feasibleto optimize both station and network processing.Station processing improvements include detector tuning effortsthat use Receiver Operator Characteristic (ROC) curves to helpjudiciously set acceptable Type 1 (false) vs. Type 2 (miss) errorrates. Other station processing improvements include the use ofempirical/historical observations and continuous background noisemeasurements to compute time-varying, maximum likelihood probabilityof detection thresholds.The USAEDS network processing software makes extensive use of theazimuth and slowness information provided by frequency-wavenumberanalysis at array sites, and polarization analysis at three-componentsites. Most of the improvements in USAEDS network processing aredue to improvements in the models used to predict azimuth, slowness,and probability of detection. Kriged travel-time, azimuth andslowness corrections-and associated uncertainties-are computedusing a ground truth database. Improvements in station processingand the use of improved models for azimuth, slowness, and probabilityof detection have led to significant improvements in USADES networkprocessing.

  20. Growth-Prediction Model for Blue Mussels (Mytilus edulis on Future Optimally Thinned Farm-Ropes in Great Belt (Denmark

    Directory of Open Access Journals (Sweden)

    Poul S. Larsen

    2016-07-01

    Full Text Available A recently developed BioEnergetic Growth (BEG model for blue mussels (Mytilus edulis, valid for juvenile mussels, has been further developed to an ‘extended model’ and an alternative ‘ad hoc BEG model’ valid for post-metamorphic mussels, where the latter accounts for changing ambient chl a concentration. It was used to predict the growth of M. edulis on optimally thinned farm-ropes in Great Belt (Denmark, from newly settled post-metamorphic mussels of an initial shell size of 0.8 mm to marketable juvenile 30–35 mm ‘mini-mussels’. Such mussels will presumably in the near future be introduced as a new Danish, smaller-sized consumer product. Field data for actual growth (from Day 0 = 14 June 2011 showed that size of ‘mini-mussel’ was reached on Day 109 (Oct 1 and length 38 mm on Day 178 (Dec 9 while the corresponding predictions using the extended model were Day 121 (Oct 13 and Day 159 (Nov 20. Similar results were obtained by use of the ad hoc BEG model which also demonstrated the sensitivity of growth prediction to levels of chl a concentration, but less to temperature. The results suggest that it is possible (when the conditions are optimal, i.e., no intraspecific competition ensured by sufficient thinning to produce ‘mini-mussels’ in Great Belt during one season, but not the usual marketable 45-mm mussels. We suggest that the prediction model may be used as a practical instrument to evaluate to what degree the actual growth of mussels on farm ropes due to intraspecific competition may deviate from the potential (optimal growth under specified chl a and temperature conditions, and this implies that the effect of thinning to optimize the individual growth by eliminating intraspecific competition can be rationally evaluated.

  1. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

  2. Evaluating the Accuracy of MODIS Products in the Southern Scean Using Tagged Marine Predators, and Measuring Significant Change in 12 Years of [Chl-a], Zeu and Cloud Fraction Data.

    Science.gov (United States)

    Biermann, L.; Boehme, L.; Guinet, C.

    2016-02-01

    The Southern Ocean is vital to the functioning of our global atmospheric and marine systems. However, this key ocean is also measurably responsive to the Southern Annular Mode (SAM), the dominant mode of atmospheric variability in the Southern Hemisphere. Decreased ozone and increases in greenhouse gases appear to be forcing the SAM towards its positive phase, significantly changing wind patterns and, thus, altering mixing and circulation regimes of Southern Ocean waters. Inevitably, these changes must impact on patterns of phytoplankton abundance and distribution. Using remotely sensed data that have been evaluated alongside in situ data collected by tagged southern elephant seals, this work investigates if changes to summer phytoplankton abundance and distribution in the Southern Ocean can already be measured in the 12-year MODIS record. Patterns and trends in surface chlorophyll-a concentration ([Chl-a]), the depth of the 1% light level (Zeu) and mean cloud fraction are examined over time, as well as relative to the SAM. Trends in [Chl-a] and Zeu over the months of October, November and December suggest overall declines in surface phytoplankton, and shifts in timing of blooms. Indeed, by January and February over the 12-year timeseries, trends reverse to suggest increases in phytoplankton abundance. Relative to the increasingly positive SAM, trends of overall decline in phytoplankton abundance are significant only over Decembers. Trends in cloud cover are more difficult to interpret but the Atlantic Ocean appears to be becoming less cloudy, the southern sector of the Pacific Ocean appears to be becoming cloudier, and that the southern sector of the Indian Ocean is most variable over time. Only the increase in cloud over the southern Pacific in Decembers appears to be significantly related to changes to the SAM. Interestingly, in no cases were the changes to [Chl-a], Zeu or cloud cover strictly zonal. The asymmetry of these results reinforces findings from

  3. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  4. Improved Modeling and Prediction of Surface Wave Amplitudes

    Science.gov (United States)

    2017-05-31

    AFRL-RV-PS- AFRL-RV-PS- TR-2017-0162 TR-2017-0162 IMPROVED MODELING AND PREDICTION OF SURFACE WAVE AMPLITUDES Jeffry L. Stevens, et al. Leidos...data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented...SUBTITLE Improved Modeling and Prediction of Surface Wave Amplitudes 5a. CONTRACT NUMBER FA9453-14-C-0225 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER

  5. Improving contact prediction along three dimensions.

    Directory of Open Access Journals (Sweden)

    Christoph Feinauer

    2014-10-01

    Full Text Available Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i filter and align the raw sequence data representing the evolutionarily related proteins; (ii choose a predictive model to describe a sequence alignment; (iii infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.

  6. Text mining improves prediction of protein functional sites.

    Directory of Open Access Journals (Sweden)

    Karin M Verspoor

    Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  7. Text Mining Improves Prediction of Protein Functional Sites

    Science.gov (United States)

    Cohn, Judith D.; Ravikumar, Komandur E.

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

  8. Evaluation of Hyperspectral Multi-Band Indices to Estimate Chlorophyll-A Concentration Using Field Spectral Measurements and Satellite Data in Dianshan Lake, China

    Directory of Open Access Journals (Sweden)

    Linna Li

    2013-04-01

    Full Text Available Chlorophyll-a (Chl-a concentration is considered as a key indicator of the eutrophic status of inland water bodies. Various algorithms have been developed for estimating Chl-a in order to improve the accuracy of predictive models. The objective of this study is to assess the potential of hyperspectral multi-band indices to estimate the Chl-a concentration in Dianshan Lake, which is the largest lake in Shanghai, an international metropolis of China. Based on field spectral measurements and in-situ Chl-a concentration collected on 7–8 September 2010, hyperspectral multi-band indices were calibrated to estimate the Chl-a concentration with optimal wavelengths selected by model tuning. A three-band index accounts for 87.36% (R2 = 0.8736 of the Chl-a variation. A four-band index, which adds a wavelength in the near infrared (NIR region, results in a higher R2 (0.8997 by removing the absorption and backscattering effects of suspended solids. To test the applicability of the proposed indices for routinely monitoring of Chl-a in inland lakes, simulated Hyperion and real HJ-1A satellite data were selected to estimate the Chl-a concentration. The results show that the explanatory powers of these satellite hyperspectral multi-band indices are relatively high with R2 = 0.8559, 0.8945, 0.7969, and 0.8241 for simulated Hyperion and real HJ-1A satellite data, respectively. All of the results provide strong evidence that hyperspectral multi-band indices are promising and applicable to estimate Chl-a in eutrophic inland lakes.

  9. Improved Chlorophyll-a Algorithm for the Satellite Ocean Color Data in the Northern Bering Sea and Southern Chukchi Sea

    Science.gov (United States)

    Lee, Sang Heon; Ryu, Jongseong; Park, Jung-woo; Lee, Dabin; Kwon, Jae-Il; Zhao, Jingping; Son, SeungHyun

    2018-03-01

    The Bering and Chukchi seas are an important conduit to the Arctic Ocean and are reported to be one of the most productive regions in the world's oceans in terms of high primary productivity that sustains large numbers of fishes, marine mammals, and sea birds as well as benthic animals. Climate-induced changes in primary production and production at higher trophic levels also have been observed in the northern Bering and Chukchi seas. Satellite ocean color observations could enable the monitoring of relatively long term patterns in chlorophyll-a (Chl-a) concentrations that would serve as an indicator of phytoplankton biomass. The performance of existing global and regional Chl-a algorithms for satellite ocean color data was investigated in the northeastern Bering Sea and southern Chukchi Sea using in situ optical measurements from the Healy 2007 cruise. The model-derived Chl-a data using the previous Chl-a algorithms present striking uncertainties regarding Chl-a concentrations-for example, overestimation in lower Chl-a concentrations or systematic overestimation in the northeastern Bering Sea and southern Chukchi Sea. Accordingly, a simple two band ratio (R rs(443)/R rs(555)) algorithm of Chl-a for the satellite ocean color data was devised for the northeastern Bering Sea and southern Chukchi Sea. The MODIS-derived Chl-a data from July 2002 to December 2014 were produced using the new Chl-a algorithm to investigate the seasonal and interannual variations of Chl-a in the northern Bering Sea and the southern Chukchi Sea. The seasonal distribution of Chl-a shows that the highest (spring bloom) Chl-a concentrations are in May and the lowest are in July in the overall area. Chl-a concentrations relatively decreased in June, particularly in the open ocean waters of the Bering Sea. The Chl-a concentrations start to increase again in August and become quite high in September. In October, Chl-a concentrations decreased in the western area of the Study area and the Alaskan

  10. Improving orbit prediction accuracy through supervised machine learning

    Science.gov (United States)

    Peng, Hao; Bai, Xiaoli

    2018-05-01

    Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

  11. Combining gene prediction methods to improve metagenomic gene annotation

    Directory of Open Access Journals (Sweden)

    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  12. Improved Wind Speed Prediction Using Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2018-05-01

    Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

  13. Improvement of gas entrainment prediction method. Introduction of surface tension effect

    International Nuclear Information System (INIS)

    Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

    2010-01-01

    A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

  14. A GIS Approach to Wind,SST(Sea Surface Temperature) and CHL(Chlorophyll) variations in the Caspian Sea

    Science.gov (United States)

    Mirkhalili, Seyedhamzeh

    2016-07-01

    Chlorophyll is an extremely important bio-molecule, critical in photosynthesis, which allows plants to absorb energy from light. At the base of the ocean food web are single-celled algae and other plant-like organisms known as Phytoplankton. Like plants on land, Phytoplankton use chlorophyll and other light-harvesting pigments to carry out photosynthesis. Where Phytoplankton grow depends on available sunlight, temperature, and nutrient levels. In this research a GIS Approach using ARCGIS software and QuikSCAT satellite data was applied to visualize WIND,SST(Sea Surface Temperature) and CHL(Chlorophyll) variations in the Caspian Sea.Results indicate that increase in chlorophyll concentration in coastal areas is primarily driven by terrestrial nutrients and does not imply that warmer SST will lead to an increase in chlorophyll concentration and consequently Phytoplankton abundance.

  15. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  16. Parameterization of the chlorophyll a-specific in vivo light absorption coefficient covering estuarine, coastal and oceanic waters

    DEFF Research Database (Denmark)

    Stæhr, P. A.; Markager, S.

    2004-01-01

    We evaluated models predicting the spectral chlorophyll-a (Chl a)-specific absorption coefficient (a*ph (¿)) from Chl a concentration [Chl a] on the basis of 465 phytoplankton absorption spectra collected in estuarine, coastal and oceanic waters. A power model on ln-transformed data provided...

  17. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    Science.gov (United States)

    Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

    2017-01-01

    Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

  18. An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

    Directory of Open Access Journals (Sweden)

    Guoxing Li

    2015-01-01

    Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

  19. Recent Improvements in IERS Rapid Service/Prediction Center Products

    National Research Council Canada - National Science Library

    Stamatakos, N; Luzum, B; Wooden, W

    2007-01-01

    ...) at USNO has made several improvements to its combination and pre- diction products. These improvements are due to the inclusion of new input data sources as well as modifications to the combination and prediction algorithms...

  20. Brassinosteroids improve photosystem II efficiency, gas exchange, antioxidant enzymes and growth of cowpea plants exposed to water deficit.

    Science.gov (United States)

    Lima, J V; Lobato, A K S

    2017-01-01

    Water deficit is considered the main abiotic stress that limits agricultural production worldwide. Brassinosteroids (BRs) are natural substances that play roles in plant tolerance against abiotic stresses, including water deficit. This research aims to determine whether BRs can mitigate the negative effects caused by water deficiency, revealing how BRs act and their possible contribution to increased tolerance of cowpea plants to water deficit. The experiment was a factorial design with the factors completely randomised, with two water conditions (control and water deficit) and three levels of brassinosteroids (0, 50 and 100 nM 24-epibrassinolide; EBR is an active BRs). Plants sprayed with 100 nM EBR under the water deficit presented significant increases in Φ PSII , q P and ETR compared with plants subjected to the water deficit without EBR. With respect to gas exchange, P N , E and g s exhibited significant reductions after water deficit, but application of 100 nM EBR caused increases in these variables of 96, 24 and 33%, respectively, compared to the water deficit + 0 nM EBR treatment. To antioxidant enzymes, EBR resulted in increases in SOD, CAT, APX and POX, indicating that EBR acts on the antioxidant system, reducing cell damage. The water deficit caused significant reductions in Chl a , Chl b and total Chl, while plants sprayed with 100 nM EBR showed significant increases of 26, 58 and 33% in Chl a , Chl b and total Chl, respectively. This study revealed that EBR improves photosystem II efficiency, inducing increases in Φ PSII , q P and ETR. This substance also mitigated the negative effects on gas exchange and growth induced by the water deficit. Increases in SOD, CAT, APX and POX of plants treated with EBR indicate that this steroid clearly increased the tolerance to the water deficit, reducing reactive oxygen species, cell damage, and maintaining the photosynthetic pigments. Additionally, 100 nM EBR resulted in a better dose-response of cowpea

  1. Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school.

    Science.gov (United States)

    Maurer, Urs; Bucher, Kerstin; Brem, Silvia; Benz, Rosmarie; Kranz, Felicitas; Schulz, Enrico; van der Mark, Sanne; Steinhausen, Hans-Christoph; Brandeis, Daniel

    2009-08-15

    More struggling readers could profit from additional help at the beginning of reading acquisition if dyslexia prediction were more successful. Currently, prediction is based only on behavioral assessment of early phonological processing deficits associated with dyslexia, but it might be improved by adding brain-based measures. In a 5-year longitudinal study of children with (n = 21) and without (n = 23) familial risk for dyslexia, we tested whether neurophysiological measures of automatic phoneme and tone deviance processing obtained in kindergarten would improve prediction of reading over behavioral measures alone. Together, neurophysiological and behavioral measures obtained in kindergarten significantly predicted reading in school. Particularly the late mismatch negativity measure that indicated hemispheric lateralization of automatic phoneme processing improved prediction of reading ability over behavioral measures. It was also the only significant predictor for long-term reading success in fifth grade. Importantly, this result also held for the subgroup of children at familial risk. The results demonstrate that brain-based measures of processing deficits associated with dyslexia improve prediction of reading and thus may be further evaluated to complement clinical practice of dyslexia prediction, especially in targeted populations, such as children with a familial risk.

  2. Machine Learning Principles Can Improve Hip Fracture Prediction

    DEFF Research Database (Denmark)

    Kruse, Christian; Eiken, Pia; Vestergaard, Peter

    2017-01-01

    Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

  3. Improving urban wind flow predictions through data assimilation

    Science.gov (United States)

    Sousa, Jorge; Gorle, Catherine

    2017-11-01

    Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

  4. Gestational Age Assessment with Anthropometric Parameters in Newborns

    Directory of Open Access Journals (Sweden)

    Niloy Kumar Das1,

    2018-05-01

    Full Text Available Objectives: We sought to evaluate the relationship between gestational age (GA and neonatal anthropometric parameters, namely head circumference (HC and crown-heel length (CHL. Methods: We conducted a cross-sectional study in a tertiary care hospital with 530 consecutively live-born newborns of 28–41 weeks gestation. Anthropometric parameters were measured after three days of life. We summarized the variables using descriptive statistics, including percentile values, and the strength of association was determined through correlation analysis. The correlation was strong for HC and CHL, and linear regression analysis was done to develop predictive equations. Results: HC and CHL correlated well with GA with r-values of 0.863 and 0.859, respectively. The regression equations derived were GA (week = 9.2671 + [0.8616 × HC (cm] and GA (weeks = 7.2489 + [0.621 × CHL (cm]. Multiple regression gave the relationship as GA (weeks = 4.0244 + [0.4058 × HC (cm] + [0.4249× CHL (cm]. Application of this multiple regression equation to a test cohort of 30 babies for prediction of GA gave a mean margin of error of 2.9%, indicating that it is a satisfactory tool for prediction. Conclusions: HC and CHL can be used as simple tools for predicting GA in babies when this is in doubt. This can help in identification of high-risk newborns at primary care level without recourse to imaging modalities.

  5. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

    Directory of Open Access Journals (Sweden)

    Sun Zhangzhen

    2012-08-01

    Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

  6. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  7. Reactive oxygen production induced by near-infrared radiation in three strains of the Chl d-containing cyanobacterium Acaryochloris marina [v1; ref status: indexed, http://f1000r.es/o4

    Directory of Open Access Journals (Sweden)

    Lars Behrendt

    2013-02-01

    Full Text Available Cyanobacteria in the genus Acaryochloris have largely exchanged Chl a with Chl d, enabling them to harvest near-infrared radiation (NIR for oxygenic photosynthesis, a biochemical pathway prone to generate reactive oxygen species (ROS. In this study, ROS production under different light conditions was quantified in three Acaryochloris strains (MBIC11017, HICR111A and the novel strain CRS using a real-time ethylene detector in conjunction with addition of 2-keto-4-thiomethylbutyric acid, a substrate that is converted to ethylene when reacting with certain types of ROS. In all strains, NIR was found to generate less ROS than visible light (VIS. More ROS was generated if strains MBIC11017 and HICR111A were adapted to NIR and then exposed to VIS, while strain CRS demonstrated the opposite behavior. To our knowledge, this is the first study of ROS generation associated with NIR-driven oxygenic photosynthesis and it suggests that Acaryochloris can avoid a considerable amount of light-induced stress by using NIR instead of VIS for its photosynthesis, adding further evolutionary arguments to their widespread appearance.

  8. Reactive oxygen production induced by near-infrared radiation in three strains of the Chl d-containing cyanobacterium Acaryochloris marina [v2; ref status: indexed, http://f1000r.es/z7

    Directory of Open Access Journals (Sweden)

    Lars Behrendt

    2013-03-01

    Full Text Available Cyanobacteria in the genus Acaryochloris have largely exchanged Chl a with Chl d, enabling them to harvest near-infrared-radiation (NIR for oxygenic photosynthesis, a biochemical pathway prone to generate reactive oxygen species (ROS. In this study, ROS production under different light conditions was quantified in three Acaryochloris strains (MBIC11017, HICR111A and the novel strain CRS using a real-time ethylene detector in conjunction with addition of 2-keto-4-thiomethylbutyric acid, a substrate that is converted to ethylene when reacting with certain types of ROS. In all strains, NIR was found to generate less ROS than visible light (VIS. More ROS was generated if strains MBIC11017 and HICR111A were adapted to NIR and then exposed to VIS, while strain CRS demonstrated the opposite behavior. This is the very first study of ROS generation and suggests that Acaryochloris can avoid a considerable amount of light-induced stress by using NIR instead of VIS for its photosynthesis, adding further evolutionary arguments to their widespread appearance.

  9. NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

    Science.gov (United States)

    Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

    2017-12-01

    NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

  10. Genomic instability induced by 137Cs γ-ray irradiation in CHL surviving cells

    International Nuclear Information System (INIS)

    Yue Jingyin; Liu Bingchen; Wu Hongying; Zhou Jiwen; Mu Chuanjie

    1999-01-01

    Objective: To study in parallel several possible manifestations of instability of surviving CHL cells after irradiation, namely the frequencies of mutation at locus, micronuclei and apoptosis. Methods: The frequencies of mutation at HGPRT locus, micronuclei and apoptosis were assayed at various times in surviving cells irradiated with γ-rays. Results: The surviving cells showed a persistently increased frequency of mutation at the HGPRT locus after irradiation until 53 days. Mutant fraction as high as 10 -4 was scored, tens of times higher than those assayed in control cells studied in parallel. The frequency of bi nucleated cells with micronuclei determined within 24 hours after irradiation increased with dose and reached a peak value of (26.58 +- 2.48)% at 3 Gy, decreasing at higher doses to a plateau around 20%. The micronucleus frequency decreased steeply to about (14.47 +- 2.39)% within the first 3 days post-irradiation, and fluctuated at around 10% up to 56 days post-irradiation. The delayed efficiency of irradiated cells was significantly decreased. The frequency of apoptosis peaked about (24.90 +- 4.72)% at 10 Gy 48 h post-irradiation (γ-ray dose between 3-10 Gy) and then decreased to about 12% within 3 days. It was significantly higher than in control cells until 14 days. Conclusions: It shows that genomic instability induced by radiation can be transmitted to the progeny of surviving cells and may take many forms of expression such as lethal mutation, chromosome aberrations, gene mutation, etc

  11. Inland-coastal water interaction: Remote sensing application for shallow-water quality and algal blooms modeling

    Science.gov (United States)

    Melesse, Assefa; Hajigholizadeh, Mohammad; Blakey, Tara

    2017-04-01

    In this study, Landsat 8 and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) sensors were used to model the spatiotemporal changes of four water quality parameters: Landsat 8 (turbidity, chlorophyll-a (chl-a), total phosphate, and total nitrogen) and Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) (algal blooms). The study was conducted in Florda bay, south Florida and model outputs were compared with in-situ observed data. The Landsat 8 based study found that, the predictive models to estimate chl-a and turbidity concentrations, developed through the use of stepwise multiple linear regression (MLR), gave high coefficients of determination in dry season (wet season) (R2 = 0.86(0.66) for chl-a and R2 = 0.84(0.63) for turbidity). Total phosphate and TN were estimated using best-fit multiple linear regression models as a function of Landsat TM and OLI,127 and ground data and showed a high coefficient of determination in dry season (wet season) (R2 = 0.74(0.69) for total phosphate and R2 = 0.82(0.82) for TN). Similarly, the ability of SeaWIFS for chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels' benthic class was evaluated. Benthic class was determined through satellite image-based classification methods. It was found that benthic class based chl-a modeling algorithm was better than the existing regionally-tuned approach. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Key words: Landsat, SeaWIFS, water quality, Florida bay, Chl-a, turbidity

  12. Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction.

    Science.gov (United States)

    Chen, Chia-Yen; Han, Jiali; Hunter, David J; Kraft, Peter; Price, Alkes L

    2015-09-01

    Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction. © 2015 WILEY PERIODICALS, INC.

  13. Phytochrome B Mediates the Regulation of Chlorophyll Biosynthesis through Transcriptional Regulation of ChlH and GUN4 in Rice Seedlings

    Science.gov (United States)

    Kagawa, Takatoshi; Tanaka, Ayumi; Ueno, Osamu; Shimada, Hiroaki; Takano, Makoto

    2015-01-01

    Accurate regulation of chlorophyll synthesis is crucial for chloroplast formation during the greening process in angiosperms. In this study, we examined the role of phytochrome B (phyB) in the regulation of chlorophyll synthesis in rice seedlings (Oryza sativa L.) through the characterization of a pale-green phenotype observed in the phyB mutant grown under continuous red light (Rc) irradiation. Our results show that the Rc-induced chlorophyll accumulation can be divided into two components—a phyB-dependent and a phyB-independent component, and that the pale-green phenotype is caused by the absence of the phyB-dependent component. To elucidate the role of the missing component we established an Rc-induced greening experiment, the results of which revealed that several genes encoding proteins on the chlorophyll branch were repressed in the phyB mutant. Notable among them were ChlH and GUN4 genes, which encode subunit H and an activating factor of magnesium chelatase (Mg-chelatase), respectively, that were largely repressed in the mutant. Moreover, the kinetic profiles of chlorophyll precursors suggested that Mg-chelatase activity simultaneously decreased with the reduction in the transcript levels of ChlH and GUN4. These results suggest that phyB mediates the regulation of chlorophyll synthesis through transcriptional regulation of these two genes, whose products exert their action at the branching point of the chlorophyll biosynthesis pathway. Reduction of 5-aminolevulinic acid (5-ALA) synthesis could be detected in the mutant, but the kinetic profiles of chlorophyll precursors indicated that it was an event posterior to the reduction of the Mg-chelatase activity. It means that the repression of 5-ALA synthesis should not be a triggering event for the appearance of the pale-green phenotype. Instead, the repression of 5-ALA synthesis might be important for the subsequent stabilization of the pale-green phenotype for preventing excessive accumulation of hazardous

  14. Chlorophyll Can Be Reduced in Crop Canopies with Little Penalty to Photosynthesis1[OPEN

    Science.gov (United States)

    Drewry, Darren T.; VanLoocke, Andy; Cho, Young B.

    2018-01-01

    The hypothesis that reducing chlorophyll content (Chl) can increase canopy photosynthesis in soybeans was tested using an advanced model of canopy photosynthesis. The relationship among leaf Chl, leaf optical properties, and photosynthetic biochemical capacity was measured in 67 soybean (Glycine max) accessions showing large variation in leaf Chl. These relationships were integrated into a biophysical model of canopy-scale photosynthesis to simulate the intercanopy light environment and carbon assimilation capacity of canopies with wild type, a Chl-deficient mutant (Y11y11), and 67 other mutants spanning the extremes of Chl to quantify the impact of variation in leaf-level Chl on canopy-scale photosynthetic assimilation and identify possible opportunities for improving canopy photosynthesis through Chl reduction. These simulations demonstrate that canopy photosynthesis should not increase with Chl reduction due to increases in leaf reflectance and nonoptimal distribution of canopy nitrogen. However, similar rates of canopy photosynthesis can be maintained with a 9% savings in leaf nitrogen resulting from decreased Chl. Additionally, analysis of these simulations indicate that the inability of Chl reductions to increase photosynthesis arises primarily from the connection between Chl and leaf reflectance and secondarily from the mismatch between the vertical distribution of leaf nitrogen and the light absorption profile. These simulations suggest that future work should explore the possibility of using reduced Chl to improve canopy performance by adapting the distribution of the “saved” nitrogen within the canopy to take greater advantage of the more deeply penetrating light. PMID:29061904

  15. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  16. Proteomic analysis identifies galectin-1 as a predictive biomarker for relapsed/refractory disease in classical Hodgkin lymphoma

    DEFF Research Database (Denmark)

    Kamper, Peter; Ludvigsen, Maja; Bendix, Knud

    2011-01-01

    Considerable effort has been spent identifying prognostic biomarkers in classic Hodgkin lymphoma (cHL). The aim of our study was to search for possible prognostic parameters in advanced-stage cHL using a proteomics-based strategy. A total of 14 cHL pretreatment tissue samples from younger, advanced......-stage patients were included. Patients were grouped according to treatment response. Proteins that were differentially expressed between the groups were analyzed using 2D-PAGE and identified by liquid chromatography mass spectrometry. Selected proteins were validated using Western blot analysis. One...

  17. Improving Flash Flood Prediction in Multiple Environments

    Science.gov (United States)

    Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

    2009-12-01

    Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

  18. Predictive Maintenance: One key to improved power plant availability

    International Nuclear Information System (INIS)

    Mobley; Allen, J.W.

    1986-01-01

    Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

  19. Benthic Light Availability Improves Predictions of Riverine Primary Production

    Science.gov (United States)

    Kirk, L.; Cohen, M. J.

    2017-12-01

    Light is a fundamental control on photosynthesis, and often the only control strongly correlated with gross primary production (GPP) in streams and rivers; yet it has received far less attention than nutrients. Because benthic light is difficult to measure in situ, surrogates such as open sky irradiance are often used. Several studies have now refined methods to quantify canopy and water column attenuation of open sky light in order to estimate the amount of light that actually reaches the benthos. Given the additional effort that measuring benthic light requires, we should ask if benthic light always improves our predictions of GPP compared to just open sky irradiance. We use long-term, high-resolution dissolved oxygen, turbidity, dissolved organic matter (fDOM), and irradiance data from streams and rivers in north-central Florida, US across gradients of size and color to build statistical models of benthic light that predict GPP. Preliminary results on a large, clear river show only modest model improvements over open sky irradiance, even in heavily canopied reaches with pulses of tannic water. However, in another spring-fed river with greater connectivity to adjacent wetlands - and hence larger, more frequent pulses of tannic water - the model improved dramatically with the inclusion of fDOM (model R2 improved from 0.28 to 0.68). River shade modeling efforts also suggest that knowing benthic light will greatly enhance our ability to predict GPP in narrower, forested streams flowing in particular directions. Our objective is to outline conditions where an assessment of benthic light conditions would be necessary for riverine metabolism studies or management strategies.

  20. BACE1 elevation engendered by GGA3 deletion increases β-amyloid pathology in association with APP elevation and decreased CHL1 processing in 5XFAD mice.

    Science.gov (United States)

    Kim, WonHee; Ma, Liang; Lomoio, Selene; Willen, Rachel; Lombardo, Sylvia; Dong, Jinghui; Haydon, Philip G; Tesco, Giuseppina

    2018-02-02

    β-site amyloid precursor protein cleaving enzyme 1 (BACE1) is the rate-limiting enzyme in the production of amyloid beta (Aβ), the toxic peptide that accumulates in the brains of Alzheimer's disease (AD) patients. Our previous studies have shown that the clathrin adaptor Golgi-localized γ-ear-containing ARF binding protein 3 (GGA3) plays a key role in the trafficking of BACE1 to lysosomes, where it is normally degraded. GGA3 depletion results in BACE1 stabilization both in vitro and in vivo. Moreover, levels of GGA3 are reduced and inversely related to BACE1 levels in post-mortem brains of AD patients. In order to assess the effect of GGA3 deletion on AD-like phenotypes, we crossed GGA3 -/- mice with 5XFAD mice. BACE1-mediated processing of APP and the cell adhesion molecule L1 like protein (CHL1) was measured as well as levels of Aβ42 and amyloid burden. In 5XFAD mice, we found that hippocampal and cortical levels of GGA3 decreased while BACE1 levels increased with age, similar to what is observed in human AD brains. GGA3 deletion prevented age-dependent elevation of BACE1 in GGA3KO;5XFAD mice. We also found that GGA3 deletion resulted in increased hippocampal levels of Aβ42 and amyloid burden in 5XFAD mice at 12 months of age. While levels of BACE1 did not change with age and gender in GGAKO;5XFAD mice, amyloid precursor protein (APP) levels increased with age and were higher in female mice. Moreover, elevation of APP was associated with a decreased BACE1-mediated processing of CHL1 not only in 12 months old 5XFAD mice but also in human brains from subjects affected by Down syndrome, most likely due to substrate competition. This study demonstrates that GGA3 depletion is a leading candidate mechanism underlying elevation of BACE1 in AD. Furthermore, our findings suggest that BACE1 inhibition could exacerbate mechanism-based side effects in conditions associated with APP elevation (e.g. Down syndrome) owing to impairment of BACE1-mediated processing of CHL1

  1. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

    Science.gov (United States)

    Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

    2010-07-01

    PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

  2. Improving Permafrost Hydrology Prediction Through Data-Model Integration

    Science.gov (United States)

    Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

    2017-12-01

    The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

  3. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  4. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  5. Can biomechanical variables predict improvement in crouch gait?

    Science.gov (United States)

    Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

    2011-01-01

    Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

  6. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit

    2015-04-16

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  7. Improving Saliency Models by Predicting Human Fixation Patches

    KAUST Repository

    Dubey, Rachit; Dave, Akshat; Ghanem, Bernard

    2015-01-01

    There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.

  8. Improved hybrid optimization algorithm for 3D protein structure prediction.

    Science.gov (United States)

    Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

    2014-07-01

    A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

  9. Pigment organization effects on energy transfer and Chl a emission imaged in the diatoms C. meneghiniana and P. tricornutum in vivo: a confocal laser scanning fluorescence (CLSF) microscopy and spectroscopy study.

    Science.gov (United States)

    Premvardhan, Lavanya; Réfrégiers, Matthieu; Büchel, Claudia

    2013-09-26

    The (auto)fluorescence from three diatom strains, Cyclotella meneghiniana (Cm), Phaeodactylum tricornutum 1a (Pt1a), and Phaeodactylum UTex (PtUTex), has been imaged in vivo to submicrometer resolution using confocal laser scanning fluorescence (CLSF) microscopy. The diatoms are excited at 473 and 532 nm, energy primarily absorbed by the carotenoid fucoxanthin (Fx) found within the fucoxanthin chlorophyll a/c proteins (FCPs). On the basis of the fluorescence spectra measured in each image voxel, we obtain information about the spatial and energetic distribution of the terminal Chl a emitters, localized in the FCPs and the reaction centers of the PSII protein complexes, and the nature and location of the primary absorbers that are linked to these emitters; 532 nm excites the highly efficient Fx(red) light harvesters, and lesser amounts of Fx(green)s, that are enriched in some FCPs and preferentially transfer energy to PSII, compared to 473 nm, which excites almost equal amounts of all three previously identified sets of Fx--Fx(red), Fx(green) and Fx(blue)--as well as Chl c. The heterogeneous Chl a emission observed from the (C)LSF images indicates that the different Fx's serve different final emitters in P. tricornutum and suggest, at least in C. meneghiniana , a localization of FCPs with relatively greater Fx(red) content at the chloroplast edges, but with overall higher FCP concentration in the interior of the plastid. To better understand our results, the concentration-dependent ensemble-averaged diatom solution spectra are compared to the (auto)fluorescence spectra of individual diatoms, which indicate that pigment packing effects at an intracellular level do affect the diatoms' spectral properties, in particular, concerning a 710 nm emission band apparent under stress conditions. A species-specific response of the spectral signature to the incident light is also discussed in terms of the presence of a silica shell in Cm but not in Pt1a nor PtUTex.

  10. Innovative predictive maintenance concepts to improve life cycle management

    NARCIS (Netherlands)

    Tinga, Tiedo

    2014-01-01

    For naval systems with typically long service lives, high sustainment costs and strict availability requirements, an effective and efficient life cycle management process is very important. In this paper four approaches are discussed to improve that process: physics of failure based predictive

  11. Solar radio proxies for improved satellite orbit prediction

    Science.gov (United States)

    Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

    2017-12-01

    Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

  12. Solar radio proxies for improved satellite orbit prediction

    Directory of Open Access Journals (Sweden)

    Yaya Philippe

    2017-01-01

    Full Text Available Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model performs better with (past and predicted values of the 30 cm radio flux than with the 10.7 flux.

  13. A two-stage approach for improved prediction of residue contact maps

    Directory of Open Access Journals (Sweden)

    Pollastri Gianluca

    2006-03-01

    Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

  14. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  15. Healthy, wealthy, and wise: retirement planning predicts employee health improvements.

    Science.gov (United States)

    Gubler, Timothy; Pierce, Lamar

    2014-09-01

    Are poor physical and financial health driven by the same underlying psychological factors? We found that the decision to contribute to a 401(k) retirement plan predicted whether an individual acted to correct poor physical-health indicators revealed during an employer-sponsored health examination. Using this examination as a quasi-exogenous shock to employees' personal-health knowledge, we examined which employees were more likely to improve their health, controlling for differences in initial health, demographics, job type, and income. We found that existing retirement-contribution patterns and future health improvements were highly correlated. Employees who saved for the future by contributing to a 401(k) showed improvements in their abnormal blood-test results and health behaviors approximately 27% more often than noncontributors did. These findings are consistent with an underlying individual time-discounting trait that is both difficult to change and domain interdependent, and that predicts long-term individual behaviors in multiple dimensions. © The Author(s) 2014.

  16. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

    Science.gov (United States)

    Adhikari, Badri; Hou, Jie; Cheng, Jianlin

    2018-05-01

    Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. chengji@missouri.edu. Supplementary data are available at Bioinformatics online.

  17. Genomic Prediction of Manganese Efficiency in Winter Barley

    Directory of Open Access Journals (Sweden)

    Florian Leplat

    2016-07-01

    Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

  18. Improved prediction of signal peptides: SignalP 3.0

    DEFF Research Database (Denmark)

    Bendtsen, Jannick Dyrløv; Nielsen, Henrik; von Heijne, G.

    2004-01-01

    We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the ...

  19. Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-12-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

  20. TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

    Science.gov (United States)

    Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

    2017-09-01

    α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD PHP, MySQL and Apache, with all major browsers supported.

  1. Suspended Matter, Chl-a, CDOM, Grain Sizes, and Optical Properties in the Arctic Fjord-Type Estuary, Kangerlussuaq, West Greenland During Summer

    DEFF Research Database (Denmark)

    Lund-Hansen, L. C.; Andersen, T. J.; Nielsen, Morten Holtegaard

    2010-01-01

    Optical constituents as suspended particulate matter (SPM), chlorophyll (Chl-a), colored dissolved organic matter (CDOM), and grain sizes were obtained on a transect in the arctic fjord-type estuary Kangerlussuaq (66A degrees) in August 2007 along with optical properties. These comprised diffuse...... water outlet. Values of optical constituents and properties decreased with distance from the melt water outlet to a more or less constant level in central and outer part of the estuary. There was a strong correlation between inorganic suspended matter (SPMI) and diffuse attenuation coefficient K (d...... from the very high turbid melt water outlet to clear marine waters. Results showed a strong spatial variation with high values as for suspended matter concentrations, CDOM, diffuse attenuation coefficient K (d)(PAR), particle beam attenuation coefficients (c (p)), and reflectance R(-0, PAR) at the melt...

  2. Pan-Arctic sea ice-algal chl a biomass and suitable habitat are largely underestimated for multiyear ice.

    Science.gov (United States)

    Lange, Benjamin A; Flores, Hauke; Michel, Christine; Beckers, Justin F; Bublitz, Anne; Casey, John Alec; Castellani, Giulia; Hatam, Ido; Reppchen, Anke; Rudolph, Svenja A; Haas, Christian

    2017-11-01

    There is mounting evidence that multiyear ice (MYI) is a unique component of the Arctic Ocean and may play a more important ecological role than previously assumed. This study improves our understanding of the potential of MYI as a suitable habitat for sea ice algae on a pan-Arctic scale. We sampled sea ice cores from MYI and first-year sea ice (FYI) within the Lincoln Sea during four consecutive spring seasons. This included four MYI hummocks with a mean chl a biomass of 2.0 mg/m 2 , a value significantly higher than FYI and MYI refrozen ponds. Our results support the hypothesis that MYI hummocks can host substantial ice-algal biomass and represent a reliable ice-algal habitat due to the (quasi-) permanent low-snow surface of these features. We identified an ice-algal habitat threshold value for calculated light transmittance of 0.014%. Ice classes and coverage of suitable ice-algal habitat were determined from snow and ice surveys. These ice classes and associated coverage of suitable habitat were applied to pan-Arctic CryoSat-2 snow and ice thickness data products. This habitat classification accounted for the variability of the snow and ice properties and showed an areal coverage of suitable ice-algal habitat within the MYI-covered region of 0.54 million km 2 (8.5% of total ice area). This is 27 times greater than the areal coverage of 0.02 million km 2 (0.3% of total ice area) determined using the conventional block-model classification, which assigns single-parameter values to each grid cell and does not account for subgrid cell variability. This emphasizes the importance of accounting for variable snow and ice conditions in all sea ice studies. Furthermore, our results indicate the loss of MYI will also mean the loss of reliable ice-algal habitat during spring when food is sparse and many organisms depend on ice-algae. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

  3. Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

    Science.gov (United States)

    Ickes, Jacob C.

    Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

  4. Improved prediction of aerodynamic noise from wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Guidati, G.; Bareiss, R.; Wagner, S. [Univ. of Stuttgart, Inst. of Aerodynamics and Gasdynamics, Stuttgart (Germany)

    1997-12-31

    This paper focuses on an improved prediction model for inflow-turbulence noise which takes the true airfoil shape into account. Predictions are compared to the results of acoustic measurements on three 2D-models of 0.25 m chord. Two of the models have NACA-636xx airfoils of 12% and 18% relative thickness. The third airfoil was acoustically optimized by using the new prediction model. In the experiments the turbulence intensity of the flow was strongly increased by mounting a grid with 60 mm wide meshes and 12 mm thick rods onto the tunnel exhaust nozzle. The sound radiated from the airfoil was distinguished by the tunnel background noise by using an acoustic antenna consisting of a cross array of 36 microphones in total. An application of a standard beam-forming algorithm allows to determine how much noise is radiated from different parts of the models. This procedure normally results in a peak at the leading and trailing edge of the airfoil. The strength of the leading-edge peak is taken as the source strength for inflow-turbulence noise. (LN) 14 refs.

  5. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  6. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

    2018-01-01

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  7. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.

    2018-03-20

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  8. Time series from hyperion to track productivity in pivot agriculture in saudi arabia

    KAUST Repository

    Houborg, Rasmus

    2017-12-13

    The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll (Chlc), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. Chlc is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of Chlc are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.

  9. Time series from hyperion to track productivity in pivot agriculture in saudi arabia

    KAUST Repository

    Houborg, Rasmus; McCabe, Matthew; Angel, Yoseline; Middleton, Elizabeth M.

    2017-01-01

    The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll (Chlc), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. Chlc is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of Chlc are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.

  10. Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

    KAUST Repository

    Kalaroni, S.

    2016-04-12

    A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

  11. Data assimilation of depth-distributed satellite chlorophyll-α in two Mediterranean contrasting sites

    KAUST Repository

    Kalaroni, S.; Tsiaras, K.; Petihakis, G.; Hoteit, Ibrahim; Economou-Amilli, A.; G.Triantafyllou

    2016-01-01

    A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the Singular Evolutive Interpolated Kalman (SEIK) filter, the ensemble variant of the Singular Evolutive Extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in-situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

  12. Photosynthesis, light use efficiency, and yield of reduced-chlorophyll soybean mutants in field conditions

    Science.gov (United States)

    Reducing chlorophyll (chl) content may improve the conversion efficiency of absorbed radiation into biomass (ec) and therefore yield in dense monoculture crops by improving light penetration and distribution within the canopy. Modeling suggests that reducing chl content may also reduce leaf temperat...

  13. Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

    Science.gov (United States)

    Woelmer, Whitney; Kao, Yu-Chun; Bunnell, David B.; Deines, Andrew M.; Bennion, David; Rogers, Mark W.; Brooks, Colin N.; Sayers, Michael J.; Banach, David M.; Grimm, Amanda G.; Shuchman, Robert A.

    2016-01-01

    Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables.

  14. Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring.

    Science.gov (United States)

    Boucher, Jonah; Weathers, Kathleen C; Norouzi, Hamid; Steele, Bethel

    2018-06-01

    Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll a (chl a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl a data from Maine and New Hampshire, USA lakes and remotely sensed chl a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on 11 scenes from 2013 to 2015 covering 192 lakes. The best performing algorithm across data from both states had a 0.16 correlation coefficient (R 2 ) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R 2 of 0.25 when data from Maine only were used. The strength of the correlation varied with the specificity of the time window in relation to the in-situ sampling date, explaining up to 27% of the variation in the data across several scenes. Two previously published algorithms using Landsat 8's Bands 1-4 were best correlated with chl a, and for particular late-summer scenes, they accounted for up to 69% of the variation in in-situ measurements. A sensitivity analysis revealed that a longer time difference between in situ measurements and the satellite image increased uncertainty in the models, and an effect of the time of year on several indices was demonstrated. A regional model based on the best performing remote sensing algorithm was developed and was validated using independent in situ measurements and satellite

  15. Modelling ocean-colour-derived chlorophyll a

    Directory of Open Access Journals (Sweden)

    S. Dutkiewicz

    2018-01-01

    Full Text Available This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called derived Chl a in this paper. We compare the derived Chl a to the actual model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that region-specific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter. The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation

  16. Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

    International Nuclear Information System (INIS)

    Wurzbach, Richard N.

    2002-01-01

    On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

  17. Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS

    Directory of Open Access Journals (Sweden)

    Tara Blakey

    2016-10-01

    Full Text Available This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning.

  18. Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

    Science.gov (United States)

    Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

    2017-11-15

    Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  19. Critical health literacy in American deaf college students.

    Science.gov (United States)

    Kushalnagar, Poorna; Ryan, Claire; Smith, Scott; Kushalnagar, Raja

    2017-05-24

    This study investigates the relationship between critical health literacy (CHL) and discussion of health information among college deaf students who use American Sign Language. CHL is crucial in making appropriate health-related decisions for oneself and aiding others in making good health-choices. Research on general youth population shows that frequent health-related discussions with both friends and family is associated with higher health literacy. However, for our sample of deaf college-aged students who might have had less access to communication at home, we hypothesize that health-related discussions with same-age peers may be more important for critical health literacy. We asked two questions to assess the frequency of health-related discussions with friends and families: "How often do you discuss health-related information with your friends" and "How often do you discuss your family medical history with your family?". Participants rated their experience on a scale from 1-5 (1=never, 5=always). To assess CHL, 38 deaf and 38 hearing participants were shown a short scenario that showed a woman confiding in her friend after finding a lump in her breast. Participants were then asked what the friend should say. Responses were scored by a team of 3 raters using a CHL rubric. As predicted, results showed a strong relationship between discussion of health-related information with friends and CHL in both deaf and hearing samples. Discussion with family was linked to CHL only for hearing participants, but not deaf participants in our study. These findings underscore the importance of socializing with health-literate, accessible peers to improve the health literacy and health outcomes of all deaf people. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  20. Improved prediction of genetic predisposition to psychiatric disorders using genomic feature best linear unbiased prediction models

    DEFF Research Database (Denmark)

    Rohde, Palle Duun; Demontis, Ditte; Børglum, Anders

    is enriched for causal variants. Here we apply the GFBLUP model to a small schizophrenia case-control study to test the promise of this model on psychiatric disorders, and hypothesize that the performance will be increased when applying the model to a larger ADHD case-control study if the genomic feature...... contains the causal variants. Materials and Methods: The schizophrenia study consisted of 882 controls and 888 schizophrenia cases genotyped for 520,000 SNPs. The ADHD study contained 25,954 controls and 16,663 ADHD cases with 8,4 million imputed genotypes. Results: The predictive ability for schizophrenia.......6% for the null model). Conclusion: The improvement in predictive ability for schizophrenia was marginal, however, greater improvement is expected for the larger ADHD data....

  1. Incorporating Scale-Dependent Fracture Stiffness for Improved Reservoir Performance Prediction

    Science.gov (United States)

    Crawford, B. R.; Tsenn, M. C.; Homburg, J. M.; Stehle, R. C.; Freysteinson, J. A.; Reese, W. C.

    2017-12-01

    We present a novel technique for predicting dynamic fracture network response to production-driven changes in effective stress, with the potential for optimizing depletion planning and improving recovery prediction in stress-sensitive naturally fractured reservoirs. A key component of the method involves laboratory geomechanics testing of single fractures in order to develop a unique scaling relationship between fracture normal stiffness and initial mechanical aperture. Details of the workflow are as follows: tensile, opening mode fractures are created in a variety of low matrix permeability rocks with initial, unstressed apertures in the micrometer to millimeter range, as determined from image analyses of X-ray CT scans; subsequent hydrostatic compression of these fractured samples with synchronous radial strain and flow measurement indicates that both mechanical and hydraulic aperture reduction varies linearly with the natural logarithm of effective normal stress; these stress-sensitive single-fracture laboratory observations are then upscaled to networks with fracture populations displaying frequency-length and length-aperture scaling laws commonly exhibited by natural fracture arrays; functional relationships between reservoir pressure reduction and fracture network porosity, compressibility and directional permeabilities as generated by such discrete fracture network modeling are then exported to the reservoir simulator for improved naturally fractured reservoir performance prediction.

  2. Improving the accuracy of protein secondary structure prediction using structural alignment

    Directory of Open Access Journals (Sweden)

    Gallin Warren J

    2006-06-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

  3. Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

    Science.gov (United States)

    2017-12-01

    19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

  4. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  5. Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems

    Directory of Open Access Journals (Sweden)

    Ali Azadeh

    2015-06-01

    Full Text Available Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2. Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS. Mean absolute percentage errors (MAPEs resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

  6. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  7. Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia.

    Science.gov (United States)

    Menéndez, R; Martínez, R; Reyes, S; Mensa, J; Filella, X; Marcos, M A; Martínez, A; Esquinas, C; Ramirez, P; Torres, A

    2009-07-01

    Prognostic scales provide a useful tool to predict mortality in community-acquired pneumonia (CAP). However, the inflammatory response of the host, crucial in resolution and outcome, is not included in the prognostic scales. The aim of this study was to investigate whether information about the initial inflammatory cytokine profile and markers increases the accuracy of prognostic scales to predict 30-day mortality. To this aim, a prospective cohort study in two tertiary care hospitals was designed. Procalcitonin (PCT), C-reactive protein (CRP) and the systemic cytokines tumour necrosis factor alpha (TNFalpha) and interleukins IL6, IL8 and IL10 were measured at admission. Initial severity was assessed by PSI (Pneumonia Severity Index), CURB65 (Confusion, Urea nitrogen, Respiratory rate, Blood pressure, > or = 65 years of age) and CRB65 (Confusion, Respiratory rate, Blood pressure, > or = 65 years of age) scales. A total of 453 hospitalised CAP patients were included. The 36 patients who died (7.8%) had significantly increased levels of IL6, IL8, PCT and CRP. In regression logistic analyses, high levels of CRP and IL6 showed an independent predictive value for predicting 30-day mortality, after adjustment for prognostic scales. Adding CRP to PSI significantly increased the area under the receiver operating characteristic curve (AUC) from 0.80 to 0.85, that of CURB65 from 0.82 to 0.85 and that of CRB65 from 0.79 to 0.85. Adding IL6 or PCT values to CRP did not significantly increase the AUC of any scale. When using two scales (PSI and CURB65/CRB65) and CRP simultaneously the AUC was 0.88. Adding CRP levels to PSI, CURB65 and CRB65 scales improves the 30-day mortality prediction. The highest predictive value is reached with a combination of two scales and CRP. Further validation of that improvement is needed.

  8. Improving consensus contact prediction via server correlation reduction.

    Science.gov (United States)

    Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

    2009-05-06

    Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  9. Improving consensus contact prediction via server correlation reduction

    Directory of Open Access Journals (Sweden)

    Xu Jinbo

    2009-05-01

    Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  10. Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information

    Science.gov (United States)

    Aghakouchak, Amir; Tourian, Mohammad J.

    2015-04-01

    Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014

  11. Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery.

    Science.gov (United States)

    Chen, Li; Tan, Chih-Hung; Kao, Shuh-Ji; Wang, Tai-Sheng

    2008-01-01

    Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.

  12. Theoretical Characterization of the Spectral Density of the Water-Soluble Chlorophyll-Binding Protein from Combined Quantum Mechanics/Molecular Mechanics Molecular Dynamics Simulations.

    Science.gov (United States)

    Rosnik, Andreana M; Curutchet, Carles

    2015-12-08

    Over the past decade, both experimentalists and theorists have worked to develop methods to describe pigment-protein coupling in photosynthetic light-harvesting complexes in order to understand the molecular basis of quantum coherence effects observed in photosynthesis. Here we present an improved strategy based on the combination of quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations and excited-state calculations to predict the spectral density of electronic-vibrational coupling. We study the water-soluble chlorophyll-binding protein (WSCP) reconstituted with Chl a or Chl b pigments as the system of interest and compare our work with data obtained by Pieper and co-workers from differential fluorescence line-narrowing spectra (Pieper et al. J. Phys. Chem. B 2011, 115 (14), 4042-4052). Our results demonstrate that the use of QM/MM MD simulations where the nuclear positions are still propagated at the classical level leads to a striking improvement of the predicted spectral densities in the middle- and high-frequency regions, where they nearly reach quantitative accuracy. This demonstrates that the so-called "geometry mismatch" problem related to the use of low-quality structures in QM calculations, not the quantum features of pigments high-frequency motions, causes the failure of previous studies relying on similar protocols. Thus, this work paves the way toward quantitative predictions of pigment-protein coupling and the comprehension of quantum coherence effects in photosynthesis.

  13. Does microtia predict severity of temporal bone CT abnormalities in children with persistent conductive hearing loss?

    Science.gov (United States)

    Tekes, Aylin; Ishman, Stacey L; Baugher, Katherine M; Brown, David J; Lin, Sandra Y; Tunkel, David E; Unalp-Arida, Aynur; Huisman, Thierry A G M

    2013-07-01

    This study aimed to determine the spectrum of temporal bone computed tomography (CT) abnormalities in children with conductive hearing loss (CHL) with and without microtia. From 1993 to 2008, a total of 3396 pediatric records including CHL were reviewed at our institution and revealed 180 cases of persistent CHL, 46 of whom had diagnostic temporal bone CT examinations. All of these examinations were systematically reviewed by two pediatric neuroradiologists, working in consensus, who had 5 and 18 years, respectively, of dedicated pediatric neuroradiology experience. Of the 46 children, 16 were boys and 30 were girls (age: 0.2-16 years; mean: 5 years). Also, 21 (46%) children had microtia and 25 (54%) children did not, as determined by clinical evaluation. External auditory canal atresia/stenosis (EAC-A/S) was the most common anomaly in both microtia and non-microtia groups. Two or more anomalies were observed in 18/21 children with microtia. The frequency of EAC-A/S was greater in children with microtia versus those without it (86% versus 32%, respectively; P = 0.0003). Syndromic diagnoses were also significantly more frequently made in children with microtia versus those without microtia (76% versus 20%, respectively; P = 0.0001). Temporal bone CT scans were normal in 10 children (22%) with persistent CHL. Microtia is an important finding in children with CHL. EAC and middle ear/ossicle anomalies were significantly more frequently seen in children with microtia, and multiple anomalies and bilateral microtia were more common in children with syndromic associations. These findings highlight the importance of understanding the embryological development of the temporal bone. The presence of one anomaly should raise suspicion of the possibility of other anomalies, especially in the setting of microtia. Bilateral microtia and multiple anomalies should also raise suspicion of genetic syndromes. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

  14. Advanced Materials Test Methods for Improved Life Prediction of Turbine Engine Components

    National Research Council Canada - National Science Library

    Stubbs, Jack

    2000-01-01

    Phase I final report developed under SBIR contract for Topic # AF00-149, "Durability of Turbine Engine Materials/Advanced Material Test Methods for Improved Use Prediction of Turbine Engine Components...

  15. Improved model predictive control for high voltage quality in microgrid applications

    DEFF Research Database (Denmark)

    Dragicevic, T.; Al hasheem, Mohamed; Lu, M.

    2017-01-01

    This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based...

  16. Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data

    Directory of Open Access Journals (Sweden)

    Francisco Flores-de-Santiago

    2016-03-01

    Full Text Available The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy between reflectance (400–1000 nm and leaf pigment contents (chlorophyll-a (chl-a, chlorophyll-b (chl-b, total carotenoids (tcar, chlorophyll a/b ratio in three mangrove species (Avicennia germinans (A. germinans, Laguncularia racemosa (L. racemosa, and Rhizophora mangle (R. mangle according to their condition (stressed vs. healthy. Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm and red-edge (710 nm wavelengths (r = 0.8 and 0.9, respectively for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1 index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data

  17. Data Prediction for Public Events in Professional Domains Based on Improved RNN- LSTM

    Science.gov (United States)

    Song, Bonan; Fan, Chunxiao; Wu, Yuexin; Sun, Juanjuan

    2018-02-01

    The traditional data services of prediction for emergency or non-periodic events usually cannot generate satisfying result or fulfill the correct prediction purpose. However, these events are influenced by external causes, which mean certain a priori information of these events generally can be collected through the Internet. This paper studied the above problems and proposed an improved model—LSTM (Long Short-term Memory) dynamic prediction and a priori information sequence generation model by combining RNN-LSTM and public events a priori information. In prediction tasks, the model is qualified for determining trends, and its accuracy also is validated. This model generates a better performance and prediction results than the previous one. Using a priori information can increase the accuracy of prediction; LSTM can better adapt to the changes of time sequence; LSTM can be widely applied to the same type of prediction tasks, and other prediction tasks related to time sequence.

  18. Improving local clustering based top-L link prediction methods via asymmetric link clustering information

    Science.gov (United States)

    Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

    2018-02-01

    Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

  19. Methods to improve genomic prediction and GWAS using combined Holstein populations

    DEFF Research Database (Denmark)

    Li, Xiujin

    The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

  20. Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

    Science.gov (United States)

    Zhang, Hua; Kurgan, Lukasz

    2014-12-01

    Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

  1. Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.

    Directory of Open Access Journals (Sweden)

    Kais Gadhoumi

    Full Text Available Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5% and specificity (25.1% of total time under warning for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.

  2. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    Science.gov (United States)

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  3. Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory.

    Science.gov (United States)

    Panaite, Vanessa; Hindash, Alexandra Cowden; Bylsma, Lauren M; Small, Brent J; Salomon, Kristen; Rottenberg, Jonathan

    2016-01-01

    Respiratory sinus arrhythmia (RSA) reactivity, an index of cardiac vagal tone, has been linked to self-regulation and the severity and course of depression (Rottenberg, 2007). Although initial data supports the proposition that RSA withdrawal during a sad film is a specific predictor of depression course (Fraguas, 2007; Rottenberg, 2005), the robustness and specificity of this finding are unclear. To provide a stronger test, RSA reactivity to three emotion films (happy, sad, fear) and to a more robust stressor, a speech task, were examined in currently depressed individuals (n=37), who were assessed for their degree of symptomatic improvement over 30weeks. Robust RSA reactivity to the sad film uniquely predicted overall symptom improvement over 30weeks. RSA reactivity to both sad and stressful stimuli predicted the speed and maintenance of symptomatic improvement. The current analyses provide the most robust support to date that RSA withdrawal to sad stimuli (but not stressful) has specificity in predicting the overall symptomatic improvement. In contrast, RSA reactivity to negative stimuli (both sad and stressful) predicted the trajectory of depression course. Patients' engagement with sad stimuli may be an important sign to attend to in therapeutic settings. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Catchment coevolution: A useful framework for improving predictions of hydrological change?

    Science.gov (United States)

    Troch, Peter A.

    2017-04-01

    The notion that landscape features have co-evolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this presentation we present a general framework of catchment coevolution that could improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building

  5. The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

    Science.gov (United States)

    Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

    2017-01-01

    Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2  = 0.18). The use of routinely available pre-operative patient factors improves the prediction of operative

  6. Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

    Science.gov (United States)

    W. Brad Smith

    1983-01-01

    A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

  7. LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

    DEFF Research Database (Denmark)

    Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

    2012-01-01

    Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

  8. Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.

    Science.gov (United States)

    Raia, Valentina; Schilling, Marcel; Böhm, Martin; Hahn, Bettina; Kowarsch, Andreas; Raue, Andreas; Sticht, Carsten; Bohl, Sebastian; Saile, Maria; Möller, Peter; Gretz, Norbert; Timmer, Jens; Theis, Fabian; Lehmann, Wolf-Dieter; Lichter, Peter; Klingmüller, Ursula

    2011-02-01

    Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

  9. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Science.gov (United States)

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  10. Predictive modelling of interventions to improve iodine intake in New Zealand.

    Science.gov (United States)

    Schiess, Sonja; Cressey, Peter J; Thomson, Barbara M

    2012-10-01

    The potential effects of four interventions to improve iodine intakes of six New Zealand population groups are assessed. A model was developed to estimate iodine intake when (i) bread is manufactured with or without iodized salt, (ii) recommended foods are consumed to augment iodine intake, (iii) iodine supplementation as recommended for pregnant women is taken and (iv) the level of iodization for use in bread manufacture is doubled from 25-65 mg to 100 mg iodine/kg salt. New Zealanders have low and decreasing iodine intakes and low iodine status. Predictive modelling is a useful tool to assess the likely impact, and potential risk, of nutrition interventions. Food consumption information was sourced from 24 h diet recall records for 4576 New Zealanders aged over 5 years. Most consumers (73-100 %) are predicted to achieve an adequate iodine intake when salt iodized at 25-65 mg iodine/kg salt is used in bread manufacture, except in pregnant females of whom 37 % are likely to meet the estimated average requirement. Current dietary advice to achieve estimated average requirements is challenging for some consumers. Pregnant women are predicted to achieve adequate but not excessive iodine intakes when 150 μg of supplemental iodine is taken daily, assuming iodized salt in bread. The manufacture of bread with iodized salt and supplemental iodine for pregnant women are predicted to be effective interventions to lift iodine intakes in New Zealand. Current estimations of iodine intake will be improved with information on discretionary salt and supplemental iodine usage.

  11. Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

    Science.gov (United States)

    Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.

    2017-11-01

    The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

  12. Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

    Science.gov (United States)

    Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

    2015-12-01

    Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

  13. Factors predicting visual improvement post pars plana vitrectomy for proliferative diabetic retinopathy

    Directory of Open Access Journals (Sweden)

    Evelyn Tai Li Min

    2017-08-01

    Full Text Available AIM: To identify factors predicting visual improvement post vitrectomy for sequelae of proliferative diabetic retinopathy(PDR.METHODS: This was a retrospective analysis of pars plana vitrectomy indicated for sequelae of PDR from Jan. to Dec. 2014 in Hospital Sultanah Bahiyah, Alor Star, Kedah, Malaysia. Data collected included patient demographics, baseline visual acuity(VAand post-operative logMAR best corrected VA at 1y. Data analysis was performed with IBM SPSS Statistics Version 22.0. RESULTS: A total of 103 patients were included. The mean age was 51.2y. On multivariable analysis, each pre-operative positive deviation of 1 logMAR from a baseline VA of 0 logMAR was associated with a post-operative improvement of 0.859 logMAR(P0.001. Likewise, an attached macula pre-operatively was associated with a 0.374(P=0.003logMAR improvement post vitrectomy. Absence of iris neovascularisation and absence of post-operative complications were associated with a post vitrectomy improvement in logMAR by 1.126(P=0.001and 0.377(P=0.005respectively. Absence of long-acting intraocular tamponade was associated with a 0.302(P=0.010improvement of logMAR post vitrectomy.CONCLUSION: Factors associated with visual improvement after vitrectomy are poor pre-operative VA, an attached macula, absence of iris neovascularisation, absence of post-operative complications and abstaining from use of long-acting intraocular tamponade. A thorough understanding of the factors predicting visual improvement will facilitate decision-making in vitreoretinal surgery.

  14. Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With Conventional Risk Scores.

    Science.gov (United States)

    Schoenenberger, Andreas W; Moser, André; Bertschi, Dominic; Wenaweser, Peter; Windecker, Stephan; Carrel, Thierry; Stuck, Andreas E; Stortecky, Stefan

    2018-02-26

    This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores. European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR). This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]). All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction. This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement. Copyright © 2018 American College of Cardiology Foundation

  15. An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

    Directory of Open Access Journals (Sweden)

    Jin Xin

    2015-01-01

    Full Text Available To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.

  16. A study on improvement of analytical prediction model for spacer grid pressure loss coefficients

    International Nuclear Information System (INIS)

    Lim, Jonh Seon

    2002-02-01

    Nuclear fuel assemblies used in the nuclear power plants consist of the nuclear fuel rods, the control rod guide tubes, an instrument guide tube, spacer grids,a bottom nozzle, a top nozzle. The spacer grid is the most important component of the fuel assembly components for thermal hydraulic and mechanical design and analyses. The spacer grids fixed with the guide tubes support the fuel rods and have the very important role to activate thermal energy transfer by the coolant mixing caused to the turbulent flow and crossflow in the subchannels. In this paper, the analytical spacer grid pressure loss prediction model has been studied and improved by considering the test section wall to spacer grid gap pressure loss independently and applying the appropriate friction drag coefficient to predict pressure loss more accurately at the low Reynolds number region. The improved analytical model has been verified based on the hydraulic pressure drop test results for the spacer grids of three types with 5x5, 16x16, 17x17 arrays, respectively. The pressure loss coefficients predicted by the improved analytical model are coincident with those test results within ±12%. This result shows that the improved analytical model can be used for research and design change of the nuclear fuel assembly

  17. Effects of internal phosphorus loadings and food-web structure on the recovery of a deep lake from eutrophication

    Science.gov (United States)

    Lepori, Fabio; Roberts, James J.

    2017-01-01

    We used monitoring data from Lake Lugano (Switzerland and Italy) to assess key ecosystem responses to three decades of nutrient management (1983–2014). We investigated whether reductions in external phosphorus loadings (Lext) caused declines in lake phosphorus concentrations (P) and phytoplankton biomass (Chl a), as assumed by the predictive models that underpinned the management plan. Additionally, we examined the hypothesis that deep lakes respond quickly to Lext reductions. During the study period, nutrient management reduced Lext by approximately a half. However, the effects of such reduction on P and Chl a were complex. Far from the scenarios predicted by classic nutrient-management approaches, the responses of P and Chl a did not only reflect changes in Lext, but also variation in internal P loadings (Lint) and food-web structure. In turn, Lint varied depending on basin morphometry and climatic effects, whereas food-web structure varied due to apparently stochastic events of colonization and near-extinction of key species. Our results highlight the complexity of the trajectory of deep-lake ecosystems undergoing nutrient management. From an applied standpoint, they also suggest that [i] the recovery of warm monomictic lakes may be slower than expected due to the development of Lint, and that [ii] classic P and Chl a models based on Lext may be useful in nutrient management programs only if their predictions are used as starting points within adaptive frameworks.

  18. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  19. Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage

    Directory of Open Access Journals (Sweden)

    Douglas Halamay

    2014-09-01

    Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

  20. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  1. Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

    Science.gov (United States)

    Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

    2013-05-01

    Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

  2. Eddy-induced cross-shelf export of high Chl-a coastal waters in the SE Bay of Biscay

    KAUST Repository

    Rubio, Anna

    2017-12-08

    Different remote sensing data were combined to characterise a winter anticyclonic eddy in the southeastern Bay of Biscay and to infer its effects on cross-shelf exchanges, in a period when typical along shelf-slope currents depict a cyclonic pattern. While the joint analysis of available satellite data (infrared, visible and altimetry) permitted the characterisation and tracking of the anticyclone properties and path, data from a coastal high-frequency radar system enabled a quantitative analysis of the surface cross-shelf transports associated with this anticyclone. The warm core anticyclone had a diameter of around 50km, maximum azimuthal velocities near 50cms−1 and a relative vorticity of up to −0.45f. The eddy generation occurred after the relaxation of a cyclonic wind-driven current regime over the shelf-slope; then, the eddy remained stationary for several weeks until it started to drift northwards along the shelf break. The surface signature of this eddy was observed by means of high-frequency radar data for 20 consecutive days, providing a unique opportunity to characterise and quantify, from a Lagrangian perspective, the associated transport and its effect on the Chl-a surface distribution. We observed the presence of mesoscale structures with similar characteristics in the area during different winters within the period 2011–2014. Our results suggest that the eddy-induced recurrent cross-shelf export is an effective mechanism for the expansion of coastal productive waters into the adjacent oligotrophic ocean basin.

  3. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Science.gov (United States)

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  4. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Directory of Open Access Journals (Sweden)

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  5. Reliable B cell epitope predictions: impacts of method development and improved benchmarking

    DEFF Research Database (Denmark)

    Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole

    2012-01-01

    biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping...... evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version...

  6. A national-scale model of linear features improves predictions of farmland biodiversity.

    Science.gov (United States)

    Sullivan, Martin J P; Pearce-Higgins, James W; Newson, Stuart E; Scholefield, Paul; Brereton, Tom; Oliver, Tom H

    2017-12-01

    Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications . This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

  7. Improved nucleic acid descriptors for siRNA efficacy prediction.

    Science.gov (United States)

    Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

    2013-02-01

    Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

  8. Utilizing multiple scale models to improve predictions of extra-axial hemorrhage in the immature piglet.

    Science.gov (United States)

    Scott, Gregory G; Margulies, Susan S; Coats, Brittany

    2016-10-01

    Traumatic brain injury (TBI) is a leading cause of death and disability in the USA. To help understand and better predict TBI, researchers have developed complex finite element (FE) models of the head which incorporate many biological structures such as scalp, skull, meninges, brain (with gray/white matter differentiation), and vasculature. However, most models drastically simplify the membranes and substructures between the pia and arachnoid membranes. We hypothesize that substructures in the pia-arachnoid complex (PAC) contribute substantially to brain deformation following head rotation, and that when included in FE models accuracy of extra-axial hemorrhage prediction improves. To test these hypotheses, microscale FE models of the PAC were developed to span the variability of PAC substructure anatomy and regional density. The constitutive response of these models were then integrated into an existing macroscale FE model of the immature piglet brain to identify changes in cortical stress distribution and predictions of extra-axial hemorrhage (EAH). Incorporating regional variability of PAC substructures substantially altered the distribution of principal stress on the cortical surface of the brain compared to a uniform representation of the PAC. Simulations of 24 non-impact rapid head rotations in an immature piglet animal model resulted in improved accuracy of EAH prediction (to 94 % sensitivity, 100 % specificity), as well as a high accuracy in regional hemorrhage prediction (to 82-100 % sensitivity, 100 % specificity). We conclude that including a biofidelic PAC substructure variability in FE models of the head is essential for improved predictions of hemorrhage at the brain/skull interface.

  9. Improved prediction and tracking of volcanic ash clouds

    Science.gov (United States)

    Mastin, Larry G.; Webley, Peter

    2009-01-01

    During the past 30??years, more than 100 airplanes have inadvertently flown through clouds of volcanic ash from erupting volcanoes. Such encounters have caused millions of dollars in damage to the aircraft and have endangered the lives of tens of thousands of passengers. In a few severe cases, total engine failure resulted when ash was ingested into turbines and coating turbine blades. These incidents have prompted the establishment of cooperative efforts by the International Civil Aviation Organization and the volcanological community to provide rapid notification of eruptive activity, and to monitor and forecast the trajectories of ash clouds so that they can be avoided by air traffic. Ash-cloud properties such as plume height, ash concentration, and three-dimensional ash distribution have been monitored through non-conventional remote sensing techniques that are under active development. Forecasting the trajectories of ash clouds has required the development of volcanic ash transport and dispersion models that can calculate the path of an ash cloud over the scale of a continent or a hemisphere. Volcanological inputs to these models, such as plume height, mass eruption rate, eruption duration, ash distribution with altitude, and grain-size distribution, must be assigned in real time during an event, often with limited observations. Databases and protocols are currently being developed that allow for rapid assignment of such source parameters. In this paper, we summarize how an interdisciplinary working group on eruption source parameters has been instigating research to improve upon the current understanding of volcanic ash cloud characterization and predictions. Improved predictions of ash cloud movement and air fall will aid in making better hazard assessments for aviation and for public health and air quality. ?? 2008 Elsevier B.V.

  10. Improving the Remote Sensing Retrieval of Phytoplankton Functional Types (PFT Using Empirical Orthogonal Functions: A Case Study in a Coastal Upwelling Region

    Directory of Open Access Journals (Sweden)

    Marco Correa-Ramirez

    2018-03-01

    Full Text Available An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a in the normalized water-leaving radiance (nLw spectrum can be effectively isolated from the signal of accessory pigment biomarkers of different PFT by using Empirical Orthogonal Function (EOF decomposition. PHYSTWO operates in the dimensionless plane composed by the first two EOF modes generated through the decomposition of a space–nLw matrix at seven wavelengths (412, 443, 469, 488, 531, 547, and 555 nm. PFT determination is performed using orthogonal models derived from the acceptable ranges of anomalies proposed by PHYSAT but adjusted with the available regional and global data. In applying PHYSTWO to study phytoplankton community structures in the coastal upwelling system off central Chile, we find that this method increases the accuracy of PFT identification, extends the application of this tool to waters with high Chl-a concentration, and significantly decreases (~60% the undetermined retrievals when compared with PHYSAT. The improved accuracy of PHYSTWO and its applicability for the identification of new PFT are discussed.

  11. The global distribution of leaf chlorophyll content and seasonal controls on carbon uptake

    Science.gov (United States)

    Croft, H.; Chen, J. M.; Luo, X.; Bartlett, P. A.; Staebler, R. M.; He, L.; Mo, G.; Luo, S.; Simic, A.; Arabian, J.; He, Y.; Zhang, Y.; Beringer, J.; Hutley, L. B.; Noland, T. L.; Arellano, P.; Stahl, C.; Homolová, L.; Bonal, D.; Malenovský, Z.; Yi, Q.; Amiri, R.

    2017-12-01

    Leaf chlorophyll (ChlLeaf) is crucial to biosphere-atmosphere exchanges of carbon and water, and the functioning of terrestrial ecosystems. Improving the accuracy of modelled photosynthetic carbon uptake is a central priority for understanding ecosystem response to a changing climate. A source of uncertainty within gross primary productivity (GPP) estimates is the failure to explicitly consider seasonal controls on leaf photosynthetic potential. Whilst the inclusion of ChlLeafinto carbon models has shown potential to provide a physiological constraint, progress has been hampered by the absence of a spatially-gridded, global chlorophyll product. Here, we present the first spatially-continuous, global view of terrestrial ChlLeaf, at weekly intervals. Satellite-derived ChlLeaf was modelled using a physically-based radiative transfer modelling approach, with a two stage model inversion method. 4-Scale and SAIL canopy models were first used to model leaf-level reflectance from ENIVSAT MERIS 300m satellite data. The PROSPECT leaf model was then used to derive ChlLeaf from the modelled leaf reflectance. This algorithm was validated using measured ChlLeaf data from 248 measurements within 26 field locations, covering six plant functional types (PFTs). Modelled results show very good relationships with measured data, particularly for deciduous broadleaf forests (R2 = 0.67; pmake an important step towards improving the accuracy of global carbon budgets.

  12. Improvement of energy expenditure prediction from heart rate during running

    International Nuclear Information System (INIS)

    Charlot, Keyne; Borne, Rachel; Richalet, Jean-Paul; Chapelot, Didier; Pichon, Aurélien; Cornolo, Jérémy; Brugniaux, Julien Vincent

    2014-01-01

    We aimed to develop new equations that predict exercise-induced energy expenditure (EE) more accurately than previous ones during running by including new parameters as fitness level, body composition and/or running intensity in addition to heart rate (HR). Original equations predicting EE were created from data obtained during three running intensities (25%, 50% and 70% of HR reserve) performed by 50 subjects. Five equations were conserved according to their accuracy assessed from error rates, interchangeability and correlations analyses: one containing only basic parameters, two containing VO 2max  or speed at VO 2max  and two including running speed with or without HR. Equations accuracy was further tested in an independent sample during a 40 min validation test at 50% of HR reserve. It appeared that: (1) the new basic equation was more accurate than pre-existing equations (R 2  0.809 versus. 0,737 respectively); (2) the prediction of EE was more accurate with the addition of VO 2max  (R 2  = 0.879); and (3) the equations containing running speed were the most accurate and were considered to have good agreement with indirect calorimetry. In conclusion, EE estimation during running might be significantly improved by including running speed in the predictive models, a parameter readily available with treadmill or GPS. (paper)

  13. Mid- and long-term runoff predictions by an improved phase-space reconstruction model

    International Nuclear Information System (INIS)

    Hong, Mei; Wang, Dong; Wang, Yuankun; Zeng, Xiankui; Ge, Shanshan; Yan, Hengqian; Singh, Vijay P.

    2016-01-01

    In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated

  14. Mid- and long-term runoff predictions by an improved phase-space reconstruction model

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Mei [Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing (China); Wang, Dong, E-mail: wangdong@nju.edu.cn [Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093 (China); Wang, Yuankun; Zeng, Xiankui [Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Collaborative Innovation Center of South China Sea Studies, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210093 (China); Ge, Shanshan; Yan, Hengqian [Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and oceanography, PLA University of Science and Technology, Nanjing (China); Singh, Vijay P. [Department of Biological and Agricultural Engineering Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843 (United States)

    2016-07-15

    In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated

  15. An improved ENSO simulation by representing chlorophyll-induced climate feedback in the NCAR Community Earth System Model.

    Science.gov (United States)

    Kang, Xianbiao; Zhang, Rong-Hua; Gao, Chuan; Zhu, Jieshun

    2017-12-07

    The El Niño-Southern oscillation (ENSO) simulated in the Community Earth System Model of the National Center for Atmospheric Research (NCAR CESM) is much stronger than in reality. Here, satellite data are used to derive a statistical relationship between interannual variations in oceanic chlorophyll (CHL) and sea surface temperature (SST), which is then incorporated into the CESM to represent oceanic chlorophyll -induced climate feedback in the tropical Pacific. Numerical runs with and without the feedback (referred to as feedback and non-feedback runs) are performed and compared with each other. The ENSO amplitude simulated in the feedback run is more accurate than that in the non-feedback run; quantitatively, the Niño3 SST index is reduced by 35% when the feedback is included. The underlying processes are analyzed and the results show that interannual CHL anomalies exert a systematic modulating effect on the solar radiation penetrating into the subsurface layers, which induces differential heating in the upper ocean that affects vertical mixing and thus SST. The statistical modeling approach proposed in this work offers an effective and economical way for improving climate simulations.

  16. Improving the Accuracy of Predicting Maximal Oxygen Consumption (VO2pk)

    Science.gov (United States)

    Downs, Meghan E.; Lee, Stuart M. C.; Ploutz-Snyder, Lori; Feiveson, Alan

    2016-01-01

    Maximal oxygen (VO2pk) is the maximum amount of oxygen that the body can use during intense exercise and is used for benchmarking endurance exercise capacity. The most accurate method to determineVO2pk requires continuous measurements of ventilation and gas exchange during an exercise test to maximal effort, which necessitates expensive equipment, a trained staff, and time to set-up the equipment. For astronauts, accurate VO2pk measures are important to assess mission critical task performance capabilities and to prescribe exercise intensities to optimize performance. Currently, astronauts perform submaximal exercise tests during flight to predict VO2pk; however, while submaximal VO2pk prediction equations provide reliable estimates of mean VO2pk for populations, they can be unacceptably inaccurate for a given individual. The error in current predictions and logistical limitations of measuring VO2pk, particularly during spaceflight, highlights the need for improved estimation methods.

  17. Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

    Science.gov (United States)

    Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

    2017-12-01

    Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

  18. Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

    Science.gov (United States)

    Daigle, Matthew John; Goebel, Kai Frank

    2010-01-01

    Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

  19. RAPID COMMUNICATION: Improving prediction accuracy of GPS satellite clocks with periodic variation behaviour

    Science.gov (United States)

    Heo, Youn Jeong; Cho, Jeongho; Heo, Moon Beom

    2010-07-01

    The broadcast ephemeris and IGS ultra-rapid predicted (IGU-P) products are primarily available for use in real-time GPS applications. The IGU orbit precision has been remarkably improved since late 2007, but its clock products have not shown acceptably high-quality prediction performance. One reason for this fact is that satellite atomic clocks in space can be easily influenced by various factors such as temperature and environment and this leads to complicated aspects like periodic variations, which are not sufficiently described by conventional models. A more reliable prediction model is thus proposed in this paper in order to be utilized particularly in describing the periodic variation behaviour satisfactorily. The proposed prediction model for satellite clocks adds cyclic terms to overcome the periodic effects and adopts delay coordinate embedding, which offers the possibility of accessing linear or nonlinear coupling characteristics like satellite behaviour. The simulation results have shown that the proposed prediction model outperforms the IGU-P solutions at least on a daily basis.

  20. Role of chlorophyllase in chlorophyll homeostasis and post-harvest breakdown in Piper betle L. leaf.

    Science.gov (United States)

    Gupta, Supriya; Gupta, Sanjay Mohan; Kumar, Nikhil

    2011-10-01

    Piper betle L., a dioecious shade-loving perennial climber is one of the important Pan-Asiatic plants. More than hundred landraces having marked variation in leaf chlorophyll (Chl) content are in cultivation in India. In this study, role of chlorophyllase (Chlase) in Chl homeostasis and post-harvest breakdown was investigated in two contrasting P. betle landraces Kapoori Vellaikodi (KV) with light green and Khasi Shillong (KS) with dark green leaves. The two landraces showed negative correlation between Chl content and Chlase activity in fresh as well as stored leaves. Accumulation of chlorophyllide a (Chlid a) was correlated with the level of Chlase activity, which was higher in KV than KS. The overall response of abscisic acid (ABA) and benzylaminopurine (BAP) was similar in KV and KS, however, the time-course was different. ABA-induced Chl loss was accompanied by rise in Chlase activity in KV and KS and the delay in Chl loss by BAP was accompanied by reduction in Chlase activity. While there were significant differences in Chlase activity in KV and KS, only minor differences were observed in the enzyme properties like pH and temperature optima, Km and Vmax. No landrace-related differences were observed on the effect of metal ions and functional group reagents/amino acid effectors on Chlase activity. These results showed that despite significant differences in Chl content and Chlase activity between landraces KV and KS, the properties of Chlase were similar. The findings show that in P. betle Chlase is involved in Chl homeostasis and also in Chl degradation during post-harvest storage and responds to hormonal regulations. These findings might be useful in predicting the stability of Chl during post-harvest storage and also the shelf-life in other P. betle landraces.

  1. Coastal 'Big Data' and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

    Science.gov (United States)

    Millie, David F.; Weckman, Gary R.; Young, William A.; Ivey, James E.; Fries, David P.; Ardjmand, Ehsan; Fahnenstiel, Gary L.

    2013-07-01

    Coastal monitoring has become reliant upon automated sensors for data acquisition. Such a technical commitment comes with a cost; particularly, the generation of large, high-dimensional data streams ('Big Data') that personnel must search through to identify data structures. Nature-inspired computation, inclusive of artificial neural networks (ANNs), affords the unearthing of complex, recurring patterns within sizable data volumes. In 2009, select meteorological and hydrological data were acquired via autonomous instruments in Sarasota Bay, Florida (USA). ANNs estimated continuous chlorophyll (CHL) a concentrations from abiotic predictors, with correlations between measured:modeled concentrations >0.90 and model efficiencies ranging from 0.80 to 0.90. Salinity and water temperature were the principal influences for modeled CHL within the Bay; concentrations steadily increased at temperatures >28° C and were greatest at salinities 6.1 μg CHL L-1 maximized at a salinity of ca. 36.3 and a temperature of ca. 29.5 °C. A 10th-order Chebyshev bivariate polynomial equation was fit (adj. r2 = 0.99, p turbidity, temperature, and salinity (and to lesser degrees, wind speed, wind/current direction, irradiance, and urea-nitrogen) were key variables for quantitative rules in tree formalisms. Taken together, computations enabled knowledge provision for and quantifiable representations of the non-linear relationships between environmental variables and CHL a.

  2. Improved time series prediction with a new method for selection of model parameters

    International Nuclear Information System (INIS)

    Jade, A M; Jayaraman, V K; Kulkarni, B D

    2006-01-01

    A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

  3. An improved method for predicting brittleness of rocks via well logs in tight oil reservoirs

    Science.gov (United States)

    Wang, Zhenlin; Sun, Ting; Feng, Cheng; Wang, Wei; Han, Chuang

    2018-06-01

    There can be no industrial oil production in tight oil reservoirs until fracturing is undertaken. Under such conditions, the brittleness of the rocks is a very important factor. However, it has so far been difficult to predict. In this paper, the selected study area is the tight oil reservoirs in Lucaogou formation, Permian, Jimusaer sag, Junggar basin. According to the transformation of dynamic and static rock mechanics parameters and the correction of confining pressure, an improved method is proposed for quantitatively predicting the brittleness of rocks via well logs in tight oil reservoirs. First, 19 typical tight oil core samples are selected in the study area. Their static Young’s modulus, static Poisson’s ratio and petrophysical parameters are measured. In addition, the static brittleness indices of four other tight oil cores are measured under different confining pressure conditions. Second, the dynamic Young’s modulus, Poisson’s ratio and brittleness index are calculated using the compressional and shear wave velocity. With combination of the measured and calculated results, the transformation model of dynamic and static brittleness index is built based on the influence of porosity and clay content. The comparison of the predicted brittleness indices and measured results shows that the model has high accuracy. Third, on the basis of the experimental data under different confining pressure conditions, the amplifying factor of brittleness index is proposed to correct for the influence of confining pressure on the brittleness index. Finally, the above improved models are applied to formation evaluation via well logs. Compared with the results before correction, the results of the improved models agree better with the experimental data, which indicates that the improved models have better application effects. The brittleness index prediction method of tight oil reservoirs is improved in this research. It is of great importance in the optimization of

  4. Improved prediction of reservoir behavior through integration of quantitative geological and petrophysical data

    Energy Technology Data Exchange (ETDEWEB)

    Auman, J. B.; Davies, D. K.; Vessell, R. K.

    1997-08-01

    Methodology that promises improved reservoir characterization and prediction of permeability, production and injection behavior during primary and enhanced recovery operations was demonstrated. The method is based on identifying intervals of unique pore geometry by a combination of image analysis techniques and traditional petrophysical measurements to calculate rock type and estimate permeability and saturation. Results from a complex carbonate and sandstone reservoir were presented as illustrative examples of the versatility and high level of accuracy of this method in predicting reservoir quality. 16 refs., 5 tabs., 14 figs.

  5. Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies.

    Science.gov (United States)

    Kong, Weiping; Huang, Wenjiang; Casa, Raffaele; Zhou, Xianfeng; Ye, Huichun; Dong, Yingying

    2017-11-23

    Monitoring the vertical profile of leaf chlorophyll (Chl) content within winter wheat canopies is of significant importance for revealing the real nutritional status of the crop. Information on the vertical profile of Chl content is not accessible to nadir-viewing remote or proximal sensing. Off-nadir or multi-angle sensing would provide effective means to detect leaf Chl content in different vertical layers. However, adequate information on the selection of sensitive spectral bands and spectral index formulas for vertical leaf Chl content estimation is not yet available. In this study, all possible two-band and three-band combinations over spectral bands in normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like types of indices at different viewing angles were calculated and assessed for their capability of estimating leaf Chl for three vertical layers of wheat canopies. The vertical profiles of Chl showed top-down declining trends and the patterns of band combinations sensitive to leaf Chl content varied among different vertical layers. Results indicated that the combinations of green band (520 nm) with NIR bands were efficient in estimating upper leaf Chl content, whereas the red edge (695 nm) paired with NIR bands were dominant in quantifying leaf Chl in the lower layers. Correlations between published spectral indices and all NDVI-, SR- and CI-like types of indices and vertical distribution of Chl content showed that reflectance measured from 50°, 30° and 20° backscattering viewing angles were the most promising to obtain information on leaf Chl in the upper-, middle-, and bottom-layer, respectively. Three types of optimized spectral indices improved the accuracy for vertical leaf Chl content estimation. The optimized three-band CI-like index performed the best in the estimation of vertical distribution of leaf Chl content, with R² of 0.84-0.69, and RMSE of 5.37-5.56 µg/cm² from the top to the bottom layers

  6. Effects of global climate change on chlorophyll-a concentrations in a tropical aquatic system during a cyanobacterial bloom: a microcosm study

    Directory of Open Access Journals (Sweden)

    Meirielle Euripa Pádua de Moura

    2017-05-01

    Full Text Available Recent studies have investigated the impact of climate change on aquatic environments, and Chlorophyll-a (Chl-a concentration is a quick and reliable variable for monitoring such changes. This study evaluated the impact of rainfall frequency as a diluting agent and the effect of increased temperature on Chl-a concentrations in eutrophic environments during a bloom of cyanobacteria. This was based on the hypothesis that the concentration of Chl-a will be higher in treatments in which the rainfall frequency is not homogeneous and that warmer temperatures predicted due to climate change should favor higher concentrations of Chl-a. The experiment was designed to investigate three factors: temperature, precipitation and time. Temperature was tested with two treatment levels (22°C and the future temperature of 25°C. Precipitation was tested with four treatments (no precipitation, a homogeneous precipitation pattern, and two types of concentrated precipitation patterns. Experiments were run for 15 days, and Chl-a concentration was measured every five days in each of the temperature and precipitation treatments. The water used in the microcosms was collected from a eutrophic lake located in Central Brazil during a bloom of filamentous cyanobacteria (Geilterinema amphibium. Chl-a levels were high in all treatments. The higher temperature treatment showed increased Chl-a concentration (F=10.343; P=0.002; however, the extreme precipitation events did not significantly influence Chl-a concentrations (F=1.198; P=0.326. Therefore, the study demonstrates that future climatic conditions (projected to 2100, such as elevated temperatures, may affect the primary productivity of aquatic environments in tropical aquatic systems.

  7. Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

    Science.gov (United States)

    Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

    2016-08-07

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry.

    Science.gov (United States)

    Mozer, M C; Wolniewicz, R; Grimes, D B; Johnson, E; Kaushansky, H

    2000-01-01

    Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another.We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include logit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47,000 U.S. domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.

  9. Improving MJO Prediction and Simulation Using AGCM Coupled Ocean Model with Refined Vertical Resolution

    Science.gov (United States)

    Tu, Chia-Ying; Tseng, Wan-Ling; Kuo, Pei-Hsuan; Lan, Yung-Yao; Tsuang, Ben-Jei; Hsu, Huang-Hsiung

    2017-04-01

    Precipitation in Taiwan area is significantly influenced by MJO (Madden-Julian Oscillation) in the boreal winter. This study is therefore conducted by toggling the MJO prediction and simulation with a unique model structure. The one-dimensional TKE (Turbulence Kinetic Energy) type ocean model SIT (Snow, Ice, Thermocline) with refined vertical resolution near surface is able to resolve cool skin, as well as diurnal warm layer. SIT can simulate accurate SST and hence give precise air-sea interaction. By coupling SIT with ECHAM5 (MPI-Meteorology), CAM5 (NCAR) and HiRAM (GFDL), the MJO simulations in 20-yrs climate integrations conducted by three SIT-coupled AGCMs are significant improved comparing to those driven by prescribed SST. The horizontal resolutions in ECHAM5, CAM5 and HiRAM are 2-deg., 1-deg and 0.5-deg., respectively. This suggests that the improvement of MJO simulation by coupling SIT is AGCM-resolution independent. This study further utilizes HiRAM coupled SIT to evaluate its MJO forecast skill. HiRAM has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013), but was not as successful in MJO forecast. The preliminary result of the HiRAM-SIT experiment during DYNAMO period shows improved success in MJO forecast. These improvements of MJO prediction and simulation in both hindcast experiments and climate integrations are mainly from better-simulated SST diurnal cycle and diurnal amplitude, which is contributed by the refined vertical resolution near ocean surface in SIT. Keywords: MJO Predictability, DYNAMO

  10. Better estimation of protein-DNA interaction parameters improve prediction of functional sites

    Directory of Open Access Journals (Sweden)

    O'Flanagan Ruadhan A

    2008-12-01

    Full Text Available Abstract Background Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment method designed to produce many such sequences. Results We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP, and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. Conclusion We think that training binding site detection

  11. Combining specificity determining and conserved residues improves functional site prediction

    Directory of Open Access Journals (Sweden)

    Gelfand Mikhail S

    2009-06-01

    Full Text Available Abstract Background Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core. Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. Results Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs, as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. Conclusion The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.

  12. Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant

    International Nuclear Information System (INIS)

    Wang, Hang; Peng, Min-jun; Wu, Peng; Cheng, Shou-yu

    2016-01-01

    Highlights: • Different methods for online monitoring and diagnosis are summarized. • Numerical simulation modeling of condensate and feed water system in nuclear power plant are done by FORTRAN programming. • Integrated online monitoring and prediction methods have been developed and tested. • Online monitoring module, fault diagnosis module and trends prediction module can be verified with each other. - Abstract: Faults or accidents may occur in a nuclear power plant (NPP), but it is hard for operators to recognize the situation and take effective measures quickly. So, online monitoring, diagnosis and prediction (OMDP) is used to provide enough information to operators and improve the safety of NPPs. In this paper, distributed conservation equation (DCE) and artificial immunity system (AIS) are proposed for online monitoring and diagnosis. On this basis, quantitative simulation models and interactive database are combined to predict the trends and severity of faults. The effectiveness of OMDP in improving the monitoring and prediction of condensate and feed water system (CFWS) was verified through simulation tests.

  13. Prediction and moderation of improvement in cognitive-behavioral and psychodynamic psychotherapy for panic disorder.

    Science.gov (United States)

    Chambless, Dianne L; Milrod, Barbara; Porter, Eliora; Gallop, Robert; McCarthy, Kevin S; Graf, Elizabeth; Rudden, Marie; Sharpless, Brian A; Barber, Jacques P

    2017-08-01

    To identify variables predicting psychotherapy outcome for panic disorder or indicating which of 2 very different forms of psychotherapy-panic-focused psychodynamic psychotherapy (PFPP) or cognitive-behavioral therapy (CBT)-would be more effective for particular patients. Data were from 161 adults participating in a randomized controlled trial (RCT) including these psychotherapies. Patients included 104 women; 118 patients were White, 33 were Black, and 10 were of other races; 24 were Latino(a). Predictors/moderators measured at baseline or by Session 2 of treatment were used to predict change on the Panic Disorder Severity Scale (PDSS). Higher expectancy for treatment gains (Credibility/Expectancy Questionnaire d = -1.05, CI 95% [-1.50, -0.60]), and later age of onset (d = -0.65, CI 95% [-0.98, -0.32]) were predictive of greater change. Both variables were also significant moderators: patients with low expectancy of improvement improved significantly less in PFPP than their counterparts in CBT, whereas this was not the case for patients with average or high levels of expectancy. When patients had an onset of panic disorder later in life (≥27.5 years old), they fared as well in PFPP as CBT. In contrast, at low and mean levels of onset age, CBT was the more effective treatment. Predictive variables suggest possibly fruitful foci for improvement of treatment outcome. In terms of moderation, CBT was the more consistently effective treatment, but moderators identified some patients who would do as well in PFPP as in CBT, thereby widening empirically supported options for treatment of this disorder. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva.

    Science.gov (United States)

    van Dijk, Lisanne V; Brouwer, Charlotte L; van der Schaaf, Arjen; Burgerhof, Johannes G M; Beukinga, Roelof J; Langendijk, Johannes A; Sijtsema, Nanna M; Steenbakkers, Roel J H M

    2017-02-01

    Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER 12m ) and sticky saliva (STIC 12m ) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XER base ) or sticky saliva (STIC base ) scores. The purpose is to improve prediction of XER 12m and STIC 12m with patient-specific characteristics, based on CT image biomarkers (IBMs). Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancer patients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping. The prediction of XER 12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XER base . For STIC 12m , the IBM maximum CT intensity of the submandibular gland was selected in addition to STIC base and mean dose to submandibular glands. Prediction of XER 12m and STIC 12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  15. Improving Flood Predictions in Data-Scarce Basins

    Science.gov (United States)

    Vimal, Solomon; Zanardo, Stefano; Rafique, Farhat; Hilberts, Arno

    2017-04-01

    Flood modeling methodology at Risk Management Solutions Ltd. has evolved over several years with the development of continental scale flood risk models spanning most of Europe, the United States and Japan. Pluvial (rain fed) and fluvial (river fed) flood maps represent the basis for the assessment of regional flood risk. These maps are derived by solving the 1D energy balance equation for river routing and 2D shallow water equation (SWE) for overland flow. The models are run with high performance computing and GPU based solvers as the time taken for simulation is large in such continental scale modeling. These results are validated with data from authorities and business partners, and have been used in the insurance industry for many years. While this methodology has been proven extremely effective in regions where the quality and availability of data are high, its application is very challenging in other regions where data are scarce. This is generally the case for low and middle income countries, where simpler approaches are needed for flood risk modeling and assessment. In this study we explore new methods to make use of modeling results obtained in data-rich contexts to improve predictive ability in data-scarce contexts. As an example, based on our modeled flood maps in data-rich countries, we identify statistical relationships between flood characteristics and topographic and climatic indicators, and test their generalization across physical domains. Moreover, we apply the Height Above Nearest Drainage (HAND)approach to estimate "probable" saturated areas for different return period flood events as functions of basin characteristics. This work falls into the well-established research field of Predictions in Ungauged Basins.

  16. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

  17. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

  18. Improved prediction for the mass of the W boson in the NMSSM

    International Nuclear Information System (INIS)

    Staal, O.; Zeune, L.

    2015-10-01

    Electroweak precision observables, being highly sensitive to loop contributions of new physics, provide a powerful tool to test the theory and to discriminate between different models of the underlying physics. In that context, the W boson mass, M W , plays a crucial role. The accuracy of the M W measurement has been significantly improved over the last years, and further improvement of the experimental accuracy is expected from future LHC measurements. In order to fully exploit the precise experimental determination, an accurate theoretical prediction for M W in the Standard Model (SM) and extensions of it is of central importance. We present the currently most accurate prediction for the W boson mass in the Next-to-Minimal Supersymmetric extension of the Standard Model (NMSSM), including the full one-loop result and all available higher-order corrections of SM and SUSY type. The evaluation of M W is performed in a flexible framework, which facilitates the extension to other models beyond the SM. We show numerical results for the W boson mass in the NMSSM, focussing on phenomenologically interesting scenarios, in which the Higgs signal can be interpreted as the lightest or second lightest CP-even Higgs boson of the NMSSM. We find that, for both Higgs signal interpretations, the NMSSM M W prediction is well compatible with the measurement. We study the SUSY contributions to M W in detail and investigate in particular the genuine NMSSM effects from the Higgs and neutralino sectors.

  19. Síntese e hidrólise de azalactonas de Erlenmeyer-Plöchl mediadas por radiação micro-ondas em aparelhos doméstico e dedicado: experimentos de química orgânica para a graduação Synthesis and hydrolysis of Erlenmeyer-Plöchl azalactones mediated by microwave radiation in domestic and dedicated ovens: undergraduate organic chemistry experiments

    Directory of Open Access Journals (Sweden)

    Silvio Cunha

    2013-01-01

    Full Text Available This work describes a green chemistry experiment for the synthesis of Erlenmeyer-Plöchl azalactones mediated by microwave irradiation, employing both dedicated and domestic equipment. Hippuric acid was reacted with equimolar amounts of benzaldehyde, p-chloro-benzaldehyde or p-N,N-dimethyl-benzaldehyde in acetic anhydride as the solvent. Acid hydrolysis of obtained 4-benzylidene-2-phenyloxazol-5(4H-one under microwave and convectional heating afforded Z-α-(benzoylaminocinnamic acid at a 51-61.5% yield. The UV-Vis molecular spectra of 4-benzylidene-2-phenyloxazol-5(4H-one and 4-(4'-N,N-dimethylbenzylidene-2-phenyloxazol-5(4H-one were obtained in ethanol, CH2Cl2 and DMSO and bathochromic shift was observed for the latter azalactone.

  20. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    International Nuclear Information System (INIS)

    Rattá, G.A.; Vega, J.; Murari, A.

    2012-01-01

    Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  1. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  2. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  3. Improvement of PM10 prediction in East Asia using inverse modeling

    Science.gov (United States)

    Koo, Youn-Seo; Choi, Dae-Ryun; Kwon, Hi-Yong; Jang, Young-Kee; Han, Jin-Seok

    2015-04-01

    Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10 ㎛ in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia.

  4. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

    Science.gov (United States)

    Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

    2018-02-19

    Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

  5. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Directory of Open Access Journals (Sweden)

    Michael F Sloma

    2017-11-01

    Full Text Available Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

  6. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Science.gov (United States)

    Sloma, Michael F; Mathews, David H

    2017-11-01

    Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

  7. Improving the Accuracy of a Heliocentric Potential (HCP Prediction Model for the Aviation Radiation Dose

    Directory of Open Access Journals (Sweden)

    Junga Hwang

    2016-12-01

    Full Text Available The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs, flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA. However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015. In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1 real-time daily sunspot assessments, (2 predictions of the daily HCP by our prediction algorithm, and (3 calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

  8. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  9. Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

    Directory of Open Access Journals (Sweden)

    Otto Savolainen

    Full Text Available The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D risk that would improve prediction of T2D over current risk markers.Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629. Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA, smoking, serum adiponectin alone, and in combination with metabolomics had the largest areas under the curve (AUC (0.794 (95% confidence interval [0.738-0.850] and 0.808 [0.749-0.867] respectively, with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]. Prediction based on non-blood based measures was 0.638 [0.565-0.711].Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

  10. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  11. Thermal-based modeling of coupled carbon, water, and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations

    KAUST Repository

    Schull, M. A.

    2015-03-11

    Recent studies have shown that estimates of leaf chlorophyll content (Chl), defined as the combined mass of chlorophyll a and chlorophyll b per unit leaf area, can be useful for constraining estimates of canopy light use efficiency (LUE). Canopy LUE describes the amount of carbon assimilated by a vegetative canopy for a given amount of absorbed photosynthetically active radiation (APAR) and is a key parameter for modeling land-surface carbon fluxes. A carbon-enabled version of the remote-sensing-based two-source energy balance (TSEB) model simulates coupled canopy transpiration and carbon assimilation using an analytical sub-model of canopy resistance constrained by inputs of nominal LUE (βn), which is modulated within the model in response to varying conditions in light, humidity, ambient CO2 concentration, and temperature. Soil moisture constraints on water and carbon exchange are conveyed to the TSEB-LUE indirectly through thermal infrared measurements of land-surface temperature. We investigate the capability of using Chl estimates for capturing seasonal trends in the canopy βn from in situ measurements of Chl acquired in irrigated and rain-fed fields of soybean and maize near Mead, Nebraska. The results show that field-measured Chl is nonlinearly related to βn, with variability primarily related to phenological changes during early growth and senescence. Utilizing seasonally varying βn inputs based on an empirical relationship with in situ measured Chl resulted in improvements in carbon flux estimates from the TSEB model, while adjusting the partitioning of total water loss between plant transpiration and soil evaporation. The observed Chl-βn relationship provides a functional mechanism for integrating remotely sensed Chl into the TSEB model, with the potential for improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

  12. On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data

    Directory of Open Access Journals (Sweden)

    E. Momoniat

    2013-01-01

    Full Text Available The use of a BDF method as a tool to correct the direction of predictions made using curve fitting techniques is investigated. Random data is generated in such a fashion that it has the same properties as the data we are modelling. The data is assumed to have “memory” such that certain information imbedded in the data will remain within a certain range of points. Data within this period where “memory” exists—say at time steps t1,t2,…,tn—is curve-fitted to produce a prediction at the next discrete time step, tn+1. In this manner a vector of predictions is generated and converted into a discrete ordinary differential representing the gradient of the data. The BDF method implemented with this lower order approximation is used as a means of improving upon the direction of the generated predictions. The use of the BDF method in this manner improves the prediction of the direction of the time series by approximately 30%.

  13. ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

    Directory of Open Access Journals (Sweden)

    Raghava Gajendra PS

    2008-11-01

    Full Text Available Abstract Background The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features. Results Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM. In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets. Conclusion These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search

  14. Breast calcifications. A standardized mammographic reporting and data system to improve positive predictive value

    International Nuclear Information System (INIS)

    Perugini, G.; Bonzanini, B.; Valentino, C.

    1999-01-01

    The purpose of this work is to investigate the usefulness of a standardized reporting and data system in improving the positive predictive value of mammography in breast calcifications. Using the Breast Imaging Reporting and Data System lexicon developed by the American College of Radiology, it is defined 5 descriptive categories of breast calcifications and classified diagnostic suspicion of malignancy on a 3-grade scale (low, intermediate and high). Two radiologists reviewed 117 mammographic studies selected from those of the patients submitted to surgical biopsy for mammographically detected calcifications from January 1993 to December 1997, and classified them according to the above criteria. The positive predictive value was calculated for all examinations and for the stratified groups. Defining a standardized system for assessing and describing breast calcifications helps improve the diagnostic accuracy of mammography in clinical practice [it

  15. Improving a two-equation eddy-viscosity turbulence model to predict the aerodynamic performance of thick wind turbine airfoils

    Science.gov (United States)

    Bangga, Galih; Kusumadewi, Tri; Hutomo, Go; Sabila, Ahmad; Syawitri, Taurista; Setiadi, Herlambang; Faisal, Muhamad; Wiranegara, Raditya; Hendranata, Yongki; Lastomo, Dwi; Putra, Louis; Kristiadi, Stefanus

    2018-03-01

    Numerical simulations for relatively thick airfoils are carried out in the present studies. An attempt to improve the accuracy of the numerical predictions is done by adjusting the turbulent viscosity of the eddy-viscosity Menter Shear-Stress-Transport (SST) model. The modification involves the addition of a damping factor on the wall-bounded flows incorporating the ratio of the turbulent kinetic energy to its specific dissipation rate for separation detection. The results are compared with available experimental data and CFD simulations using the original Menter SST model. The present model improves the lift polar prediction even though the stall angle is still overestimated. The improvement is caused by the better prediction of separated flow under a strong adverse pressure gradient. The results show that the Reynolds stresses are damped near the wall causing variation of the logarithmic velocity profiles.

  16. Predictive Factors for Subjective Improvement in Lumbar Spinal Stenosis Patients with Nonsurgical Treatment: A 3-Year Prospective Cohort Study.

    Directory of Open Access Journals (Sweden)

    Ko Matsudaira

    Full Text Available To assess the predictive factors for subjective improvement with nonsurgical treatment in consecutive patients with lumbar spinal stenosis (LSS.Patients with LSS were enrolled from 17 medical centres in Japan. We followed up 274 patients (151 men; mean age, 71 ± 7.4 years for 3 years. A multivariable logistic regression model was used to assess the predictive factors for subjective symptom improvement with nonsurgical treatment.In 30% of patients, conservative treatment led to a subjective improvement in the symptoms; in 70% of patients, the symptoms remained unchanged, worsened, or required surgical treatment. The multivariable analysis of predictive factors for subjective improvement with nonsurgical treatment showed that the absence of cauda equina symptoms (only radicular symptoms had an odds ratio (OR of 3.31 (95% confidence interval [CI]: 1.50-7.31; absence of degenerative spondylolisthesis/scoliosis had an OR of 2.53 (95% CI: 1.13-5.65; <1-year duration of illness had an OR of 3.81 (95% CI: 1.46-9.98; and hypertension had an OR of 2.09 (95% CI: 0.92-4.78.The predictive factors for subjective symptom improvement with nonsurgical treatment in LSS patients were the presence of only radicular symptoms, absence of degenerative spondylolisthesis/scoliosis, and an illness duration of <1 year.

  17. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

    Science.gov (United States)

    Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

    2014-12-30

    This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

  18. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chuncai Xiao

    2014-12-01

    Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

  19. Optically-derived estimates of phytoplankton size class and taxonomic group biomass in the Eastern Subarctic Pacific Ocean

    Science.gov (United States)

    Zeng, Chen; Rosengard, Sarah Z.; Burt, William; Peña, M. Angelica; Nemcek, Nina; Zeng, Tao; Arrigo, Kevin R.; Tortell, Philippe D.

    2018-06-01

    We evaluate several algorithms for the estimation of phytoplankton size class (PSC) and functional type (PFT) biomass from ship-based optical measurements in the Subarctic Northeast Pacific Ocean. Using underway measurements of particulate absorption and backscatter in surface waters, we derived estimates of PSC/PFT based on chlorophyll-a concentrations (Chl-a), particulate absorption spectra and the wavelength dependence of particulate backscatter. Optically-derived [Chl-a] and phytoplankton absorption measurements were validated against discrete calibration samples, while the derived PSC/PFT estimates were validated using size-fractionated Chl-a measurements and HPLC analysis of diagnostic photosynthetic pigments (DPA). Our results showflo that PSC/PFT algorithms based on [Chl-a] and particulate absorption spectra performed significantly better than the backscatter slope approach. These two more successful algorithms yielded estimates of phytoplankton size classes that agreed well with HPLC-derived DPA estimates (RMSE = 12.9%, and 16.6%, respectively) across a range of hydrographic and productivity regimes. Moreover, the [Chl-a] algorithm produced PSC estimates that agreed well with size-fractionated [Chl-a] measurements, and estimates of the biomass of specific phytoplankton groups that were consistent with values derived from HPLC. Based on these results, we suggest that simple [Chl-a] measurements should be more fully exploited to improve the classification of phytoplankton assemblages in the Northeast Pacific Ocean.

  20. Improved monitoring of phytoplankton bloom dynamics in a Norwegian fjord by integrating satellite data, pigment analysis, and Ferrybox data with a coastal observation network

    Science.gov (United States)

    Volent, Zsolt; Johnsen, Geir; Hovland, Erlend K.; Folkestad, Are; Olsen, Lasse M.; Tangen, Karl; Sørensen, Kai

    2011-01-01

    Monitoring of the coastal environment is vitally important as these areas are of economic value and at the same time highly exposed to anthropogenic influence, in addition to variation of environmental variables. In this paper we show how the combination of bio-optical data from satellites, analysis of water samples, and a ship-mounted automatic flow-through sensor system (Ferrybox) can be used to detect and monitor phytoplankton blooms both spatially and temporally. Chlorophyll a (Chl a) data and turbidity from Ferrybox are combined with remotely sensed Chl a and total suspended matter from the MERIS instrument aboard the satellite ENVISAT (ENVIronmental SATellite) European Space Agency. Data from phytoplankton speciation and enumeration obtained by a national coastal observation network consisting of fish farms and the Norwegian Food Safety Authority are supplemented with data on phytoplankton pigments. All the data sets are then integrated in order to describe phytoplankton bloom dynamics in a Norwegian fjord over a growth season, with particular focus on Emiliania huxleyi. The approach represents a case example of how coastal environmental monitoring can be improved with existing instrument platforms. The objectives of the paper is to present the operative phytoplankton monitoring scheme in Norway, and to present an improved model of how such a scheme can be designed for a large part of the world's coastal areas.

  1. Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

    Science.gov (United States)

    Ober, Ulrike; Huang, Wen; Magwire, Michael; Schlather, Martin; Simianer, Henner; Mackay, Trudy F C

    2015-01-01

    The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

  2. Improved feature selection based on genetic algorithms for real time disruption prediction on JET

    Energy Technology Data Exchange (ETDEWEB)

    Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)

    2012-09-15

    Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

  3. Prediction of multi-wake problems using an improved Jensen wake model

    DEFF Research Database (Denmark)

    Tian, Linlin; Zhu, Wei Jun; Shen, Wen Zhong

    2017-01-01

    The improved analytical wake model named as 2D_k Jensen model (which was proposed to overcome some shortcomes in the classical Jensen wake model) is applied and validated in this work for wind turbine multi-wake predictions. Different from the original Jensen model, this newly developed 2D_k Jensen...... model uses a cosine shape instead of the top-hat shape for the velocity deficit in the wake, and the wake decay rate as a variable that is related to the ambient turbulence as well as the rotor generated turbulence. Coupled with four different multi-wake combination models, the 2D_k Jensen model...... is assessed through (1) simulating two wakes interaction under full wake and partial wake conditions and (2) predicting the power production in the Horns Rev wind farm for different wake sectors around two different wind directions. Through comparisons with field measurements, results from Large Eddy...

  4. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

    Science.gov (United States)

    Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

    2017-03-01

    Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  5. Prediction model of ammonium uranyl carbonate calcination by microwave heating using incremental improved Back-Propagation neural network

    Energy Technology Data Exchange (ETDEWEB)

    Li Yingwei [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Peng Jinhui, E-mail: jhpeng@kmust.edu.c [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Liu Bingguo [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Li Wei [Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Huang Daifu [No. 272 Nuclear Industry Factory, China National Nuclear Corporation, Hengyang, Hunan Province 421002 (China); Zhang Libo [Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China); Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan Province 650093 (China)

    2011-05-15

    Research highlights: The incremental improved Back-Propagation neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward. The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ammonium uranyl carbonate (AUC). AUC can accept the microwave energy and microwave heating can quickly decompose AUC. In the experiment of microwave calcining of AUC, the contents of U and U{sup 4+} increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth. - Abstract: The incremental improved Back-Propagation (BP) neural network prediction model was put forward, which was very useful in overcoming the problems, such as long testing cycle, high testing quantity, difficulty of optimization for process parameters, many training data probably were offered by the way of increment batch and the limitation of the system memory could make the training data infeasible, which existed in the process of calcinations for ammonium uranyl carbonate (AUC) by microwave heating. The prediction model of the nonlinear system was built, which could effectively predict the experiment of microwave calcining of AUC. The predicted results indicated that the contents of U and U{sup 4+} were increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth.

  6. Prediction model of ammonium uranyl carbonate calcination by microwave heating using incremental improved Back-Propagation neural network

    International Nuclear Information System (INIS)

    Li Yingwei; Peng Jinhui; Liu Bingguo; Li Wei; Huang Daifu; Zhang Libo

    2011-01-01

    Research highlights: → The incremental improved Back-Propagation neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward. → The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ammonium uranyl carbonate (AUC). → AUC can accept the microwave energy and microwave heating can quickly decompose AUC. → In the experiment of microwave calcining of AUC, the contents of U and U 4+ increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth. - Abstract: The incremental improved Back-Propagation (BP) neural network prediction model was put forward, which was very useful in overcoming the problems, such as long testing cycle, high testing quantity, difficulty of optimization for process parameters, many training data probably were offered by the way of increment batch and the limitation of the system memory could make the training data infeasible, which existed in the process of calcinations for ammonium uranyl carbonate (AUC) by microwave heating. The prediction model of the nonlinear system was built, which could effectively predict the experiment of microwave calcining of AUC. The predicted results indicated that the contents of U and U 4+ were increased with increasing of microwave power and irradiation time, and decreased with increasing of the material average depth.

  7. An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

    Science.gov (United States)

    Dash, Rajashree

    2017-11-01

    Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

  8. Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

    DEFF Research Database (Denmark)

    Pena, Michelle J; Jankowski, Joachim; Heinze, Georg

    2015-01-01

    OBJECTIVE: Micro and macroalbuminuria are strong risk factors for progression of nephropathy in patients with hypertension or type 2 diabetes. Early detection of progression to micro and macroalbuminuria may facilitate prevention and treatment of renal diseases. We aimed to develop plasma...... proteomics classifiers to predict the development of micro or macroalbuminuria in hypertension or type 2 diabetes. METHODS: Patients with hypertension (n = 125) and type 2 diabetes (n = 82) were selected for this case-control study from the Prevention of REnal and Vascular ENd-stage Disease cohort....... RESULTS: In hypertensive patients, the classifier improved risk prediction for transition in albuminuria stage on top of the reference model (C-index from 0.69 to 0.78; P diabetes, the classifier improved risk prediction for transition from micro to macroalbuminuria (C-index from 0...

  9. Congestion Prediction Modeling for Quality of Service Improvement in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ga-Won Lee

    2014-04-01

    Full Text Available Information technology (IT is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute “Information Networks” through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs. Information observed from sensor networks in real-time is used to increase quality of life (QoL in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.

  10. Carbon-14 labeling of phytoplankton carbon and chlorophyll a carbon: determination of specific growth rates

    International Nuclear Information System (INIS)

    Welschmeyer, N.A.; Lorenzen, C.J.

    1984-01-01

    The pattern of photosynthetic 14 C labeling over time is described for phytoplankton. The carbon-specific growth rate (d -1 ) is defined explicitly by changes in the specific activity (dpm μg -1 C) of the algae. For Skeletonema costatum, growing in axenic batch culture, the specific activities of both total cellular carbon and chlorophyll carbon increase at equal rates and the change in specific activity with time follows the predicted pattern. The specific activity of 14 C-labeled chlorophyll a was used to estimate phytoplankton growth rates and C:Chl ratios of field samples in Dabob Bay (Puget Sound), Washington. Growth rates decreased with depth and C:Chl ratios were higher for samples incubated under high light intensity. In several instances the C:Chl ratio increased from the beginning to the end of the incubation; this trend was most conspicuous near surface light intensities and for days of high total incident radiation. On these occasions, Chl a was actively 14 C labeled, yet little (or even negative) change was noted in the concentration of Chl a. These results suggest that some process (or processes) of chlorophyll degradation must be active at the same time that chlorophyll is being synthesized

  11. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  12. Protein docking prediction using predicted protein-protein interface.

    Science.gov (United States)

    Li, Bin; Kihara, Daisuke

    2012-01-10

    Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  13. Changes in the Oswestry Disability Index that predict improvement after lumbar fusion.

    Science.gov (United States)

    Djurasovic, Mladen; Glassman, Steven D; Dimar, John R; Crawford, Charles H; Bratcher, Kelly R; Carreon, Leah Y

    2012-11-01

    Clinical studies use both disease-specific and generic health outcomes measures. Disease-specific measures focus on health domains most relevant to the clinical population, while generic measures assess overall health-related quality of life. There is little information about which domains of the Oswestry Disability Index (ODI) are most important in determining improvement in overall health-related quality of life, as measured by the 36-Item Short Form Health Survey (SF-36), after lumbar spinal fusion. The objective of the study is to determine which clinical elements assessed by the ODI most influence improvement of overall health-related quality of life. A single tertiary spine center database was used to identify patients undergoing lumbar fusion for standard degenerative indications. Patients with complete preoperative and 2-year outcomes measures were included. Pearson correlation was used to assess the relationship between improvement in each item of the ODI with improvement in the SF-36 physical component summary (PCS) score, as well as achievement of the SF-36 PCS minimum clinically important difference (MCID). Multivariate regression modeling was used to examine which items of the ODI best predicted achievement for the SF-36 PCS MCID. The effect size and standardized response mean were calculated for each of the items of the ODI. A total of 1104 patients met inclusion criteria (674 female and 430 male patients). The mean age at surgery was 57 years. All items of the ODI showed significant correlations with the change in SF-36 PCS score and achievement of MCID for the SF-36 PCS, but only pain intensity, walking, and social life had r values > 0.4 reflecting moderate correlation. These 3 variables were also the dimensions that were independent predictors of the SF-36 PCS, and they were the only dimensions that had effect sizes and standardized response means that were moderate to large. Of the health dimensions measured by the ODI, pain intensity, walking

  14. Improving student success using predictive models and data visualisations

    Directory of Open Access Journals (Sweden)

    Hanan Ayad

    2012-08-01

    Full Text Available The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50–60%. At the college level in the US only 30% of students graduate from 2-year colleges in 3 years or less and approximately 50% graduate from 4-year colleges in 5 years or less. A basic challenge in delivering global education, therefore, is improving student success. By student success we mean improving retention, completion and graduation rates. In this paper we describe a Student Success System (S3 that provides a holistic, analytical view of student academic progress.1 The core of S3 is a flexible predictive modelling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualisations for reaching diagnostic insights and a case management tool for managing interventions. S3's open modular architecture will also allow integration and plug-ins with both open and proprietary software. Powered by learning analytics, S3 is intended as an end-to-end solution for identifying at-risk students, understanding why they are at risk, designing interventions to mitigate that risk and finally closing the feedback look by tracking the efficacy of the applied intervention.

  15. EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

    Science.gov (United States)

    Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

    2018-04-24

    Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

  16. Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

    Directory of Open Access Journals (Sweden)

    Ulrike Ober

    Full Text Available The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17% of the genetic variance among lines in females (males, the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

  17. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Eric R. Edelman

    2017-06-01

    Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

  18. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

    Science.gov (United States)

    Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

    2017-01-01

    For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

  19. P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery.

    Science.gov (United States)

    Wong, Jim K; Lobato, Robert L; Pinesett, Andre; Maxwell, Bryan G; Mora-Mangano, Christina T; Perez, Marco V

    2014-12-01

    To test the hypothesis that including preoperative electrocardiogram (ECG) characteristics with clinical variables significantly improves the new-onset postoperative atrial fibrillation prediction model. Retrospective analysis. Single-center university hospital. Five hundred twenty-six patients, ≥ 18 years of age, who underwent coronary artery bypass grafting, aortic valve replacement, mitral valve replacement/repair, or a combination of valve surgery and coronary artery bypass grafting requiring cardiopulmonary bypass. Retrospective review of medical records. Baseline characteristics and cardiopulmonary bypass times were collected. Digitally-measured timing and voltages from preoperative electrocardiograms were extracted. Postoperative atrial fibrillation was defined as atrial fibrillation requiring therapeutic intervention. Two hundred eight (39.5%) patients developed postoperative atrial fibrillation. Clinical predictors were age, ejection fractionelectrocardiogram variables to the prediction model with only clinical predictors significantly improved the area under the receiver operating characteristic curve, from 0.71 to 0.78 (p<0.01). Overall net reclassification improvement was 0.059 (p = 0.09). Among those who developed postoperative atrial fibrillation, the net reclassification improvement was 0.063 (p = 0.03). Several p-wave characteristics are independently associated with postoperative atrial fibrillation. Addition of these parameters improves the postoperative atrial fibrillation prediction model. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Improving diagnosis, prognosis and prediction by using biomarkers in CRC patients (Review).

    Science.gov (United States)

    Nikolouzakis, Taxiarchis Konstantinos; Vassilopoulou, Loukia; Fragkiadaki, Persefoni; Mariolis Sapsakos, Theodoros; Papadakis, Georgios Z; Spandidos, Demetrios A; Tsatsakis, Aristides M; Tsiaoussis, John

    2018-06-01

    Colorectal cancer (CRC) is among the most common cancers. In fact, it is placed in the third place among the most diagnosed cancer in men, after lung and prostate cancer, and in the second one for the most diagnosed cancer in women, following breast cancer. Moreover, its high mortality rates classifies it among the leading causes of cancer‑related death worldwide. Thus, in order to help clinicians to optimize their practice, it is crucial to introduce more effective tools that will improve not only early diagnosis, but also prediction of the most likely progression of the disease and response to chemotherapy. In that way, they will be able to decrease both morbidity and mortality of their patients. In accordance with that, colon cancer research has described numerous biomarkers for diagnostic, prognostic and predictive purposes that either alone or as part of a panel would help improve patient's clinical management. This review aims to describe the most accepted biomarkers among those proposed for use in CRC divided based on the clinical specimen that is examined (tissue, faeces or blood) along with their restrictions. Lastly, new insight in CRC monitoring will be discussed presenting promising emerging biomarkers (telomerase activity, telomere length and micronuclei frequency).

  1. Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

    Science.gov (United States)

    Mazzoleni, Maurizio; Verlaan, Martin; Alfonso, Leonardo; Monego, Martina; Norbiato, Daniele; Ferri, Miche; Solomatine, Dimitri P.

    2017-02-01

    Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

  2. Improvement of Surface Temperature Prediction Using SVR with MOGREPS Data for Short and Medium range over South Korea

    Science.gov (United States)

    Lim, S. J.; Choi, R. K.; Ahn, K. D.; Ha, J. C.; Cho, C. H.

    2014-12-01

    As the Korea Meteorology Administration (KMA) has operated Met Office Global and Regional Ensemble Prediction System (MOGREPS) with introduction of Unified Model (UM), many attempts have been made to improve predictability in temperature forecast in last years. In this study, post-processing method of MOGREPS for surface temperature prediction is developed with machine learning over 52 locations in South Korea. Past 60-day lag time was used as a training phase of Support Vector Regression (SVR) method for surface temperature forecast model. The selected inputs for SVR are followings: date and surface temperatures from Numerical Weather prediction (NWP), such as GDAPS, individual 24 ensemble members, mean and median of ensemble members for every 3hours for 12 days.To verify the reliability of SVR-based ensemble prediction (SVR-EP), 93 days are used (from March 1 to May 31, 2014). The result yielded improvement of SVR-EP by RMSE value of 16 % throughout entire prediction period against conventional ensemble prediction (EP). In particular, short range predictability of SVR-EP resulted in 18.7% better RMSE for 1~3 day forecast. The mean temperature bias between SVR-EP and EP at all test locations showed around 0.36°C and 1.36°C, respectively. SVR-EP is currently extending for more vigorous sensitivity test, such as increasing training phase and optimizing machine learning model.

  3. Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

    International Nuclear Information System (INIS)

    Suhartono Nurdin; Muzzneena Ahmad Mustapha; Tukimat Lihan; Mazlan Abdul Ghaffar; Muzzneena Ahmad Mustapha; Nurdin, S.

    2015-01-01

    Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a) improves our understanding on the variability and productivity of the marine environment, which is important for exploring fishery resources. Monthly level 3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the archipelagic waters of Spermonde Indonesia were used to investigate the relationship between SST and chl-a and to forecast the potential fishing ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive correlation was also found between SST and chl-a with the catch of R. kanagurta (R=0.7, p<0.05). The potential fishing grounds of R. kanagurta were found located along the coast (at accuracy of 76.9 %). This study indicated that, with the integration of remote sensing technology, statistical modeling and geographic information systems (GIS) technique were able to determine the relationship between SST and chl-a and also able to forecast aggregation of R. kanagurta. This may contribute in decision making and reducing search hunting time and cost in fishing activities. (author)

  4. Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

    Directory of Open Access Journals (Sweden)

    Lees Jonathan G

    2008-01-01

    Full Text Available Abstract Background A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available. Results In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements. Conclusion Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained.

  5. Phylogenetic relationships in Taxodiaceae and Cupressaceae sensu stricto based on matK gene, chlL gene, trnL-trnF IGS region, and trnL intron sequences.

    Science.gov (United States)

    Kusumi, J; Tsumura, Y; Yoshimaru, H; Tachida, H

    2000-10-01

    Nucleotide sequences from four chloroplast genes, the matK, chlL, intergenic spacer (IGS) region between trnL and trnF, and an intron of trnL, were determined from all species of Taxodiaceae and five species of Cupressaceae sensu stricto (s.s.). Phylogenetic trees were constructed using the maximum parsimony and the neighbor-joining methods with Cunninghamia as an outgroup. These analyses provided greater resolution of relationships among genera and higher bootstrap supports for clades compared to previous analyses. Results indicate that Taiwania diverged first, and then Athrotaxis diverged from the remaining genera. Metasequoia, Sequoia, and Sequoiadendron form a clade. Taxodium and Glyptostrobus form a clade, which is the sister to Cryptomeria. Cupressaceae s.s. are derived from within Taxodiaceae, being the most closely related to the Cryptomeria/Taxodium/Glyptostrobus clade. These relationships are consistent with previous morphological groupings and the analyses of molecular data. In addition, we found acceleration of evolutionary rates in Cupressaceae s.s. Possible causes for the acceleration are discussed.

  6. Risk prediction is improved by adding markers of subclinical organ damage to SCORE

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Jeppesen, Jørgen; Hansen, Tine W

    2010-01-01

    cardiovascular, anti-diabetic, or lipid-lowering treatment, aged 41, 51, 61, or 71 years, we measured traditional cardiovascular risk factors, left ventricular (LV) mass index, atherosclerotic plaques in the carotid arteries, carotid/femoral pulse wave velocity (PWV), and urine albumin/creatinine ratio (UACR......) and followed them for a median of 12.8 years. Eighty-one subjects died because of cardiovascular causes. Risk of cardiovascular death was independently of SCORE associated with LV hypertrophy [hazard ratio (HR) 2.2 (95% CI 1.2-4.0)], plaques [HR 2.5 (1.6-4.0)], UACR > or = 90th percentile [HR 3.3 (1.......07). CONCLUSION: Subclinical organ damage predicted cardiovascular death independently of SCORE and the combination may improve risk prediction....

  7. Improving ELM-Based Service Quality Prediction by Concise Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yuhai Zhao

    2015-01-01

    Full Text Available Web services often run on highly dynamic and changing environments, which generate huge volumes of data. Thus, it is impractical to monitor the change of every QoS parameter for the timely trigger precaution due to high computational costs associated with the process. To address the problem, this paper proposes an active service quality prediction method based on extreme learning machine. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Second, by the proposed EC rules, we are enabled to trigger the precaution of QoS as soon as possible with high confidence. An efficient prefix tree based mining algorithm together with some effective pruning rules is developed to mine such rules. Finally, we study how to extract a set of diversified features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that ELM trained by the selected feature subsets can efficiently improve the reliability and the earliness of service quality prediction.

  8. Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

    Science.gov (United States)

    Hu, Chuanmin; Lee, Zhongping; Franz, Bryan

    2011-01-01

    A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

  9. Improving behavioral performance under full attention by adjusting response criteria to changes in stimulus predictability.

    Science.gov (United States)

    Katzner, Steffen; Treue, Stefan; Busse, Laura

    2012-09-04

    One of the key features of active perception is the ability to predict critical sensory events. Humans and animals can implicitly learn statistical regularities in the timing of events and use them to improve behavioral performance. Here, we used a signal detection approach to investigate whether such improvements in performance result from changes of perceptual sensitivity or rather from adjustments of a response criterion. In a regular sequence of briefly presented stimuli, human observers performed a noise-limited motion detection task by monitoring the stimulus stream for the appearance of a designated target direction. We manipulated target predictability through the hazard rate, which specifies the likelihood that a target is about to occur, given it has not occurred so far. Analyses of response accuracy revealed that improvements in performance could be accounted for by adjustments of the response criterion; a growing hazard rate was paralleled by an increasing tendency to report the presence of a target. In contrast, the hazard rate did not affect perceptual sensitivity. Consistent with previous research, we also found that reaction time decreases as the hazard rate grows. A simple rise-to-threshold model could well describe this decrease and attribute predictability effects to threshold adjustments rather than changes in information supply. We conclude that, even under conditions of full attention and constant perceptual sensitivity, behavioral performance can be optimized by dynamically adjusting the response criterion to meet ongoing changes in the likelihood of a target.

  10. Does early change predict long-term (6 months) improvements in subjects who receive manual therapy for low back pain?

    Science.gov (United States)

    Cook, Chad; Petersen, Shannon; Donaldson, Megan; Wilhelm, Mark; Learman, Ken

    2017-09-01

    Early change is commonly assessed for manual therapy interventions and has been used to determine treatment appropriateness. However, current studies have only explored the relationship of between or within-session changes and short-/medium-term outcomes. The goal of this study was to determine whether pain changes after two weeks of pragmatic manual therapy could predict those participants with chronic low back pain who demonstrate continued improvements at 6-month follow-up. This study was a retrospective observational design. Univariate logistic regression analyses were performed using a 33% and a 50% pain change to predict improvement. Those who experienced a ≥33% pain reduction by 2 weeks had 6.98 (95% CI = 1.29, 37.53) times higher odds of 50% improvement on the GRoC and 4.74 (95% CI = 1.31, 17.17) times higher odds of 50% improvement on the ODI (at 6 months). Subjects who reported a ≥50% pain reduction at 2 weeks had 5.98 (95% CI = 1.56, 22.88) times higher odds of a 50% improvement in the GRoC and 3.99 (95% CI = 1.23, 12.88) times higher odds of a 50% improvement in the ODI (at 6 months). Future studies may investigate whether a change in plan of care is beneficial for patients who are not showing early improvement predictive of a good long-term outcome.

  11. Improving prediction of fall risk among nursing home residents using electronic medical records.

    Science.gov (United States)

    Marier, Allison; Olsho, Lauren E W; Rhodes, William; Spector, William D

    2016-03-01

    Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls. The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data. In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification. Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. The Potential of Satellite Imagery to Estimate Chlorophyll-a and Water Clarity Data For the Assessment of Lake Water Quality

    Science.gov (United States)

    Shrift, M.; Weathers, K. C.; Norouzi, H.; Ewing, H. A.

    2017-12-01

    Lake water quality is declining nationwide and has become a tremendous point of interest. Remote sensing (RS) data have provided the ability to efficiently study oceans and terrestrial systems over space and time. However, fresh water systems, especially small, nutrient poor lakes have only recently been assessed using remote sensing technology. Prior research suggests that there is poor satellite sensitivity to lakes with low chlorophyll a (chl a) values. This study focuses on the potential to utilize Landsat 8 satellite imagery to predict chl a and Secchi disk transparency values from Lake Auburn, Maine, an oligo-mesotrophic lake that is the primary source of drinking water for the cities of Lewiston and Auburn and has had an increasing number of algal blooms. A total of 28 Landsat scenes from 2013-2017 within 4 days of in-lake measurements were collected for band value extraction and radiometric correction. Band combinations were explored and analyzed to obtain the most reliable prediction of in-lake chl a and Secchi disk values. A nonlinear combination of bands 5 and 4 for chl a, and bands 3 and 2 for Secchi disk transparency show the most promising algorithms, with correlations coefficients of 0.57 and 0.74, respectively. The resultant algorithms show promise for utilizing RS data to estimate water quality for a large array of low-nutrient lakes in northern North America, and thereby to gain a better understanding of water quality of our vital fresh water resources.

  13. Genomic selection: genome-wide prediction in plant improvement.

    Science.gov (United States)

    Desta, Zeratsion Abera; Ortiz, Rodomiro

    2014-09-01

    Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. The film boiling look-up table: an improvement in predicting post-chf temperatures

    International Nuclear Information System (INIS)

    Groeneveld, D.C.; Leung, L.K.H.; Vasic, A.Z.; Guo, Y.J.; El Nakla, M.; Cheng, S.C.

    2002-01-01

    During the past 50 years more than 60 film boiling prediction methods have been proposed (Groeneveld and Leung, 2000). These prediction methods generally are applicable over limited ranges of flow conditions and do not provide reasonable predictions when extrapolated well outside the range of their respective database. Leung et al. (1996, 1997) and Kirillov et al. (1996) have proposed the use of a film-boiling look-up table as an alternative to the many models, equations and correlations for the inverted annular film boiling (IAFB) and the dispersed flow film-boiling (DFFB) regime. The film-boiling look-up table is a logical follow-up to the development of the successful CHF look-up table (Groeneveld et al., 1996). It is basically a normalized data bank of heat-transfer coefficients for discrete values of pressure, mass flux, quality and heat flux or surface-temperature. The look-up table proposed by Leung et al. (1996, 1997), and referred to as PDO-LW-96, was based on 14,687 data and predicted the surface temperature with an average error of 1.2% and an rms error of 6.73%. The heat-transfer coefficient was predicted with an average error of -4.93% and an rms error of 16.87%. Leung et al. clearly showed that the look-up table approach, as a general predictive tool for film-boiling heat transfer, was superior to the correlation or model approach. Error statistics were not provided for the look-up table proposed by Kirillov et al. (1996). This paper reviews the look-up table approach and describes improvements to the derivation of the film-boiling look-up table. These improvements include: (i) a larger data base, (ii) a wider range of thermodynamic qualities, (iii) use of the wall temperature instead of the heat flux as an independent parameter, (iv) employment of fully-developed film-boiling data only for the derivation of the look-up table, (v) a finer subdivision and thus more table entries, (vi) smoother table, and (vii) use of the best of five prediction methods

  15. An Improved Algorithm for Predicting Free Recalls

    Science.gov (United States)

    Laming, Donald

    2008-01-01

    Laming [Laming, D. (2006). "Predicting free recalls." "Journal of Experimental Psychology: Learning, Memory, and Cognition," 32, 1146-1163] has shown that, in a free-recall experiment in which the participants rehearsed out loud, entire sequences of recalls could be predicted, to a useful degree of precision, from the prior sequences of stimuli…

  16. Variation of particulate organic carbon and its relationship with bio-optical properties during a phytoplankton bloom in the Pearl River estuary

    International Nuclear Information System (INIS)

    Wang Guifen; Zhou Wen; Cao Wenxi; Yin Jianping; Yang Yuezhong; Sun Zhaohua; Zhang Yuanzhi; Zhao Jun

    2011-01-01

    Highlights: → A study about relationship between POC and optical properties during a phytoplankton bloom. → Empirical algorithms for retrieving POC concentration from optical data were developed. → Phytoplankton carbon and it's ratio to Chl-a are estimated and discussed. → Demonstrates that marine optical buoy can be a new platform for monitoring biogeochemical cycle. - Abstract: In this study, variations in the particulate organic carbon (POC) were monitored during a phytoplankton bloom event, and the corresponding changes in bio-optical properties were tracked at one station (114.29 o E, 22.06 o N) located in the Pearl River estuary. A greater than 10-fold increase in POC (112.29-1173.36 mg m -3 ) was observed during the bloom, with the chlorophyll a concentration (Chl-a) varying from 0.984 to 25.941 mg m -3 . A power law function is used to describe the relationship between POC and Chl-a, and the POC:Chl-a ratio tends to change inversely with Chl-a. Phytoplankton carbon concentration is indirectly estimated using the conceptual model proposed by , and this carbon is found to contribute 47.21% (±10.65%) to total POC. The estimated carbon-to-chlorophyll ratio of phytoplankton in diatom-dominated waters is found to be comparable with results reported in the literature. Empirical algorithms for determining the concentrations of Chl-a and POC were developed based on the relationships of these variables with the blue-to-green reflectance ratio. With these bio-optical models, the levels of particulate organic carbon and Chl-a could be predicted from the radiometric data measured by a marine optical buoy, which showed much more detailed information about the variability in biogeochemical parameters during this bloom event.

  17. An Improved User Selection Algorithm in Multiuser MIMO Broadcast with Channel Prediction

    Science.gov (United States)

    Min, Zhi; Ohtsuki, Tomoaki

    In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. Unfortunately, the high calculation cost of the optimal algorithm prevents its implementation. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. We also apply a channel prediction technique to a MIMO-BC system to get more accurate channel information at the transmitter. Simulation results show that the channel prediction can improve the accuracy of channel information for user selections, and the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. Also we discuss the setting of the algorithm threshold. As the result of a discussion on the calculation complexity, which uses the number of complex multiplications as the parameter, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.

  18. Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates

    Directory of Open Access Journals (Sweden)

    M. Ardyna

    2013-06-01

    Full Text Available Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC. In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs that significantly contribute to primary production (PP are often observed. These are neither detected by ocean color sensors nor accounted for in the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e., 5206 stations and develop an empirical model to estimate vertical chlorophyll a (Chl a according to (1 the shelf–offshore gradient delimited by the 50 m isobath, (2 seasonal variability along pre-bloom, post-bloom, and winter periods, and (3 regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface Chl a concentration (Chl asurf; 0.7–30 mg m−3 throughout the open water period, the Chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when Chl asurf is low (0–0.5 mg m−3. By applying our empirical model to annual Chl asurf time series, instead of the conventional method assuming vertically homogenous Chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in Chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e., pre-bloom, post-bloom > 0.7 mg m−3, and the winter period somehow compensate for the underestimates found when SCMs are deep (i.e., post-bloom −3. SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.

  19. Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

    Directory of Open Access Journals (Sweden)

    Osman Yildiz

    2013-12-01

    Full Text Available It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance.

  20. Has growth mixture modeling improved our understanding of how early change predicts psychotherapy outcome?

    Science.gov (United States)

    Koffmann, Andrew

    2017-03-02

    Early change in psychotherapy predicts outcome. Seven studies have used growth mixture modeling [GMM; Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class-latent growth modeling. In L. M. Collins & A. G. Sawyers (Eds.), New methods for the analysis of change (pp. 291-322). Washington, DC: American Psychological Association] to identify patient classes based on early change but have yielded conflicting results. Here, we review the earlier studies and apply GMM to a new data set. In a university-based training clinic, 251 patients were administered the Outcome Questionnaire-45 [Lambert, M. J., Hansen, N. B., Umphress, V., Lunnen, K., Okiishi, J., Burlingame, G., … Reisinger, C. W. (1996). Administration and scoring manual for the Outcome Questionnaire (OQ 45.2). Wilmington, DE: American Professional Credentialing Services] at each psychotherapy session. We used GMM to identify class structure based on change in the first six sessions and examined trajectories as predictors of outcome. The sample was best described as a single class. There was no evidence of autoregressive trends in the data. We achieved better fit to the data by permitting latent variables some degree of kurtosis, rather than to assume multivariate normality. Treatment outcome was predicted by the amount of early improvement, regardless of initial level of distress. The presence of sudden early gains or losses did not further improve outcome prediction. Early improvement is an easily computed, powerful predictor of psychotherapy outcome. The use of GMM to investigate the relationship between change and outcome is technically complex and computationally intensive. To date, it has not been particularly informative.

  1. Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson’s Disease

    Science.gov (United States)

    Ridgel, Angela L.; Abdar, Hassan Mohammadi; Alberts, Jay L.; Discenzo, Fred M.; Loparo, Kenneth A.

    2014-01-01

    Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease. PMID:23144045

  2. Controversy and discussion on blood supply and interventional therapy of cavernous hemangiomas of the liver

    International Nuclear Information System (INIS)

    Ouyang Yong; Wang Ying; Ouyang Xuehui; Yu Ming

    2004-01-01

    hepatic artery angiography or CTHA, it was filled very slowly by the arterial blood containing contrast media and was difficult to opacify or enhance. Simultaneously this could result in a low pressure of abnormal sinusoids. When the sinusoidal pressure was lower than that of the connecting portal venules, the portal venous blood containing contrast media could easily flow into the abnormal sinusoids and make it enhanced during the direct or indirect portography (or CTAP). Conclusion: CHL is really a congenital venous malformation. All the CHL with high flow and some CHL with intermediate flow are surely supplied by the hepatic artery and drained primarily by the peripheral branches of portal vein. However, in few CHL with marked lower flow, the portal vein should become a primary supply vessel, so a direct or indirect portography (or CTAP) must often be taken to identify the diagnosis. Thereby, the technique of transcatheter embolization of CHL including the aim, indication, approach, and the used sclerotic or embolic drugs, etc, should also be reconsidered in order to improve its therapeutic efficacy. (authors)

  3. Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

    Science.gov (United States)

    Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

    2012-01-01

    In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

  4. Improved Seasonal Prediction of European Summer Temperatures With New Five-Layer Soil-Hydrology Scheme

    Science.gov (United States)

    Bunzel, Felix; Müller, Wolfgang A.; Dobrynin, Mikhail; Fröhlich, Kristina; Hagemann, Stefan; Pohlmann, Holger; Stacke, Tobias; Baehr, Johanna

    2018-01-01

    We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.

  5. A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms

    International Nuclear Information System (INIS)

    Mishra, S; Mishra, D R

    2014-01-01

    We present a novel three-band algorithm (PC 3 ) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC 3 calibration and validation were acquired from highly turbid productive catfish aquaculture ponds. Since the characteristic PC absorption feature at 620 nm is contaminated with residual chlorophyll-a (Chl-a) absorption, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating Chl-a absorption at 620 nm–665 nm enables PC 3 to compensate for the confounding effect of Chl-a at the PC absorption band and considerably increases the accuracy of the PC prediction algorithm. In the current dataset, PC 3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC 3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μg L −1 ). Therefore, introduction of PC 3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms. (letter)

  6. Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

    DEFF Research Database (Denmark)

    Gebreyesus, Grum; Lund, Mogens Sandø; Buitenhuis, Albert Johannes

    2017-01-01

    Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci...... of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we...... developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls...

  7. Improved techniques for predicting spacecraft power

    International Nuclear Information System (INIS)

    Chmielewski, A.B.

    1987-01-01

    Radioisotope Thermoelectric Generators (RTGs) are going to supply power for the NASA Galileo and Ulysses spacecraft now scheduled to be launched in 1989 and 1990. The duration of the Galileo mission is expected to be over 8 years. This brings the total RTG lifetime to 13 years. In 13 years, the RTG power drops more than 20 percent leaving a very small power margin over what is consumed by the spacecraft. Thus it is very important to accurately predict the RTG performance and be able to assess the magnitude of errors involved. The paper lists all the error sources involved in the RTG power predictions and describes a statistical method for calculating the tolerance

  8. At the Nexus of History, Ecology, and Hydrobiogeochemistry: Improved Predictions across Scales through Integration.

    Science.gov (United States)

    Stegen, James C

    2018-01-01

    To improve predictions of ecosystem function in future environments, we need to integrate the ecological and environmental histories experienced by microbial communities with hydrobiogeochemistry across scales. A key issue is whether we can derive generalizable scaling relationships that describe this multiscale integration. There is a strong foundation for addressing these challenges. We have the ability to infer ecological history with null models and reveal impacts of environmental history through laboratory and field experimentation. Recent developments also provide opportunities to inform ecosystem models with targeted omics data. A major next step is coupling knowledge derived from such studies with multiscale modeling frameworks that are predictive under non-steady-state conditions. This is particularly true for systems spanning dynamic interfaces, which are often hot spots of hydrobiogeochemical function. We can advance predictive capabilities through a holistic perspective focused on the nexus of history, ecology, and hydrobiogeochemistry.

  9. Fine mapping and candidate gene analysis of the virescent gene v 1 in Upland cotton (Gossypium hirsutum).

    Science.gov (United States)

    Mao, Guangzhi; Ma, Qiang; Wei, Hengling; Su, Junji; Wang, Hantao; Ma, Qifeng; Fan, Shuli; Song, Meizhen; Zhang, Xianlong; Yu, Shuxun

    2018-02-01

    The young leaves of virescent mutants are yellowish and gradually turn green as the plants reach maturity. Understanding the genetic basis of virescent mutants can aid research of the regulatory mechanisms underlying chloroplast development and chlorophyll biosynthesis, as well as contribute to the application of virescent traits in crop breeding. In this study, fine mapping was employed, and a recessive gene (v 1 ) from a virescent mutant of Upland cotton was narrowed to an 84.1-Kb region containing ten candidate genes. The GhChlI gene encodes the cotton Mg-chelatase I subunit (CHLI) and was identified as the candidate gene for the virescent mutation using gene annotation. BLAST analysis showed that the GhChlI gene has two copies, Gh_A10G0282 and Gh_D10G0283. Sequence analysis indicated that the coding region (CDS) of GhChlI is 1269 bp in length, with three predicted exons and one non-synonymous nucleotide mutation (G1082A) in the third exon of Gh_D10G0283, with an amino acid (AA) substitution of arginine (R) to lysine (K). GhChlI-silenced TM-1 plants exhibited a lower GhChlI expression level, a lower chlorophyll content, and the virescent phenotype. Analysis of upstream regulatory elements and expression levels of GhChlI showed that the expression quantity of GhChlI may be normal, and with the development of the true leaf, the increase in the Gh_A10G0282 dosage may partially make up for the deficiency of Gh_D10G0283 in the v 1 mutant. Phylogenetic analysis and sequence alignment revealed that the protein sequence encoded by the third exon of GhChlI is highly conserved across diverse plant species, in which AA substitutions among the completely conserved residues frequently result in changes in leaf color in various species. These results suggest that the mutation (G1082A) within the GhChlI gene may cause a functional defect of the GhCHLI subunit and thus the virescent phenotype in the v 1 mutant. The GhChlI mutation not only provides a tool for understanding the

  10. The impact of modifying photosystem antenna size on canopy photosynthetic efficiency-Development of a new canopy photosynthesis model scaling from metabolism to canopy level processes.

    Science.gov (United States)

    Song, Qingfeng; Wang, Yu; Qu, Mingnan; Ort, Donald R; Zhu, Xin-Guang

    2017-12-01

    Canopy photosynthesis (A c ) describes photosynthesis of an entire crop field and the daily and seasonal integrals of A c positively correlate with daily and seasonal biomass production. Much effort in crop breeding has focused on improving canopy architecture and hence light distribution inside the canopy. Here, we develop a new integrated canopy photosynthesis model including canopy architecture, a ray tracing algorithm, and C 3 photosynthetic metabolism to explore the option of manipulating leaf chlorophyll concentration ([Chl]) for greater A c and nitrogen use efficiency (NUE). Model simulation results show that (a) efficiency of photosystem II increased when [Chl] was decreased by decreasing antenna size and (b) the light received by leaves at the bottom layers increased when [Chl] throughout the canopy was decreased. Furthermore, the modelling revealed a modest ~3% increase in A c and an ~14% in NUE was accompanied when [Chl] reduced by 60%. However, if the leaf nitrogen conserved by this decrease in leaf [Chl] were to be optimally allocated to other components of photosynthesis, both A c and NUE can be increased by over 30%. Optimizing [Chl] coupled with strategic reinvestment of conserved nitrogen is shown to have the potential to support substantial increases in A c , biomass production, and crop yields. © 2017 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

  11. Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

    Science.gov (United States)

    Zhang, Haicang; Gao, Yujuan; Deng, Minghua; Wang, Chao; Zhu, Jianwei; Li, Shuai Cheng; Zheng, Wei-Mou; Bu, Dongbo

    2016-03-25

    Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates

  12. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  13. CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva

    NARCIS (Netherlands)

    van Dijk, Lisanne V.; Brouwer, Charlotte L.; van der Schaaf, Arjen; Burgerhof, Johannes G. M.; Beukinga, Roelof J.; Langendijk, Johannes A.; Sijtsema, Nanna M.; Steenbakkers, Roel J. H. M.

    Background and purpose: Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER12m) and sticky saliva (STIC12m) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XERbase) or sticky saliva (STICbase) scores. The purpose is to improve

  14. Pulmonary edema predictive scoring index (PEPSI), a new index to predict risk of reperfusion pulmonary edema and improvement of hemodynamics in percutaneous transluminal pulmonary angioplasty.

    Science.gov (United States)

    Inami, Takumi; Kataoka, Masaharu; Shimura, Nobuhiko; Ishiguro, Haruhisa; Yanagisawa, Ryoji; Taguchi, Hiroki; Fukuda, Keiichi; Yoshino, Hideaki; Satoh, Toru

    2013-07-01

    This study sought to identify useful predictors for hemodynamic improvement and risk of reperfusion pulmonary edema (RPE), a major complication of this procedure. Percutaneous transluminal pulmonary angioplasty (PTPA) has been reported to be effective for the treatment of chronic thromboembolic pulmonary hypertension (CTEPH). PTPA has not been widespread because RPE has not been well predicted. We included 140 consecutive procedures in 54 patients with CTEPH. The flow appearance of the target vessels was graded into 4 groups (Pulmonary Flow Grade), and we proposed PEPSI (Pulmonary Edema Predictive Scoring Index) = (sum total change of Pulmonary Flow Grade scores) × (baseline pulmonary vascular resistance). Correlations between occurrence of RPE and 11 variables, including hemodynamic parameters, number of target vessels, and PEPSI, were analyzed. Hemodynamic parameters significantly improved after median observation period of 6.4 months, and the sum total changes in Pulmonary Flow Grade scores were significantly correlated with the improvement in hemodynamics. Multivariate analysis revealed that PEPSI was the strongest factor correlated with the occurrence of RPE (p PEPSI to be a useful marker of the risk of RPE (cutoff value 35.4, negative predictive value 92.3%). Pulmonary Flow Grade score is useful in determining therapeutic efficacy, and PEPSI is highly supportive to reduce the risk of RPE after PTPA. Using these 2 indexes, PTPA could become a safe and common therapeutic strategy for CTEPH. Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  15. Climatic extremes improve predictions of spatial patterns of tree species

    Science.gov (United States)

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

    2009-01-01

    Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

  16. Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sigeti, David E. [Los Alamos National Laboratory; Pelak, Robert A. [Los Alamos National Laboratory

    2012-09-11

    We present a Bayesian statistical methodology for identifying improvement in predictive simulations, including an analysis of the number of (presumably expensive) simulations that will need to be made in order to establish with a given level of confidence that an improvement has been observed. Our analysis assumes the ability to predict (or postdict) the same experiments with legacy and new simulation codes and uses a simple binomial model for the probability, {theta}, that, in an experiment chosen at random, the new code will provide a better prediction than the old. This model makes it possible to do statistical analysis with an absolute minimum of assumptions about the statistics of the quantities involved, at the price of discarding some potentially important information in the data. In particular, the analysis depends only on whether or not the new code predicts better than the old in any given experiment, and not on the magnitude of the improvement. We show how the posterior distribution for {theta} may be used, in a kind of Bayesian hypothesis testing, both to decide if an improvement has been observed and to quantify our confidence in that decision. We quantify the predictive probability that should be assigned, prior to taking any data, to the possibility of achieving a given level of confidence, as a function of sample size. We show how this predictive probability depends on the true value of {theta} and, in particular, how there will always be a region around {theta} = 1/2 where it is highly improbable that we will be able to identify an improvement in predictive capability, although the width of this region will shrink to zero as the sample size goes to infinity. We show how the posterior standard deviation may be used, as a kind of 'plan B metric' in the case that the analysis shows that {theta} is close to 1/2 and argue that such a plan B should generally be part of hypothesis testing. All the analysis presented in the paper is done with a

  17. A Model Predictive Control Approach for Fuel Economy Improvement of a Series Hydraulic Hybrid Vehicle

    Directory of Open Access Journals (Sweden)

    Tri-Vien Vu

    2014-10-01

    Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.

  18. Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.

    Science.gov (United States)

    Srinivas, T R; Taber, D J; Su, Z; Zhang, J; Mour, G; Northrup, D; Tripathi, A; Marsden, J E; Moran, W P; Mauldin, P D

    2017-03-01

    We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient-level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow-up data (0-90 days posttransplant; structured and unstructured for 1-year models; data up to 1 year for 3-year models) on adult solitary kidney transplant recipients transplanted during 2007-2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3-year GL rate was observed among 891 patients (2007-2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61-0.74); Model 3: AUC, 0.72; (95% CI: 0.66-077); Model 4: AUC, 0.84, (95 % CI: 0.79-0.89). One-year GL (AUC, 0.87; Model 4) and 3-year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes. © 2016 The American Society of Transplantation and the American Society of Transplant Surgeons.

  19. Applying a health action model to predict and improve healthy behaviors in coal miners.

    Science.gov (United States)

    Vahedian-Shahroodi, Mohammad; Tehrani, Hadi; Mohammadi, Faeze; Gholian-Aval, Mahdi; Peyman, Nooshin

    2018-05-01

    One of the most important ways to prevent work-related diseases in occupations such as mining is to promote healthy behaviors among miners. This study aimed to predict and promote healthy behaviors among coal miners by using a health action model (HAM). The study was conducted on 200 coal miners in Iran in two steps. In the first step, a descriptive study was implemented to determine predictive constructs and effectiveness of HAM on behavioral intention. The second step involved a quasi-experimental study to determine the effect of an HAM-based education intervention. This intervention was implemented by the researcher and the head of the safety unit based on the predictive construct specified in the first step over 12 sessions of 60 min. The data was collected using an HAM questionnaire and a checklist of healthy behavior. The results of the first step of the study showed that attitude, belief, and normative constructs were meaningful predictors of behavioral intention. Also, the results of the second step revealed that the mean score of attitude and behavioral intention increased significantly after conducting the intervention in the experimental group, while the mean score of these constructs decreased significantly in the control group. The findings of this study showed that HAM-based educational intervention could improve the healthy behaviors of mine workers. Therefore, it is recommended to extend the application of this model to other working groups to improve healthy behaviors.

  20. Can survival prediction be improved by merging gene expression data sets?

    Directory of Open Access Journals (Sweden)

    Haleh Yasrebi

    Full Text Available BACKGROUND: High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS: Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS: Merging did not deteriorate performance on average despite (a The diversity of microarray platforms used. (b The heterogeneity of patients cohorts. (c The heterogeneity of breast cancer disease. (d Substantial variation of time to death or relapse. (e The reduced number of genes in the merged data

  1. Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

    Science.gov (United States)

    Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

    2017-04-01

    Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Towards improved hydrologic predictions using data assimilation techniques for water resource management at the continental scale

    Science.gov (United States)

    Naz, Bibi; Kurtz, Wolfgang; Kollet, Stefan; Hendricks Franssen, Harrie-Jan; Sharples, Wendy; Görgen, Klaus; Keune, Jessica; Kulkarni, Ketan

    2017-04-01

    More accurate and reliable hydrologic simulations are important for many applications such as water resource management, future water availability projections and predictions of extreme events. However, simulation of spatial and temporal variations in the critical water budget components such as precipitation, snow, evaporation and runoff is highly uncertain, due to errors in e.g. model structure and inputs (hydrologic parameters and forcings). In this study, we use data assimilation techniques to improve the predictability of continental-scale water fluxes using in-situ measurements along with remotely sensed information to improve hydrologic predications for water resource systems. The Community Land Model, version 3.5 (CLM) integrated with the Parallel Data Assimilation Framework (PDAF) was implemented at spatial resolution of 1/36 degree (3 km) over the European CORDEX domain. The modeling system was forced with a high-resolution reanalysis system COSMO-REA6 from Hans-Ertel Centre for Weather Research (HErZ) and ERA-Interim datasets for time period of 1994-2014. A series of data assimilation experiments were conducted to assess the efficiency of assimilation of various observations, such as river discharge data, remotely sensed soil moisture, terrestrial water storage and snow measurements into the CLM-PDAF at regional to continental scales. This setup not only allows to quantify uncertainties, but also improves streamflow predictions by updating simultaneously model states and parameters utilizing observational information. The results from different regions, watershed sizes, spatial resolutions and timescales are compared and discussed in this study.

  3. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

    Science.gov (United States)

    Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

    2017-01-01

    Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

  4. Interpreting Disruption Prediction Models to Improve Plasma Control

    Science.gov (United States)

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

  5. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

    Science.gov (United States)

    Marks, Claire; Nowak, Jaroslaw; Klostermann, Stefan; Georges, Guy; Dunbar, James; Shi, Jiye; Kelm, Sebastian; Deane, Charlotte M

    2017-05-01

    Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

  6. Synthesis of chlorophyll b: Localization of chlorophyllide a oxygenase and discovery of a stable radical in the catalytic subunit

    Science.gov (United States)

    Eggink, Laura L; LoBrutto, Russell; Brune, Daniel C; Brusslan, Judy; Yamasato, Akihiro; Tanaka, Ayumi; Hoober, J Kenneth

    2004-01-01

    Background Assembly of stable light-harvesting complexes (LHCs) in the chloroplast of green algae and plants requires synthesis of chlorophyll (Chl) b, a reaction that involves oxygenation of the 7-methyl group of Chl a to a formyl group. This reaction uses molecular oxygen and is catalyzed by chlorophyllide a oxygenase (CAO). The amino acid sequence of CAO predicts mononuclear iron and Rieske iron-sulfur centers in the protein. The mechanism of synthesis of Chl b and localization of this reaction in the chloroplast are essential steps toward understanding LHC assembly. Results Fluorescence of a CAO-GFP fusion protein, transiently expressed in young pea leaves, was found at the periphery of mature chloroplasts and on thylakoid membranes by confocal fluorescence microscopy. However, when membranes from partially degreened cells of Chlamydomonas reinhardtii cw15 were resolved on sucrose gradients, full-length CAO was detected by immunoblot analysis only on the chloroplast envelope inner membrane. The electron paramagnetic resonance spectrum of CAO included a resonance at g = 4.3, assigned to the predicted mononuclear iron center. Instead of a spectrum of the predicted Rieske iron-sulfur center, a nearly symmetrical, approximately 100 Gauss peak-to-trough signal was observed at g = 2.057, with a sensitivity to temperature characteristic of an iron-sulfur center. A remarkably stable radical in the protein was revealed by an isotropic, 9 Gauss peak-to-trough signal at g = 2.0042. Fragmentation of the protein after incorporation of 125I- identified a conserved tyrosine residue (Tyr-422 in Chlamydomonas and Tyr-518 in Arabidopsis) as the radical species. The radical was quenched by chlorophyll a, an indication that it may be involved in the enzymatic reaction. Conclusion CAO was found on the chloroplast envelope and thylakoid membranes in mature chloroplasts but only on the envelope inner membrane in dark-grown C. reinhardtii cells. Such localization provides further

  7. Improvements in disruption prediction at ASDEX Upgrade

    Energy Technology Data Exchange (ETDEWEB)

    Aledda, R., E-mail: raffaele.aledda@diee.unica.it; Cannas, B., E-mail: cannas@diee.unica.it; Fanni, A., E-mail: fanni@diee.unica.it; Pau, A., E-mail: alessandro.pau@diee.unica.it; Sias, G., E-mail: giuliana.sias@diee.unica.it

    2015-10-15

    Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

  8. Improvements in disruption prediction at ASDEX Upgrade

    International Nuclear Information System (INIS)

    Aledda, R.; Cannas, B.; Fanni, A.; Pau, A.; Sias, G.

    2015-01-01

    Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

  9. TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

    International Nuclear Information System (INIS)

    Krafft, S; Briere, T; Court, L; Martel, M

    2015-01-01

    Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. A total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP

  10. Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

    Science.gov (United States)

    Zhang, Zipeng; Fan, Tao; Wang, Shuqing

    It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

  11. Considering Organic Carbon for Improved Predictions of Clay Content from Water Vapor Sorption

    DEFF Research Database (Denmark)

    Arthur, Emmanuel; Tuller, Markus; Moldrup, Per

    2014-01-01

    Accurate determination of the soil clay fraction (CF) is of crucial importance for characterization of numerous environmental, agricultural, and engineering processes. Because traditional methods for measurement of the CF are laborious and susceptible to errors, regression models relating the CF...... to water vapor sorption isotherms that can be rapidly measured with a fully automated vapor sorption analyzer are a viable alternative. In this presentation we evaluate the performance of recently developed regression models based on comparison with standard CF measurements for soils with high organic...... carbon (OC) content and propose a modification to improve prediction accuracy. Evaluation of the CF prediction accuracy for 29 soils with clay contents ranging from 6 to 25% and with OC contents from 2.0 to 8.4% showed that the models worked reasonably well for all soils when the OC content was below 2...

  12. Activity Prediction of Schiff Base Compounds using Improved QSAR Models of Cinnamaldehyde Analogues and Derivatives

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2015-10-01

    Full Text Available In past work, QSAR (quantitative structure-activity relationship models of cinnamaldehyde analogues and derivatives (CADs have been used to predict the activities of new chemicals based on their mass concentrations, but these approaches are not without shortcomings. Therefore, molar concentrations were used instead of mass concentrations to determine antifungal activity. New QSAR models of CADs against Aspergillus niger and Penicillium citrinum were established, and the molecular design of new CADs was performed. The antifungal properties of the designed CADs were tested, and the experimental Log AR values were in agreement with the predicted Log AR values. The results indicate that the improved QSAR models are more reliable and can be effectively used for CADs molecular design and prediction of the activity of CADs. These findings provide new insight into the development and utilization of cinnamaldehyde compounds.

  13. Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.

    Science.gov (United States)

    Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris

    2017-01-01

    This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables "metabolic rate," and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data ( n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated.

  14. Partition dataset according to amino acid type improves the prediction of deleterious non-synonymous SNPs

    International Nuclear Information System (INIS)

    Yang, Jing; Li, Yuan-Yuan; Li, Yi-Xue; Ye, Zhi-Qiang

    2012-01-01

    Highlights: ► Proper dataset partition can improve the prediction of deleterious nsSNPs. ► Partition according to original residue type at nsSNP is a good criterion. ► Similar strategy is supposed promising in other machine learning problems. -- Abstract: Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning methods have been applied to train classifiers for sorting disease-associated nsSNPs from neutral ones. The continuously accumulated nsSNP data allows us to further explore better prediction approaches. In this work, we partitioned the training data into 20 subsets according to either original or substituted amino acid type at the nsSNP site. Using support vector machine (SVM), training classification models on each subset resulted in an overall accuracy of 76.3% or 74.9% depending on the two different partition criteria, while training on the whole dataset obtained an accuracy of only 72.6%. Moreover, the dataset was also randomly divided into 20 subsets, but the corresponding accuracy was only 73.2%. Our results demonstrated that partitioning the whole training dataset into subsets properly, i.e., according to the residue type at the nsSNP site, will improve the performance of the trained classifiers significantly, which should be valuable in developing better tools for predicting the disease-association of nsSNPs.

  15. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  16. An improved model to predict nonuniform deformation of Zr-2.5 Nb pressure tubes

    International Nuclear Information System (INIS)

    Lei, Q.M.; Fan, H.Z.

    1997-01-01

    Present circular pressure-tube ballooning models in most fuel channel codes assume that the pressure tube remains circular during ballooning. This model provides adequate predictions of pressure-tube ballooning behaviour when the pressure tube (PT) and the calandria tube (CT) are concentric and when a small (<100 degrees C) top-to-bottom circumferential temperature gradient is present on the pressure tube. However, nonconcentric ballooning is expected to occur under certain postulated CANDU (CANada Deuterium Uranium) accident conditions. This circular geometry assumption prevents the model from accurately predicting nonuniform pressure-tube straining and local PT/CT contact when the pressure tube is subjected to a large circumferential temperature gradient and consequently deforms in a noncircular pattern. This paper describes an improved model that predicts noncircular pressure-tube deformation. Use of this model (once fully validated) will reduce uncertainties in the prediction of pressure-tube ballooning during a postulated loss-of-coolant accident (LOCA) in a CANDU reactor. The noncircular deformation model considers a ring or cross-section of a pressure tube with unit axial length to calculate deformation in the radial and circumferential directions. The model keeps track of the thinning of the pressure-tube wall as well as the shape deviation from a reference circle. Such deviation is expressed in a cosine Fourier series for the lateral symmetry case. The coefficients of the series for the first m terms are calculated by solving a set of algebraic equations at each time step. The model also takes into account the effects of pressure-tube sag or bow on ballooning, using an input value of the offset distance between the centre of the calandria tube and the initial centre of the pressure tube for determining the position radius of the pressure tube. One significant improvement realized in using the noncircular deformation model is a more accurate prediction in

  17. Improving acute kidney injury diagnostics using predictive analytics.

    Science.gov (United States)

    Basu, Rajit K; Gist, Katja; Wheeler, Derek S

    2015-12-01

    Acute kidney injury (AKI) is a multifactorial syndrome affecting an alarming proportion of hospitalized patients. Although early recognition may expedite management, the ability to identify patients at-risk and those suffering real-time injury is inconsistent. The review will summarize the recent reports describing advancements in the area of AKI epidemiology, specifically focusing on risk scoring and predictive analytics. In the critical care population, the primary underlying factors limiting prediction models include an inability to properly account for patient heterogeneity and underperforming metrics used to assess kidney function. Severity of illness scores demonstrate limited AKI predictive performance. Recent evidence suggests traditional methods for detecting AKI may be leveraged and ultimately replaced by newer, more sophisticated analytical tools capable of prediction and identification: risk stratification, novel AKI biomarkers, and clinical information systems. Additionally, the utility of novel biomarkers may be optimized through targeting using patient context, and may provide more granular information about the injury phenotype. Finally, manipulation of the electronic health record allows for real-time recognition of injury. Integrating a high-functioning clinical information system with risk stratification methodology and novel biomarker yields a predictive analytic model for AKI diagnostics.

  18. A new process for the fractionation of uranium; Un nuevo procedimiento para el fraccionamiento de uranio

    Energy Technology Data Exchange (ETDEWEB)

    Costas, E.; Baselga, B.; Tarin, F.

    2015-07-01

    We propose a new biological process for uranium isotopic fractionation based on Chlamydomonas cf. fonticola (microalgae) isolated from a pond extremely contaminated by uranium (. 25 ppm) from the ENUSA mine in Saelices (Salamanca, Spain) and genetically improved. The metabolic activity of this genetically improved ChlSPGI strain allows recover 115 mg of U per gram of micoralgal biomass in a short time (because this strain complete their cell cycle in . 24 hours). During this process ChlSPGI microalgae selectively captures {sup 2}35U conducting an isotopic enrichment of {sup 2}35U ({sup 2}35U δ = + 3,983%). (Author)

  19. FDG-PET/CT in the prediction of pulmonary function improvement in nonspecific interstitial pneumonia. A Pilot Study

    Energy Technology Data Exchange (ETDEWEB)

    Jacquelin, V. [AP-HP, Hosp. Avicenne, Department of Nuclear Medicine, Bobigny (France); Mekinian, A. [AP-HP, Hosp. Saint-Antoine, Department of Internal Medicine and Inflammation-Immunopathology-Biotherapy Department (DHU i2B), Paris (France); Brillet, P.Y. [AP-HP, Hosp. Avicenne, Department of Radiology, Bobigny (France); Univ. Paris 13, Sorbonne Paris Cité, Bobigny (France); Nunes, H. [AP-HP, Hosp. Avicenne, Department of Pneumology, Bobigny (France); Univ. Paris 13, Sorbonne Paris Cité, Bobigny (France); Fain, O. [AP-HP, Hosp. Saint-Antoine, Department of Internal Medicine and Inflammation-Immunopathology-Biotherapy Department (DHU i2B), Paris (France); Valeyre, D. [AP-HP, Hosp. Avicenne, Department of Pneumology, Bobigny (France); Univ. Paris 13, Sorbonne Paris Cité, Bobigny (France); Soussan, M., E-mail: michael.soussan@aphp.fr [AP-HP, Hosp. Avicenne, Department of Nuclear Medicine, Bobigny (France); Univ. Paris 13, Sorbonne Paris Cité, Bobigny (France)

    2016-12-15

    Purpose: Our study aimed to analyse the characteristics of nonspecific interstitial pneumonia (NSIP) using FDG-PET/CT (PET) and to evaluate its ability to predict the therapeutic response. Procedures: Eighteen NSIP patients were included. Maximum standardized uptake value (SUV{sub max}), FDG uptake extent (in percentage of lung volume), high resolution CT scan (HRCT) elementary lesions, and HRCT fibrosis score were recorded. The predictive value of the parameters for lung function improvement was evaluated using logistic regression and Receiver Operating Characteristic (ROC) curve analysis (n = 13/18). Results: All patients had an increased pulmonary FDG uptake (median SUV{sub max} = 3.1 [2–7.6]), with a median extent of 19% [6–67]. Consolidations, ground-glass opacities, honeycombing and reticulations showed uptake in 90%, 89%, 85% and 76%, respectively. FDG uptake extent was associated with improvement of pulmonary function under treatment (increase in forced vital capacity > 10%, p = 0.03), whereas SUV{sub max} and HRCT fibrosis score were not (p > 0.5). For FDG uptake extent, ROC analysis showed an area under the curve at 0.85 ± 0.11 and sensitivity/specificity was 88%/80% for a threshold fixed at 21%. Conclusions: Increased FDG uptake was observed in all NSIP patients, both in inflammatory and fibrotic HRCT lesions. The quantification of FDG uptake extent might be useful to predict functional improvement under treatment.

  20. FDG-PET/CT in the prediction of pulmonary function improvement in nonspecific interstitial pneumonia. A Pilot Study

    International Nuclear Information System (INIS)

    Jacquelin, V.; Mekinian, A.; Brillet, P.Y.; Nunes, H.; Fain, O.; Valeyre, D.; Soussan, M.

    2016-01-01

    Purpose: Our study aimed to analyse the characteristics of nonspecific interstitial pneumonia (NSIP) using FDG-PET/CT (PET) and to evaluate its ability to predict the therapeutic response. Procedures: Eighteen NSIP patients were included. Maximum standardized uptake value (SUV max ), FDG uptake extent (in percentage of lung volume), high resolution CT scan (HRCT) elementary lesions, and HRCT fibrosis score were recorded. The predictive value of the parameters for lung function improvement was evaluated using logistic regression and Receiver Operating Characteristic (ROC) curve analysis (n = 13/18). Results: All patients had an increased pulmonary FDG uptake (median SUV max = 3.1 [2–7.6]), with a median extent of 19% [6–67]. Consolidations, ground-glass opacities, honeycombing and reticulations showed uptake in 90%, 89%, 85% and 76%, respectively. FDG uptake extent was associated with improvement of pulmonary function under treatment (increase in forced vital capacity > 10%, p = 0.03), whereas SUV max and HRCT fibrosis score were not (p > 0.5). For FDG uptake extent, ROC analysis showed an area under the curve at 0.85 ± 0.11 and sensitivity/specificity was 88%/80% for a threshold fixed at 21%. Conclusions: Increased FDG uptake was observed in all NSIP patients, both in inflammatory and fibrotic HRCT lesions. The quantification of FDG uptake extent might be useful to predict functional improvement under treatment.

  1. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

    Science.gov (United States)

    Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

    2018-03-01

    Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

  2. Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L. Improved by Accounting for Linkage Disequilibrium

    Directory of Open Access Journals (Sweden)

    Guillaume P. Ramstein

    2016-04-01

    Full Text Available Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

  3. Concurrent Modeling of Hydrodynamics and Interaction Forces Improves Particle Deposition Predictions.

    Science.gov (United States)

    Jin, Chao; Ren, Carolyn L; Emelko, Monica B

    2016-04-19

    It is widely believed that media surface roughness enhances particle deposition-numerous, but inconsistent, examples of this effect have been reported. Here, a new mathematical framework describing the effects of hydrodynamics and interaction forces on particle deposition on rough spherical collectors in absence of an energy barrier was developed and validated. In addition to quantifying DLVO force, the model includes improved descriptions of flow field profiles and hydrodynamic retardation functions. This work demonstrates that hydrodynamic effects can significantly alter particle deposition relative to expectations when only the DLVO force is considered. Moreover, the combined effects of hydrodynamics and interaction forces on particle deposition on rough, spherical media are not additive, but synergistic. Notably, the developed model's particle deposition predictions are in closer agreement with experimental observations than those from current models, demonstrating the importance of inclusion of roughness impacts in particle deposition description/simulation. Consideration of hydrodynamic contributions to particle deposition may help to explain discrepancies between model-based expectations and experimental outcomes and improve descriptions of particle deposition during physicochemical filtration in systems with nonsmooth collector surfaces.

  4. Improved apparatus for predictive diagnosis of rotator cuff disease

    Science.gov (United States)

    Pillai, Anup; Hall, Brittany N.; Thigpen, Charles A.; Kwartowitz, David M.

    2014-03-01

    Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.

  5. A New Approach to Improve Accuracy of Grey Model GMC(1,n in Time Series Prediction

    Directory of Open Access Journals (Sweden)

    Sompop Moonchai

    2015-01-01

    Full Text Available This paper presents a modified grey model GMC(1,n for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n model was verified by two cases: the study of forecasting CO2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n and D-GMC(1,n models.

  6. Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

    Science.gov (United States)

    Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

    2015-09-01

    Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

  7. Graphene quantum dots /LaCoO3/attapulgite heterojunction photocatalysts with improved photocatalytic activity

    International Nuclear Information System (INIS)

    Zhu, Wei; Li, Xiazhang

    2017-01-01

    A new nanocomposite of graphene quantum dots/LaCoO 3 /attapulgite (GQDs/LaCoO 3 /ATP) was prepared by a facile impregnation method and was applied to degradation of the organic pollutants as photocatalyst under visible light irradiation. Multiple techniques were used to characterize the structures, morphologies and photocatalytic activities of samples. The photocatalytic activity of the GQDs/LaCoO 3 /ATP nanocomposites was effectively evaluated using Methylene blue (MB), antibiotic agent chlortetracycline (CHL) and tetracycline hydrochloride (TC). The as-synthesized GQDs/LaCoO 3 /ATP nanocomposites exhibited higher photocatalytic activities than LaCoO 3 /ATP, which showed a broad spectrum of photocatalytic degradation activity. The results of ESR and free radicals trapping experiments indicated that circle OH and h + were the main species for the photocatalytic degradation. GQDs played a significant role in the photocatalytic activity improvement of LaCoO 3 /ATP, increasing the visible light absorption, slowing the recombination and improving the charge transfer. (orig.)

  8. Graphene quantum dots /LaCoO3/attapulgite heterojunction photocatalysts with improved photocatalytic activity

    Science.gov (United States)

    Zhu, Wei; Li, Xiazhang

    2017-04-01

    A new nanocomposite of graphene quantum dots/LaCoO3/attapulgite (GQDs/LaCoO3/ATP) was prepared by a facile impregnation method and was applied to degradation of the organic pollutants as photocatalyst under visible light irradiation. Multiple techniques were used to characterize the structures, morphologies and photocatalytic activities of samples. The photocatalytic activity of the GQDs/LaCoO3/ATP nanocomposites was effectively evaluated using Methylene blue (MB), antibiotic agent chlortetracycline (CHL) and tetracycline hydrochloride (TC). The as-synthesized GQDs/LaCoO3/ATP nanocomposites exhibited higher photocatalytic activities than LaCoO3/ATP, which showed a broad spectrum of photocatalytic degradation activity. The results of ESR and free radicals trapping experiments indicated that • OH and h+ were the main species for the photocatalytic degradation. GQDs played a significant role in the photocatalytic activity improvement of LaCoO3/ATP, increasing the visible light absorption, slowing the recombination and improving the charge transfer.

  9. Dynameomics: Data-driven methods and models for utilizing large-scale protein structure repositories for improving fragment-based loop prediction

    Science.gov (United States)

    Rysavy, Steven J; Beck, David AC; Daggett, Valerie

    2014-01-01

    Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼25–75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. PMID:25142412

  10. Dynameomics: data-driven methods and models for utilizing large-scale protein structure repositories for improving fragment-based loop prediction.

    Science.gov (United States)

    Rysavy, Steven J; Beck, David A C; Daggett, Valerie

    2014-11-01

    Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼ 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. © 2014 The Protein Society.

  11. Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

    Directory of Open Access Journals (Sweden)

    E Penelope Holland

    Full Text Available Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

  12. Using road topology to improve cyclist path prediction

    NARCIS (Netherlands)

    Pool, E.A.I.; Kooij, J.F.P.; Gavrila, D.; Ioannou, Petros; Zhang, Wei-Bin; Lu, Meng

    2017-01-01

    We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected

  13. Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

    Science.gov (United States)

    Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

    2017-12-01

    Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

  14. High-frequency conductive hearing loss as a diagnostic test for incomplete ossicular discontinuity in non-cholesteatomatous chronic suppurative otitis media.

    Directory of Open Access Journals (Sweden)

    Krishnamurti M A Sarmento

    Full Text Available Chronic suppurative otitis media, with or without cholesteatoma, may lead to erosion of the ossicles and discontinuity of the ossicular chain. In incomplete ossicular discontinuity (IOD, partial erosion of the ossicles occurs, but some sound transmission is noted throughout the ossicular chain. High-frequency conductive hearing loss (HfCHL has been considered a hallmark of incomplete ossicular discontinuity. This study aims to evaluate the use of HfCHL as a preoperative predictor of IOD in patients with non-cholesteatomatous chronic suppurative otitis media. The HfCHL test was defined as the preoperative air-bone gap (ABG at 4 kHz minus the average of the ABG at 0.25 and 0.5 kHz. The test was applied in 328 patients before surgery and compared to intraoperative findings as the gold standard. At surgery, 201 (61.3% patients had an intact ossicular chain, 44 (13.4% had a complete ossicular discontinuity, and 83 (25.3% exhibited an IOD. The best cutoff level was calculated as 10 dB. The HfCHL test to diagnose IOD had a sensitivity of 83% and a specificity of 92% with a post-test probability of 78% and a likelihood ratio of 10.2. We concluded that the HfCHL test is highly effective in predicting IOD in patients with non-cholesteatomatous chronic suppurative otitis media and that it should be used routinely as a screening test prior to surgery.

  15. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  16. Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units

    International Nuclear Information System (INIS)

    Kropat, Georg; Bochud, Francois; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios, Martha; Baechler, Sébastien

    2015-01-01

    Purpose: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. Method: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). Results: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. Conclusion: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables

  17. Merging economics and epidemiology to improve the prediction and management of infectious disease.

    Science.gov (United States)

    Perrings, Charles; Castillo-Chavez, Carlos; Chowell, Gerardo; Daszak, Peter; Fenichel, Eli P; Finnoff, David; Horan, Richard D; Kilpatrick, A Marm; Kinzig, Ann P; Kuminoff, Nicolai V; Levin, Simon; Morin, Benjamin; Smith, Katherine F; Springborn, Michael

    2014-12-01

    Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as "economic epidemiology" or "epidemiological economics," the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

  18. Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

    Science.gov (United States)

    Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

    2016-09-01

    Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

  19. Intra-operative hearing monitoring methods in middle ear surgeries

    Directory of Open Access Journals (Sweden)

    Wei Ren

    2016-12-01

    Full Text Available Hearing loss is a condition affecting millions of people worldwide. Conductive hearing loss (CHL is mainly caused by middle ear diseases. The low frequency area is the pivotal part of speech frequencies and most frequently impaired in patients with CHL. Among various treatments of CHL, middle ear surgery is efficient to improve hearing. However, variable success rates and possible needs for prolonged revision surgery still frustrate both surgeons and patients. Nowadays, increasing numbers of researchers explore various methods to monitor the efficacy of ossicular reconstruction intraoperatively, including electrocochleography (ECochG, auditory brainstem response (ABR, auditory steady state response (ASSR, distortion product otoacoustic emissions (DPOAE, subjective whisper test, and optical coherence tomography (OCT. Here, we illustrate several methods used clinically by reviewing the literature.

  20. The prediction of resting energy expenditure in type 2 diabetes mellitus is improved by factoring for glycemia.

    Science.gov (United States)

    Gougeon, R; Lamarche, M; Yale, J-F; Venuta, T

    2002-12-01

    Predictive equations have been reported to overestimate resting energy expenditure (REE) for obese persons. The presence of hyperglycemia results in elevated REE in obese persons with type 2 diabetes, and its effect on the validity of these equations is unknown. We tested whether (1) indicators of diabetes control were independent associates of REE in type 2 diabetes and (2) their inclusion would improve predictive equations. A cross-sectional study of 65 (25 men, 40 women) obese type 2 diabetic subjects. Variables measured were: REE by ventilated-hood indirect calorimetry, body composition by bioimpedance analysis, body circumferences, fasting plasma glucose (FPG) and hemoglobin A(1c). Data were analyzed using stepwise multiple linear regression. REE, corrected for weight, fat-free mass, age and gender, was significantly greater with FPG>10 mmol/l (P=0.017) and correlated with FPG (P=0.013) and hemoglobin A(1c) as percentage upper limit of normal (P=0.02). Weight was the main determinant of REE. Together with hip circumference and FPG, it explained 81% of the variation. FPG improved the predictability of the equation by >3%. With poor glycemic control, it can represent an increase in REE of up to 8%. Our data indicate that in a population of obese subjects with type 2 diabetes mellitus, REE is better predicted when fasting plasma glucose is included as a variable.

  1. Improved methods for predicting peptide binding affinity to MHC class II molecules.

    Science.gov (United States)

    Jensen, Kamilla Kjaergaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

    2018-01-06

    Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2. © 2018 John Wiley & Sons Ltd.

  2. Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

    Science.gov (United States)

    Ghimire, Bardan; Riley, William J.; Koven, Charles D.; Mu, Mingquan; Randerson, James T.

    2016-06-01

    In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.

  3. Improving a prediction system for oil spills in the Yellow Sea: effect of tides on subtidal flow.

    Science.gov (United States)

    Kim, Chang-Sin; Cho, Yang-Ki; Choi, Byoung-Ju; Jung, Kyung Tae; You, Sung Hyup

    2013-03-15

    A multi-nested prediction system for the Yellow Sea using drifter trajectory simulations was developed to predict the movements of an oil spill after the MV Hebei Spirit accident. The speeds of the oil spill trajectories predicted by the model without tidal forcing were substantially faster than the observations; however, predictions taking into account the tides, including both tidal cycle and subtidal periods, were satisfactorily improved. Subtidal flow in the simulation without tides was stronger than in that with tides because of reduced frictional effects. Friction induced by tidal stress decelerated the southward subtidal flows driven by northwesterly winter winds along the Korean coast of the Yellow Sea. These results strongly suggest that in order to produce accurate predictions of oil spill trajectories, simulations must include tidal effects, such as variations within a tidal cycle and advections over longer time scales in tide-dominated areas. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. The light absorption by suspended particles, phytoplankton and dissolved organic matter in deep-and coastal waiters of the Black Sea impact on algorithms for remote sensing of chlorophyll -a-.

    Science.gov (United States)

    Churilova, T.; Suslin, V.; Berseneva, G.; Georgieva, L.

    At present time for the analysis and prediction of marine ecosystem state Chlorophyll and Primary production models based on optical satellite data are widely used. However, the SeaWiFS algorithms providing the transformation of color images to chlorophyll maps give inaccurate estimation of chlorophyll "a" (Chl "a") concentration in the Black Sea - an overestimation approximately two times in summer and an underestimation - ~1,5 times during the large diatom bloom in winter-spring. A development of the regional Chl "a" algorithm requires an estimation of spectral characteristics of all light absorbing components and their relationships with Chl "a" concentration. With this aim bio-optical monitoring was organized in two fixed stations in deep-water central western part of the Black Sea and in shelf waters near the Crimea. The weekly monitoring in deep-waters region allowed to determine phytoplankton community succession: seasonal dynamics of size and taxonomic structure, development of large diatoms blooming in March and coccolithophores - in June. The significant variability in pigment concentration and species content of phytoplankton is accompanied by high variability in shape of the phytoplankton absorption spectra and in values of chl a-specific absorption coefficients. This variability had seasonal character depending mostly on the optical status of phytoplankton cells and partly on taxonomic structure of phytoplankton. The pigment packaging parameter fluctuated from 0.64-0.68 (October-December) to 0.95-0.97 (April-May). The package effect depended on intracellular pigment concentration and the size and geometry of cells, which change significantly over the year, because of extremely different environmental conditions. The relationships between phytoplankton specific absorption coefficients (at 412, 443, 490, 510, 555, 678 nm) and Chl "a" concentration have been described by power functions. The contribution of detritus to total particulate absorption

  5. Journal of Earth System Science | Indian Academy of Sciences

    Indian Academy of Sciences (India)

    ... by phytoplankton in the biological cycle. The mean Chl /Chl and Chl /Chl ratios and high phaeopigments (Pp) concentrations compared to Chl and high ratios of Chl /Pp suggests the possibility of the potential growth of phytoplankton populations in lower light intensity and low turbulent areas of these mangrove ...

  6. CHLORAMBUCIL PLUS RITUXIMAB AS FRONT-LINE THERAPY IN ELDERLY/UNFIT PATIENTS AFFECTED BY B-CELL CHRONIC LYMPHOCYTIC LEUKEMIA: RESULTS OF A SINGLE-CENTRE EXPERIENCE.

    Directory of Open Access Journals (Sweden)

    Luca Laurenti

    2013-05-01

    Full Text Available Currently standard first line therapy for fit patients with B-CLL/SLL are fludarabine-based regimens. Elderly patients or patients with comorbidities poorly tolerate purine analogue-based chemotherapy and they are often treated with Chlorambucil (Chl. However, complete response (CR and overall response (OR rates with Chl are relatively low. We now investigated whether the addition of Rituximab to Chl will improve the efficacy without impairing the tolerability in elderly and unfit patients. We included in our study 27 elderly or unfit patients that had not received prior therapy. All patients were treated with Chl (1mg/Kg per 28-day cycle for 8 cycles plus Rituximab (375 mg/m2 for the first course and 500 mg/m2 for subsequent cycles until the 6th cycle. We obtained an OR rate of 74%. The most frequent adverse effect was grade 3-4 neutropenia, which occurred in 18.5% of the patients. Infections or grade 3-4 extra-hematological side effects were not recorded. None of the patients required reduction of dose, delay of therapy or hospitalization. Overall, these data suggest that Chl-R is an effective and well tolerated regimen in elderly/unfit patients with CLL.

  7. Mutations in gp41 are correlated with coreceptor tropism but do not improve prediction methods substantially.

    Science.gov (United States)

    Thielen, Alexander; Lengauer, Thomas; Swenson, Luke C; Dong, Winnie W Y; McGovern, Rachel A; Lewis, Marilyn; James, Ian; Heera, Jayvant; Valdez, Hernan; Harrigan, P Richard

    2011-01-01

    The main determinants of HIV-1 coreceptor usage are located in the V3-loop of gp120, although mutations in V2 and gp41 are also known. Incorporation of V2 is known to improve prediction algorithms; however, this has not been confirmed for gp41 mutations. Samples with V3 and gp41 genotypes and Trofile assay (Monogram Biosciences, South San Francisco, CA, USA) results were taken from the HOMER cohort (n=444) and from patients screened for the MOTIVATE studies (n=1,916; 859 with maraviroc outcome data). Correlations of mutations with tropism were assessed using Fisher's exact test and prediction models trained using support vector machines. Models were validated by cross-validation, by testing models from one dataset on the other, and by analysing virological outcome. Several mutations within gp41 were highly significant for CXCR4 usage; most strikingly an insertion occurring in 7.7% of HOMER-R5 and 46.3% of HOMER-X4 samples (MOTIVATE 5.7% and 25.2%, respectively). Models trained on gp41 sequence alone achieved relatively high areas under the receiver-operating characteristic curve (AUCs; HOMER 0.713 and MOTIVATE 0.736) that were almost as good as V3 models (0.773 and 0.884, respectively). However, combining the two regions improved predictions only marginally (0.813 and 0.902, respectively). Similar results were found when models were trained on HOMER and validated on MOTIVATE or vice versa. The difference in median log viral load decrease at week 24 between patients with R5 and X4 virus was 1.65 (HOMER 2.45 and MOTIVATE 0.79) for V3 models, 1.59 for gp41-models (2.42 and 0.83, respectively) and 1.58 for the combined predictor (2.44 and 0.86, respectively). Several mutations within gp41 showed strong correlation with tropism in two independent datasets. However, incorporating gp41 mutations into prediction models is not mandatory because they do not improve substantially on models trained on V3 sequences alone.

  8. Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

    International Nuclear Information System (INIS)

    Han, Yong-Ming; Geng, Zhi-Qiang; Zhu, Qun-Xiong

    2016-01-01

    Graphical abstract: This paper proposed an energy optimization and prediction of complex petrochemical industries based on a DEA-integrated ANN approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. Meanwhile, the optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency. - Highlights: • The DEA-integrated ANN approach is proposed. • The DEA-ANN prediction model is effectively verified through the standard data source from the UCI repository. • The energy optimization and prediction framework of complex petrochemical industries based on the proposed method is obtained. • The proposed method is valid and efficient in improvement of energy efficiency in complex petrochemical plants. - Abstract: Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA

  9. Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO

    Science.gov (United States)

    Manatsa, Desmond; Mushore, Terrence; Lenouo, Andre

    2017-01-01

    The provision of timely and reliable climate information on which to base management decisions remains a critical component in drought planning for southern Africa. In this observational study, we have not only proposed a forecasting scheme which caters for timeliness and reliability but improved relevance of the climate information by using a novel drought index called the standardised precipitation evapotranspiration index (SPEI), instead of the traditional precipitation only based index, the standardised precipitation index (SPI). The SPEI which includes temperature and other climatic factors in its construction has a more robust connection to ENSO than the SPI. Consequently, the developed ENSO-SPEI prediction scheme can provide quantitative information about the spatial extent and severity of predicted drought conditions in a way that reflects more closely the level of risk in the global warming context of the sub region. However, it is established that the ENSO significant regional impact is restricted only to the period December-March, implying a revisit to the traditional ENSO-based forecast scheme which essentially divides the rainfall season into the two periods, October to December and January to March. Although the prediction of ENSO events has increased with the refinement of numerical models, this work has demonstrated that the prediction of drought impacts related to ENSO is also a reality based only on observations. A large temporal lag is observed between the development of ENSO phenomena (typically in May of the previous year) and the identification of regional SPEI defined drought conditions. It has been shown that using the Southern Africa Regional Climate Outlook Forum's (SARCOF) traditional 3-month averaged Nino 3.4 SST index (June to August) as a predictor does not have an added advantage over using only the May SST index values. In this regard, the extended lead time and improved skill demonstrated in this study could immensely benefit

  10. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

    Science.gov (United States)

    Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

    2017-01-01

    Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

  11. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  12. Theoretical study on new bias factor methods to effectively use critical experiments for improvement of prediction accuracy of neutronic characteristics

    International Nuclear Information System (INIS)

    Kugo, Teruhiko; Mori, Takamasa; Takeda, Toshikazu

    2007-01-01

    Extended bias factor methods are proposed with two new concepts, the LC method and the PE method, in order to effectively use critical experiments and to enhance the applicability of the bias factor method for the improvement of the prediction accuracy of neutronic characteristics of a target core. Both methods utilize a number of critical experimental results and produce a semifictitious experimental value with them. The LC and PE methods define the semifictitious experimental values by a linear combination of experimental values and the product of exponentiated experimental values, respectively, and the corresponding semifictitious calculation values by those of calculation values. A bias factor is defined by the ratio of the semifictitious experimental value to the semifictitious calculation value in both methods. We formulate how to determine weights for the LC method and exponents for the PE method in order to minimize the variance of the design prediction value obtained by multiplying the design calculation value by the bias factor. From a theoretical comparison of these new methods with the conventional method which utilizes a single experimental result and the generalized bias factor method which was previously proposed to utilize a number of experimental results, it is concluded that the PE method is the most useful method for improving the prediction accuracy. The main advantages of the PE method are summarized as follows. The prediction accuracy is necessarily improved compared with the design calculation value even when experimental results include large experimental errors. This is a special feature that the other methods do not have. The prediction accuracy is most effectively improved by utilizing all the experimental results. From these facts, it can be said that the PE method effectively utilizes all the experimental results and has a possibility to make a full-scale-mockup experiment unnecessary with the use of existing and future benchmark

  13. Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins

    Directory of Open Access Journals (Sweden)

    Selbig Joachim

    2009-04-01

    Full Text Available Abstract Background Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites. Results We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D structural information available in the protein data bank (PDB and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information. Conclusion While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as

  14. Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction

    Directory of Open Access Journals (Sweden)

    Takemasa Miyoshi

    2012-09-01

    Full Text Available In addition to conventional observations, atmospheric temperature and humidity profile data from the Atmospheric Infrared Sounder (AIRS Version 5 retrieval products are assimilated into the Weather Research and Forecasting (WRF model, using the local ensemble transform Kalman filter (LETKF. Although a naive assimilation of all available quality-controlled AIRS retrieval data yields an inferior analysis, the additional enhancements of adaptive inflation and horizontal data thinning result in a general improvement of numerical weather prediction skill due to AIRS data. In particular, the adaptive inflation method is enhanced so that it no longer assumes temporal homogeneity of the observing network and allows for a better treatment of the temporally inhomogeneous AIRS data. Results indicate that the improvements due to AIRS data are more significant in longer-lead forecasts. Forecasts of Typhoons Sinlaku and Jangmi in September 2008 show improvements due to AIRS data.

  15. Modified ensemble Kalman filter for nuclear accident atmospheric dispersion: prediction improved and source estimated.

    Science.gov (United States)

    Zhang, X L; Su, G F; Yuan, H Y; Chen, J G; Huang, Q Y

    2014-09-15

    Atmospheric dispersion models play an important role in nuclear power plant accident management. A reliable estimation of radioactive material distribution in short range (about 50 km) is in urgent need for population sheltering and evacuation planning. However, the meteorological data and the source term which greatly influence the accuracy of the atmospheric dispersion models are usually poorly known at the early phase of the emergency. In this study, a modified ensemble Kalman filter data assimilation method in conjunction with a Lagrangian puff-model is proposed to simultaneously improve the model prediction and reconstruct the source terms for short range atmospheric dispersion using the off-site environmental monitoring data. Four main uncertainty parameters are considered: source release rate, plume rise height, wind speed and wind direction. Twin experiments show that the method effectively improves the predicted concentration distribution, and the temporal profiles of source release rate and plume rise height are also successfully reconstructed. Moreover, the time lag in the response of ensemble Kalman filter is shortened. The method proposed here can be a useful tool not only in the nuclear power plant accident emergency management but also in other similar situation where hazardous material is released into the atmosphere. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Mapping distribution of Rastrelliger kanagurta in the exclusive economic zone (EEZ) of Malaysia using maximum entropy modeling approach

    Science.gov (United States)

    Yusop, Syazwani Mohd; Mustapha, Muzzneena Ahmad

    2018-04-01

    The coupling of fishing locations for R. kanagurta obtained from SEAFDEC and multi-sensor satellite imageries of oceanographic variables; sea surface temperature (SST), sea surface height (SSH) and chl-a concentration (chl-a) were utilized to evaluate the performance of maximum entropy (MaxEnt) models for R. kanagurta fishing ground for prediction. Besides, this study was conducted to identify the relative percentage contribution of each environmental variable considered in order to describe the effects of the oceanographic factors on the species distribution in the study area. The potential fishing grounds during intermonsoon periods; April and October 2008-2009 were simulated separately and covered the near-coast of Kelantan, Terengganu, Pahang and Johor. The oceanographic conditions differed between regions by the inherent seasonal variability. The seasonal and spatial extents of potential fishing grounds were largely explained by chl-a concentration (0.21-0.99 mg/m3 in April and 0.28-1.00 mg/m3 in October), SSH (77.37-85.90 cm in April and 107.60-108.97 cm in October) and SST (30.43-33.70 °C in April and 30.48-30.97 °C in October). The constructed models were applicable and therefore they were suitable for predicting the potential fishing zones of R. kanagurta in EEZ. The results from this study revealed MaxEnt's potential for predicting the spatial distribution of R. kanagurta and highlighted the use of multispectral satellite images for describing the seasonal potential fishing grounds.

  17. Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

    Science.gov (United States)

    Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

    2017-07-01

    High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

  18. Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models

    Science.gov (United States)

    Ramírez-Albores, Jorge E.; Bustamante, Ramiro O.

    2016-01-01

    naturally established individuals because this improves the accuracy of predictions about their distribution ranges. PMID:27195983

  19. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

    Science.gov (United States)

    Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

    2016-11-01

    Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

  20. Chlorophyll f distribution and dynamics in cyanobacterial beachrock biofilms.

    Science.gov (United States)

    Trampe, Erik; Kühl, Michael

    2016-12-01

    Chlorophyll (Chl) f, the most far-red (720-740 nm) absorbing Chl species, was discovered in cyanobacterial isolates from stromatolites and subsequently in other habitats as well. However, the spatial distribution and temporal dynamics of Chl f in a natural habitat have so far not been documented. Here, we report the presence of Chl f in cyanobacterial beachrock biofilms. Hyperspectral imaging on cross-sections of beachrock from Heron Island (Great Barrier Reef, Australia), showed a strong and widely distributed signature of Chl f absorption in an endolithic layer below the dense cyanobacterial surface biofilm that could be localized to aggregates of Chroococcidiopsis-like unicellular cyanobacteria packed within a thick common sheath. High-pressure liquid chromatography-based pigment analyses showed in situ ratios of Chl f to Chl a of 5% in brown-pigmented zones of the beachrock, with lower ratios of ~0.5% in the black- and pink-pigmented biofilm zones. Enrichment experiments with black beachrock biofilm showed stimulated synthesis of Chl f and Chl d when grown under near-infrared radiation (NIR; 740 nm), with a Chl f to Chl a ratio increasing 4-fold to 2%, whereas the Chl d to Chl a ratio went from 0% to 0.8%. Enrichments grown under white light (400-700 nm) produced no detectable amounts of either Chl d or Chl f. Beachrock cyanobacteria thus exhibited characteristics of far-red light photoacclimation, enabling Chl f -containing cyanobacteria to thrive in optical niches deprived of visible light when sufficient NIR is prevalent. © 2016 Phycological Society of America.

  1. Use of in situ volumetric water content at field capacity to improve prediction of soil water retention properties

    OpenAIRE

    Al Majou , Hassan; Bruand , Ary; Duval , Odile

    2008-01-01

    International audience; Use of in situ volumetric water content at field capacity to improve prediction of soil water retention properties. Most pedotransfer functions (PTFs) developed over the last three decades to generate water retention characteristics use soil texture, bulk density and organic carbon content as predictors. Despite of the high number of PTFs published, most being class- or continuous-PTFs, accuracy of prediction remains limited. In this study, we compared the performance ...

  2. Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

    Energy Technology Data Exchange (ETDEWEB)

    Maslowski, Wieslaw [Naval Postgraduate School, Monterey, CA (United States)

    2016-10-17

    This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate through polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.

  3. Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters

    Directory of Open Access Journals (Sweden)

    Majid Nazeer

    2017-11-01

    Full Text Available Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM, chlorophyll-a (Chl-a, and suspended solids (SS concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM and neural networks (NN-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60–94% and 81–94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations over the complex coastal area of Hong Kong and other similar coastal environments.

  4. Improving predictive capabilities of environmental change with GLOBE data

    Science.gov (United States)

    Robin, Jessica Hill

    This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (>35°) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (>60°) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March--mid May) when vegetation is greening up (NDVI 0.60). Results from the second study showed that a climate threshold of 153 +/- 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the interannual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.

  5. The Urgent Need for Improved Climate Models and Predictions

    Science.gov (United States)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  6. Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

    Directory of Open Access Journals (Sweden)

    Peng Ren

    Full Text Available Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD to obtain their Intrinsic Mode Functions (IMF. Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.

  7. Operative findings of conductive hearing loss with intact tympanic membrane and normal temporal bone computed tomography.

    Science.gov (United States)

    Kim, Se-Hyung; Cho, Yang-Sun; Kim, Hye Jeong; Kim, Hyung-Jin

    2014-06-01

    Despite recent technological advances in diagnostic methods including imaging technology, it is often difficult to establish a preoperative diagnosis of conductive hearing loss (CHL) in patients with an intact tympanic membrane (TM). Especially, in patients with a normal temporal bone computed tomography (TBCT), preoperative diagnosis is more difficult. We investigated middle ear disorders encountered in patients with CHL involving an intact TM and normal TBCT. We also analyzed the surgical results with special reference to the pathology. We reviewed the medical records of 365 patients with intact TM, who underwent exploratory tympanotomy for CHL. Fifty nine patients (67 ears, eight bilateral surgeries) had a normal preoperative TBCT findings reported by neuro-radiologists. Demographic data, otologic history, TM findings, preoperative imaging findings, intraoperative findings, and pre- and postoperative audiologic data were obtained and analyzed. Exploration was performed most frequently in the second and fifth decades. The most common postoperative diagnosis was stapedial fixation with non-progressive hearing loss. The most commonly performed hearing-restoring procedure was stapedotomy with piston wire prosthesis insertion. Various types of hearing-restoring procedures during exploration resulted in effective hearing improvement, especially with better outcome in the ossicular chain fixation group. In patients with CHL who have intact TM and normal TBCT, we should consider an exploratory tympanotomy for exact diagnosis and hearing improvement. Information of the common operative findings from this study may help in preoperative counseling.

  8. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

    KAUST Repository

    Ogorzalek, Tadeusz L.

    2018-01-04

    Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

  9. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

    KAUST Repository

    Ogorzalek, Tadeusz L.; Hura, Greg L.; Belsom, Adam; Burnett, Kathryn H.; Kryshtafovych, Andriy; Tainer, John A.; Rappsilber, Juri; Tsutakawa, Susan E.; Fidelis, Krzysztof

    2018-01-01

    Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

  10. The improvement of MOSFET prediction in space environments using the conversion model

    International Nuclear Information System (INIS)

    Shvetzov-Shilovsky, I.N.; Cherepko, S.V.; Pershenkov, V.S.

    1994-01-01

    The modeling of MOS device response to a low dose rate irradiation has been performed. The existing conversion model based on the linear dependence between positive oxide charge annealing and interface trap buildup accurately predicts the long time response of MOSFETs with relatively thick oxides but overestimates the threshold voltage shift for radiation hardened MOSFETs with thin oxides. To give an explanation to this fact, the authors investigate the impulse response function for threshold voltage. A revised model, which incorporates the different energy levels of hole traps in the oxide improves the fit between the model and data and gives an explanation to the fitting parameters dependence on oxide field

  11. Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

    Institute of Scientific and Technical Information of China (English)

    Yuhong Wang; Qin Liu; Jianrong Tang; Wenbin Cao; Xiaozhong Li

    2014-01-01

    A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

  12. Improved Performance and Safety for High Energy Batteries Through Use of Hazard Anticipation and Capacity Prediction

    Science.gov (United States)

    Atwater, Terrill

    1993-01-01

    Prediction of the capacity remaining in used high rate, high energy batteries is important information to the user. Knowledge of the capacity remaining in used batteries results in better utilization. This translates into improved readiness and cost savings due to complete, efficient use. High rate batteries, due to their chemical nature, are highly sensitive to misuse (i.e., over discharge or very high rate discharge). Battery failure due to misuse or manufacturing defects could be disastrous. Since high rate, high energy batteries are expensive and energetic, a reliable method of predicting both failures and remaining energy has been actively sought. Due to concerns over safety, the behavior of lithium/sulphur dioxide cells at different temperatures and current drains was examined. The main thrust of this effort was to determine failure conditions for incorporation in hazard anticipation circuitry. In addition, capacity prediction formulas have been developed from test data. A process that performs continuous, real-time hazard anticipation and capacity prediction was developed. The introduction of this process into microchip technology will enable the production of reliable, safe, and efficient high energy batteries.

  13. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

    Directory of Open Access Journals (Sweden)

    Christof Winter

    Full Text Available Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

  14. A predictive maintenance approach for improved nuclear plant availability

    International Nuclear Information System (INIS)

    Verma, R.M.P.; Pandya, M.B.; Kini, M.P.

    1979-01-01

    Predictive maintenance programme as against preventive maintenance programme aims at diagnosing, inspecting, monitoring, and objective condition-checking of equipment. It helps in forecasting failures, and scheduling the optimal frequencies for overhauls, replacements, lubrication etc. It also helps in establishing work load, manpower, resource planning and inventory control. Various stages of predictive maintenance programme for a nuclear power plant are outlined. A partial list of instruments for predictive maintenance is given. (M.G.B.)

  15. Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

    Science.gov (United States)

    Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

    2018-03-21

    Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

  16. Organization of chlorophyll biosynthesis and insertion of chlorophyll into the chlorophyll-binding proteins in chloroplasts.

    Science.gov (United States)

    Wang, Peng; Grimm, Bernhard

    2015-12-01

    Oxygenic photosynthesis requires chlorophyll (Chl) for the absorption of light energy, and charge separation in the reaction center of photosystem I and II, to feed electrons into the photosynthetic electron transfer chain. Chl is bound to different Chl-binding proteins assembled in the core complexes of the two photosystems and their peripheral light-harvesting antenna complexes. The structure of the photosynthetic protein complexes has been elucidated, but mechanisms of their biogenesis are in most instances unknown. These processes involve not only the assembly of interacting proteins, but also the functional integration of pigments and other cofactors. As a precondition for the association of Chl with the Chl-binding proteins in both photosystems, the synthesis of the apoproteins is synchronized with Chl biosynthesis. This review aims to summarize the present knowledge on the posttranslational organization of Chl biosynthesis and current attempts to envision the proceedings of the successive synthesis and integration of Chl into Chl-binding proteins in the thylakoid membrane. Potential auxiliary factors, contributing to the control and organization of Chl biosynthesis and the association of Chl with the Chl-binding proteins during their integration into photosynthetic complexes, are discussed in this review.

  17. Separation and determination of minor photosynthetic pigments by reversed-phase HPLC with minimal alteration of chlorophylls.

    Science.gov (United States)

    Nakamura, A; Watanabe, T

    2001-04-01

    Reversed-phase HPLC conditions for separation of chlorophyll (Chl) a, Chl a' (the C132-epimer of Chl a), pheophytin (Pheo) a (the primary electron acceptor of photosystem (PS) II), and phylloquinone (PhQ) (the secondary electron acceptor of PS 1), have been developed. Pigment extraction conditions were optimized in terms of pigment alteration and extraction efficiency. Pigment composition analysis of light-harvesting complex II, which would not contain Chl a' nor Pheo a, showed the Chl a'/Chl a ratio of 3-4 x 10(-4) and the Pheo a/Chl a ratio of 4-5 x 10(-4), showing that the conditions developed here were sufficiently inert for Chl analysis. Preliminary analysis of thylakoid membranes with this analytical system gave the PhQ/Chl a' ratio of 0.58 +/- 0.03 (n = 4), in line with the stoichiometry of one molecule of Chl a' per PS I.

  18. Differential Role of CBT Skills, DBT Skills and Psychological Flexibility in Predicting Depressive versus Anxiety Symptom Improvement

    Science.gov (United States)

    Webb, Christian A.; Beard, Courtney; Kertz, Sarah J.; Hsu, Kean; Björgvinsson, Thröstur

    2016-01-01

    Objective Studies have reported associations between cognitive behavioral therapy (CBT) skill use and symptom improvement in depressed outpatient samples. However, little is known regarding the temporal relationship between different subsets of therapeutic skills and symptom change among relatively severely depressed patients receiving treatment in psychiatric hospital settings. Method Adult patients with major depression (N=173) receiving combined psychotherapeutic and pharmacological treatment at a psychiatric hospital completed repeated assessments of traditional CBT skills, DBT skills and psychological flexibility, as well as depressive and anxiety symptoms. Results Results indicated that only use of behavioral activation (BA) strategies significantly predicted depressive symptom improvement in this sample; whereas DBT skills and psychological flexibility predicted anxiety symptom change. In addition, a baseline symptom severity X BA strategies interaction emerged indicating that those patients with higher pretreatment depression severity exhibited the strongest association between use of BA strategies and depressive symptom improvement. Conclusions Findings suggest the importance of emphasizing the acquisition and regular use of BA strategies with severely depressed patients in short-term psychiatric settings. In contrast, an emphasis on the development of DBT skills and the cultivation of psychological flexibility may prove beneficial for the amelioration of anxiety symptoms. PMID:27057997

  19. Improved USLE-K factor prediction: A case study on water erosion areas in China

    Directory of Open Access Journals (Sweden)

    Bin Wang

    2016-09-01

    Full Text Available Soil erodibility (K-factor is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study, we assessed the performance of available erodibility estimators Universal Soil Loss Equation (USLE, Revised Universal Soil Loss Equation (RUSLE, Erosion Productivity Impact Calculator (EPIC and the Geometric Mean Diameter based (Dg model for different geographic regions based on the Chinese soil erodibility database (CSED. Results showed that previous estimators overestimated almost all K-values. Furthermore, only the USLE and Dg approaches could be directly and reliably applicable to black and loess soil regions. Based on the nonlinear best fitting techniques, we improved soil erodibility prediction by combining Dg and soil organic matter (SOM. The NSE, R2 and RE values were 0.94, 0.67 and 9.5% after calibrating the results independently; similar model performance was showed for the validation process. The results obtained via the proposed approach were more accurate that the former K-value predictions. Moreover, those improvements allowed us to effectively establish a regional soil erodibility map (1:250,000 scale of water erosion areas in China. The mean K-value of Chinese water erosion regions was 0.0321 (t ha h·(ha MJ mm−1 with a standard deviation of 0.0107 (t ha h·(ha MJ mm−1; K-values present a decreasing trend from North to South in water erosion areas in China. The yield soil erodibility dataset also satisfactorily corresponded to former K-values from different scales (local, regional, and national.

  20. Studies on femtosecond fluorescence dynamics of photosystem II Particle complex at low temperature

    CERN Document Server

    Liu Xiao; He, Jun Fang; Cai, Xia; Peng Jun Fang; Kuang Ting Yun

    2004-01-01

    In order to understanding the diversity of energy transfer in PS II at different temperatures, PS II particle complex purified from spinach was investigated with femtosecond time-resolved fluorescence spectroscopy in the case of excitation 507 nm at 83 K, 160 K, 273 K. The data were analyzed by Gauss analysis and fluorescence decay time- fitting. Some results were achieved. (1) Increase of the temperature results in a broadening of the fluorescence emission spectra due to the temperature-dependent expressions for nonradiative transitions between two electronic states. (2) There are at least several characteristic Chl molecules exist in PS II particle complex, i.e. Chl b/sub 639//sup 640/, Chl b/sub 640//sup 645/, Chl a/sub 660//sup 663/, Chl a/sub 667//sup 668/, Chl a/sub 673//sup 676/, Chl a/sub 680 //sup 681/, Chl a/sub 680/681//sup 682/, Chl a/sub 684,685//sup 668 /689/, Chl a/sub 688//sup 698/, (Chl a/b/sub a//sup e/: a represents the peak of absorption, e represents the peak of emission). (3) Though the ...

  1. Can video games be used to predict or improve laparoscopic skills?

    Science.gov (United States)

    Rosenberg, Bradley H; Landsittel, Douglas; Averch, Timothy D

    2005-04-01

    Performance of laparoscopic surgery requires adequate hand-eye coordination. Video games are an effective way to judge one's hand-eye coordination, and practicing these games may improve one's skills. Our goal was to see if there is a correlation between skill in video games and skill in laparoscopy. Also, we hoped to demonstrate that practicing video games can improve one's laparoscopic skills. Eleven medical students (nine male, two female) volunteered to participate. On day 1, each student played three commercially available video games (Top Spin, XSN Sports; Project Gotham Racing 2, Bizarre Creations; and Amped 2, XSN Sports) for 30 minutes on an X-box (Microsoft, Seattle, WA) and was judged both objectively and subjectively. Next, the students performed four laparoscopic tasks (object transfer, tracing a figure-of-eight, suture placement, and knot-tying) in a swine model and were assessed for time to complete the task, number of errors committed, and hand-eye coordination. The students were then randomized to control (group A) or "training" (i.e., video game practicing; group B) arms. Two weeks later, all students repeated the laparoscopic skills laboratory and were reassessed. Spearman correlation coefficients demonstrated a significant relation between many of the parameters, particularly time to complete each task and hand-eye coordination at the different games. There was a weaker association between video game performance and both laparoscopic errors committed and hand-eye coordination. Group B subjects did not improve significantly over those in group A in any measure (P >0.05 for all). Video game aptitude appears to predict the level of laparoscopic skill in the novice surgeon. In this study, practicing video games did not improve one's laparoscopic skill significantly, but a larger study with more practice time could prove games to be helpful.

  2. Aerodynamic noise prediction of a Horizontal Axis Wind Turbine using Improved Delayed Detached Eddy Simulation and acoustic analogy

    International Nuclear Information System (INIS)

    Ghasemian, Masoud; Nejat, Amir

    2015-01-01

    Highlights: • The noise predictions are performed by Ffowcs Williams and Hawkings method. • There is a direct relation between the radiated noise and the wind speed. • The tonal peaks in the sound spectra match with the blade passing frequency. • The quadrupole noises have negligible effect on the low frequency noises. - Abstract: This paper presents the results of the aerodynamic and aero-acoustic prediction of the flow field around the National Renewable Energy Laboratory Phase VI wind turbine. The Improved Delayed Detached Eddy Simulation turbulence model is applied to obtain the instantaneous turbulent flow field. The noise prediction is carried out using the Ffowcs Williams and Hawkings acoustic analogy. Simulations are performed for three different inflow conditions, U = 7, 10, 15 m/s. The capability of the Improved Delayed Detached Eddy Simulation turbulence model in massive separation is verified with available experimental data for pressure coefficient. The broadband noises of the turbulent boundary layers and the tonal noises due to the blade passing frequency are predicted via flow field noise simulation. The contribution of the thickness, loading and quadrupole noises are investigated, separately. The results indicated that there is a direct relation between the strength of the radiated noise and the wind speed. Furthermore, the effect of the receiver location on the Overall Sound Pressure Level is investigated

  3. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    Science.gov (United States)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  4. Rapid improvements in emotion regulation predict intensive treatment outcome for patients with bulimia nervosa and purging disorder.

    Science.gov (United States)

    MacDonald, Danielle E; Trottier, Kathryn; Olmsted, Marion P

    2017-10-01

    Rapid and substantial behavior change (RSBC) early in cognitive behavior therapy (CBT) for eating disorders is the strongest known predictor of treatment outcome. Rapid change in other clinically relevant variables may also be important. This study examined whether rapid change in emotion regulation predicted treatment outcomes, beyond the effects of RSBC. Participants were diagnosed with bulimia nervosa or purging disorder (N = 104) and completed ≥6 weeks of CBT-based intensive treatment. Hierarchical regression models were used to test whether rapid change in emotion regulation variables predicted posttreatment outcomes, defined in three ways: (a) binge/purge abstinence; (b) cognitive eating disorder psychopathology; and (c) depression symptoms. Baseline psychopathology and emotion regulation difficulties and RSBC were controlled for. After controlling for baseline variables and RSBC, rapid improvement in access to emotion regulation strategies made significant unique contributions to the prediction of posttreatment binge/purge abstinence, cognitive psychopathology of eating disorders, and depression symptoms. Individuals with eating disorders who rapidly improve their belief that they can effectively modulate negative emotions are more likely to achieve a variety of good treatment outcomes. This supports the formal inclusion of emotion regulation skills early in CBT, and encouraging patient beliefs that these strategies are helpful. © 2017 Wiley Periodicals, Inc.

  5. Map-based prediction of organic carbon in headwater streams improved by downstream observations from the river outlet

    Science.gov (United States)

    Temnerud, J.; von Brömssen, C.; Fölster, J.; Buffam, I.; Andersson, J.-O.; Nyberg, L.; Bishop, K.

    2016-01-01

    In spite of the great abundance and ecological importance of headwater streams, managers are usually limited by a lack of information about water chemistry in these headwaters. In this study we test whether river outlet chemistry can be used as an additional source of information to improve the prediction of the chemistry of upstream headwaters (size interquartile range (IQR)) of headwater stream TOC for a given catchment, based on a large number of candidate variables including sub-catchment characteristics from GIS, and measured river chemistry at the catchment outlet. The best candidate variables from the PLS models were then used in hierarchical linear mixed models (MM) to model TOC in individual headwater streams. Three predictor variables were consistently selected for the MM calibration sets: (1) proportion of forested wetlands in the sub-catchment (positively correlated with headwater stream TOC), (2) proportion of lake surface cover in the sub-catchment (negatively correlated with headwater stream TOC), and (3) river outlet TOC (positively correlated with headwater stream TOC). Including river outlet TOC improved predictions, with 5-15 % lower prediction errors than when using map information alone. Thus, data on water chemistry measured at river outlets offer information which can complement GIS-based modelling of headwater stream chemistry.

  6. A New Hybrid Method for Improving the Performance of Myocardial Infarction Prediction

    Directory of Open Access Journals (Sweden)

    Hojatollah Hamidi

    2016-06-01

    Full Text Available Abstract Introduction: Myocardial Infarction, also known as heart attack, normally occurs due to such causes as smoking, family history, diabetes, and so on. It is recognized as one of the leading causes of death in the world. Therefore, the present study aimed to evaluate the performance of classification models in order to predict Myocardial Infarction, using a feature selection method that includes Forward Selection and Genetic Algorithm. Materials & Methods: The Myocardial Infarction data set used in this study contains the information related to 519 visitors to Shahid Madani Specialized Hospital of Khorramabad, Iran. This data set includes 33 features. The proposed method includes a hybrid feature selection method in order to enhance the performance of classification algorithms. The first step of this method selects the features using Forward Selection. At the second step, the selected features were given to a genetic algorithm, in order to select the best features. Classification algorithms entail Ada Boost, Naïve Bayes, J48 decision tree and simpleCART are applied to the data set with selected features, for predicting Myocardial Infarction. Results: The best results have been achieved after applying the proposed feature selection method, which were obtained via simpleCART and J48 algorithms with the accuracies of 96.53% and 96.34%, respectively. Conclusion: Based on the results, the performances of classification algorithms are improved. So, applying the proposed feature selection method, along with classification algorithms seem to be considered as a confident method with respect to predicting the Myocardial Infarction.

  7. Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces

    Science.gov (United States)

    Gerus, Pauline; Sartori, Massimo; Besier, Thor F.; Fregly, Benjamin J.; Delp, Scott L.; Banks, Scott A.; Pandy, Marcus G.; D’Lima, Darryl D.; Lloyd, David G.

    2013-01-01

    Estimating tibiofemoral joint contact forces is important for understanding the initiation and progression of knee osteoarthritis. However, tibiofemoral contact force predictions are influenced by many factors including muscle forces and anatomical representations of the knee joint. This study aimed to investigate the influence of subject-specific geometry and knee joint kinematics on the prediction of tibiofemoral contact forces using a calibrated EMG-driven neuromusculoskeletal model of the knee. One participant fitted with an instrumented total knee replacement walked at a self-selected speed while medial and lateral tibiofemoral contact forces, ground reaction forces, whole-body kinematics, and lower-limb muscle activity were simultaneously measured. The combination of generic and subject-specific knee joint geometry and kinematics resulted in four different OpenSim models used to estimate muscle-tendon lengths and moment arms. The subject-specific geometric model was created from CT scans and the subject-specific knee joint kinematics representing the translation of the tibia relative to the femur was obtained from fluoroscopy. The EMG-driven model was calibrated using one walking trial, but with three different cost functions that tracked the knee flexion/extension moments with and without constraint over the estimated joint contact forces. The calibrated models then predicted the medial and lateral tibiofemoral contact forces for five other different walking trials. The use of subject-specific models with minimization of the peak tibiofemoral contact forces improved the accuracy of medial contact forces by 47% and lateral contact forces by 7%, respectively compared with the use of generic musculoskeletal model. PMID:24074941

  8. Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia.

    Science.gov (United States)

    Zainuddin, Mukti; Farhum, Aisjah; Safruddin, Safruddin; Selamat, Muhammad Banda; Sudirman, Sudirman; Nurdin, Nurjannah; Syamsuddin, Mega; Ridwan, Muhammad; Saitoh, Sei-Ichi

    2017-01-01

    Using remote sensing of sea surface temperature (SST), sea surface height anomaly (SSHA) and chlorophyll-a (Chl-a) together with catch data, we investigated the detection and persistence of important pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, Indonesia. We analyzed the data for the period between the northwest and southeast monsoon 2007-2011. A pelagic hotspot index was constructed from a model of multi-spectrum satellite-based oceanographic data in relation to skipjack fishing performance. Results showed that skipjack catch per unit efforts (CPUEs) increased significantly in areas of highest pelagic hotspot indices. The distribution and dynamics of habitat hotspots were detected by the synoptic measurements of SST, SSHA and Chl-a ranging from 29.5° to 31.5°C, from 2.5 to 12.5 cm and from 0.15 to 0.35 mg m-3, respectively. Total area of hotspots consistently peaked in May. Validation of skipjack CPUE predicted by our model against observed data from 2012 was highly significant. The key pelagic habitat corresponded with the Chl-a front, which could be related to the areas of relatively high prey abundance (enhanced feeding opportunity) for skipjack. We found that the area and persistence of the potential skipjack habitat hotspots for the 5 years were clearly identified by the 0.2 mg m-3 Chl-a isopleth, suggesting that the Chl-a front provides a key oceanographic indicator for global understanding on skipjack tuna habitat hotspots in the western tropical Pacific Ocean, especially within Coral Triangle tuna.

  9. Molecular Biogeochemistry of Modern and Ancient Marine Microbes

    Science.gov (United States)

    2010-02-01

    jahnii AF163148 Isochrysis sp. AB183617 Emiliania huxleyi M87327 Chrysochromulina campanulifera AJ246273 Chrysochromulina throndsenii AJ246277... Emiliania huxleyi Skeletonema costatum hotChlE9, syn-A hotChlC1, med4-B hotChlD9, med4-B hotChlD12, med4-B hotChlE5, syn-A hotChlE4, syn-A hotChlA9, med4...straight-chain C37- C39 methyl and ethyl ketones in marine sediments and a coccolithophore Emiliania huxleyi . Advances in Organic Geochemistry 1979

  10. Understanding the origin of the solar cyclic activity for an improved earth climate prediction

    Science.gov (United States)

    Turck-Chièze, Sylvaine; Lambert, Pascal

    This review is dedicated to the processes which could explain the origin of the great extrema of the solar activity. We would like to reach a more suitable estimate and prediction of the temporal solar variability and its real impact on the Earth climatic models. The development of this new field is stimulated by the SoHO helioseismic measurements and by some recent solar modelling improvement which aims to describe the dynamical processes from the core to the surface. We first recall assumptions on the potential different solar variabilities. Then, we introduce stellar seismology and summarize the main SOHO results which are relevant for this field. Finally we mention the dynamical processes which are presently introduced in new solar models. We believe that the knowledge of two important elements: (1) the magnetic field interplay between the radiative zone and the convective zone and (2) the role of the gravity waves, would allow to understand the origin of the grand minima and maxima observed during the last millennium. Complementary observables like acoustic and gravity modes, radius and spectral irradiance from far UV to visible in parallel to the development of 1D-2D-3D simulations will improve this field. PICARD, SDO, DynaMICCS are key projects for a prediction of the next century variability. Some helioseismic indicators constitute the first necessary information to properly describe the Sun-Earth climatic connection.

  11. Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

    Directory of Open Access Journals (Sweden)

    Espen A. F. Ihlen

    2018-03-01

    Full Text Available Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME, and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016. The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001 of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

  12. SU-G-IeP4-10: Microimaging for Different Degrees of Human Cavernous Hemangioma of Liver by Using In-Line Phase-Contrast Imaging CT

    International Nuclear Information System (INIS)

    Duan, J

    2016-01-01

    Purpose: Cavernous hemangioma of the liver (CHL) is the most common benign solid tumor of the liver. In this study, we quantitative assessment the different degrees of CHL from microscopic viewpoint by using in-line phase-contrast imaging CT (ILPCI-CT). Methods: The experiments were performed at x-ray imaging and biomedical application beamline (BL13W1) of Shanghai Synchrotron Radiation Facility (SSRF) in China. Three typical specimens at different stages, i.e., mild, moderate and severe human CHL were imaged using ILPCI-CT at 16keV without contrast agents. The 3D visualization of different degrees of CHL samples were presented using ILPCI-CT. Additionally, quantitative evaluation of the CHL features, such as the range of hepatic sinusoid equivalent diameters in different degrees of CHL samples, the ratio of the hepatic sinusoid to the CHL tissue, were measured. Results: The planar image clearly displayed the dilated hepatic sinusoids in microns. There was no normal hepatic vascular found in the all CHL samples. Different stages of CHL samples were presented with vivid shapes and stereoscopic effects by using 3D visualization. The equivalent diameters of hepatic sinusoids in three degrees CHL were different. The equivalent diameters of the hepatic sinusoids in mild CHL, range from 60 to 120 µm. The equivalent diameters of the hepatic sinusoids in moderate CHL, range from 65 to 190 µm. The equivalent diameters of the hepatic sinusoids in severe CHL, range from 95 to 215 µm. The ratio of the hepatic sinusoid to the mild, moderate and severe CHL tissue were 3%, 16% and 21%, respectively. Conclusion: The results show that the high degree of sensitivity of the ILPCI-CT technique and demonstrate the feasibility of accurate visualization of different stage human CHL. ILPCI-CT may offers a potential use in non-invasive study and analysis of CHL.

  13. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

    Science.gov (United States)

    Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

    2018-04-15

    Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

  14. Improved part-of-speech prediction in suffix analysis.

    Directory of Open Access Journals (Sweden)

    Mario Fruzangohar

    Full Text Available MOTIVATION: Predicting the part of speech (POS tag of an unknown word in a sentence is a significant challenge. This is particularly difficult in biomedicine, where POS tags serve as an input to training sophisticated literature summarization techniques, such as those based on Hidden Markov Models (HMM. Different approaches have been taken to deal with the POS tagger challenge, but with one exception--the TnT POS tagger--previous publications on POS tagging have omitted details of the suffix analysis used for handling unknown words. The suffix of an English word is a strong predictor of a POS tag for that word. As a pre-requisite for an accurate HMM POS tagger for biomedical publications, we present an efficient suffix prediction method for integration into a POS tagger. RESULTS: We have implemented a fully functional HMM POS tagger using experimentally optimised suffix based prediction. Our simple suffix analysis method, significantly outperformed the probability interpolation based TnT method. We have also shown how important suffix analysis can be for probability estimation of a known word (in the training corpus with an unseen POS tag; a common scenario with a small training corpus. We then integrated this simple method in our POS tagger and determined an optimised parameter set for both methods, which can help developers to optimise their current algorithm, based on our results. We also introduce the concept of counting methods in maximum likelihood estimation for the first time and show how counting methods can affect the prediction result. Finally, we describe how machine-learning techniques were applied to identify words, for which prediction of POS tags were always incorrect and propose a method to handle words of this type. AVAILABILITY AND IMPLEMENTATION: Java source code, binaries and setup instructions are freely available at http://genomes.sapac.edu.au/text_mining/pos_tagger.zip.

  15. Severity of mutant phenotype in a series of chlorophyll-deficient wheat mutants depends on light intensity and the severity of the block in chlorophyll synthesis.

    Science.gov (United States)

    Falbel, T G; Meehl, J B; Staehelin, L A

    1996-10-01

    Analyses of a series of allelic chlorina mutants of wheat (Triticum aestivum L.), which have partial blocks in chlorophyll (Chl) synthesis and, therefore, a limited Chl supply, reinforce the principle that Chl is required for the stable accumulation of Chl-binding proteins and that only reaction centers accumulate when the supply of Chl is severely limited. Depending on the rate of Chl accumulation (determined by the severity of the mutation) and on the rate of turnover of Chl and its precursors (determined by the environment in which the plant is grown), the mutants each reach an equilibrium of Chl synthesis and degradation. Together these mutants generate a spectrum of phenotypes. Under the harshest conditions (high illumination), plants with moderate blocks in Chl synthesis have membranes with very little Chl and Chl-proteins and membrane stacks resembling the thylakoids of the lethal xantha mutants of barely grown at low to medium light intensities (which have more severe blocks). In contrast, when grown under low-light conditions the same plants with moderate blocks have thylakoids resembling those of the wild type. The wide range of phenotypes of Chl b-deficient mutants has historically produced more confusion than enlightenment, but incomparable growth conditions can now explain the discrepancies reported in the literature.

  16. Improving Prediction of Large-scale Regime Transitions

    Science.gov (United States)

    Gyakum, J. R.; Roebber, P.; Bosart, L. F.; Honor, A.; Bunker, E.; Low, Y.; Hart, J.; Bliankinshtein, N.; Kolly, A.; Atallah, E.; Huang, Y.

    2017-12-01

    Cool season atmospheric predictability over the CONUS on subseasonal times scales (1-4 weeks) is critically dependent upon the structure, configuration, and evolution of the North Pacific jet stream (NPJ). The NPJ can be perturbed on its tropical side on synoptic time scales by recurving and transitioning tropical cyclones (TCs) and on subseasonal time scales by longitudinally varying convection associated with the Madden-Julian Oscillation (MJO). Likewise, the NPJ can be perturbed on its poleward side on synoptic time scales by midlatitude and polar disturbances that originate over the Asian continent. These midlatitude and polar disturbances can often trigger downstream Rossby wave propagation across the North Pacific, North America, and the North Atlantic. The project team is investigating the following multiscale processes and features: the spatiotemporal distribution of cyclone clustering over the Northern Hemisphere; cyclone clustering as influenced by atmospheric blocking and the phases and amplitudes of the major teleconnection indices, ENSO and the MJO; composite and case study analyses of representative cyclone clustering events to establish the governing dynamics; regime change predictability horizons associated with cyclone clustering events; Arctic air mass generation and modification; life cycles of the MJO; and poleward heat and moisture transports of subtropical air masses. A critical component of the study is weather regime classification. These classifications are defined through: the spatiotemporal clustering of surface cyclogenesis; a general circulation metric combining data at 500-hPa and the dynamic tropopause; Self Organizing Maps (SOM), constructed from dynamic tropopause and 850 hPa equivalent potential temperature data. The resultant lattice of nodes is used to categorize synoptic classes and their predictability, as well as to determine the robustness of the CFSv2 model climate relative to observations. Transition pathways between these

  17. A method for improving predictive modeling by taking into account lag time: Example of selenium bioaccumulation in a flowing system

    Energy Technology Data Exchange (ETDEWEB)

    Beckon, William N., E-mail: William_Beckon@fws.gov

    2016-07-15

    Highlights: • A method for estimating response time in cause-effect relationships is demonstrated. • Predictive modeling is appreciably improved by taking into account this lag time. • Bioaccumulation lag is greater for organisms at higher trophic levels. • This methodology may be widely applicable in disparate disciplines. - Abstract: For bioaccumulative substances, efforts to predict concentrations in organisms at upper trophic levels, based on measurements of environmental exposure, have been confounded by the appreciable but hitherto unknown amount of time it may take for bioaccumulation to occur through various pathways and across several trophic transfers. The study summarized here demonstrates an objective method of estimating this lag time by testing a large array of potential lag times for selenium bioaccumulation, selecting the lag that provides the best regression between environmental exposure (concentration in ambient water) and concentration in the tissue of the target organism. Bioaccumulation lag is generally greater for organisms at higher trophic levels, reaching times of more than a year in piscivorous fish. Predictive modeling of bioaccumulation is improved appreciably by taking into account this lag. More generally, the method demonstrated here may improve the accuracy of predictive modeling in a wide variety of other cause-effect relationships in which lag time is substantial but inadequately known, in disciplines as diverse as climatology (e.g., the effect of greenhouse gases on sea levels) and economics (e.g., the effects of fiscal stimulus on employment).

  18. A method for improving predictive modeling by taking into account lag time: Example of selenium bioaccumulation in a flowing system

    International Nuclear Information System (INIS)

    Beckon, William N.

    2016-01-01

    Highlights: • A method for estimating response time in cause-effect relationships is demonstrated. • Predictive modeling is appreciably improved by taking into account this lag time. • Bioaccumulation lag is greater for organisms at higher trophic levels. • This methodology may be widely applicable in disparate disciplines. - Abstract: For bioaccumulative substances, efforts to predict concentrations in organisms at upper trophic levels, based on measurements of environmental exposure, have been confounded by the appreciable but hitherto unknown amount of time it may take for bioaccumulation to occur through various pathways and across several trophic transfers. The study summarized here demonstrates an objective method of estimating this lag time by testing a large array of potential lag times for selenium bioaccumulation, selecting the lag that provides the best regression between environmental exposure (concentration in ambient water) and concentration in the tissue of the target organism. Bioaccumulation lag is generally greater for organisms at higher trophic levels, reaching times of more than a year in piscivorous fish. Predictive modeling of bioaccumulation is improved appreciably by taking into account this lag. More generally, the method demonstrated here may improve the accuracy of predictive modeling in a wide variety of other cause-effect relationships in which lag time is substantial but inadequately known, in disciplines as diverse as climatology (e.g., the effect of greenhouse gases on sea levels) and economics (e.g., the effects of fiscal stimulus on employment).

  19. Comparison of measured and predicted thermal mixing tests using improved finite difference technique

    International Nuclear Information System (INIS)

    Hassan, Y.A.; Rice, J.G.; Kim, J.H.

    1983-01-01

    The numerical diffusion introduced by the use of upwind formulations in the finite difference solution of the flow and energy equations for thermal mixing problems (cold water injection after small break LOCA in a PWR) was examined. The relative importance of numerical diffusion in the flow equations, compared to its effect on the energy equation was demonstrated. The flow field equations were solved using both first order accurate upwind, and second order accurate differencing schemes. The energy equation was treated using the conventional upwind and a mass weighted skew upwind scheme. Results presented for a simple test case showed that, for thermal mixing problems, the numerical diffusion was most significant in the energy equation. The numerical diffusion effect in the flow field equations was much less significant. A comparison of predictions using the skew upwind and the conventional upwind with experimental data from a two dimensional thermal mixing text are presented. The use of the skew upwind scheme showed a significant improvement in the accuracy of the steady state predicted temperatures. (orig./HP)

  20. NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

    Science.gov (United States)

    Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.

    2016-12-01

    NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at http://airquality.weather.gov. NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics

  1. Repeated assessments of symptom severity improve predictions for risk of death among patients with cancer.

    Science.gov (United States)

    Sutradhar, Rinku; Atzema, Clare; Seow, Hsien; Earle, Craig; Porter, Joan; Barbera, Lisa

    2014-12-01

    Although prior studies show the importance of self-reported symptom scores as predictors of cancer survival, most are based on scores recorded at a single point in time. To show that information on repeated assessments of symptom severity improves predictions for risk of death and to use updated symptom information for determining whether worsening of symptom scores is associated with a higher hazard of death. This was a province-based longitudinal study of adult outpatients who had a cancer diagnosis and had assessments of symptom severity. We implemented a time-to-death Cox model with a time-varying covariate for each symptom to account for changing symptom scores over time. This model was compared with that using only a time-fixed (baseline) covariate for each symptom. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive performance of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. This study had 66,112 patients diagnosed with cancer and more than 310,000 assessments of symptoms. The use of repeated assessments of symptom scores improved predictions for risk of death compared with using only baseline symptom scores. Increased pain and fatigue and reduced appetite were the strongest predictors for death. If available, researchers should consider including changing information on symptom scores, as opposed to only baseline information on symptom scores, when examining hazard of death among patients with cancer. Worsening of pain, fatigue, and appetite may be a flag for impending death. Copyright © 2014 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  2. Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

    Science.gov (United States)

    Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

    2018-04-01

    With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

  3. Dynamic Filtering Improves Attentional State Prediction with fNIRS

    Science.gov (United States)

    Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

    2016-01-01

    Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

  4. Predictive values of early rest/24 hour delay Tl-201 perfusion SPECT for wall motion improvement in patients with acute myocardial infarction after reperfusion

    International Nuclear Information System (INIS)

    Hyun, In Young; Kwan, June

    1998-01-01

    We studied early rest/24 hour delay Tl-201 perfusion SPECT for prediction of wall motion improvement after reperfusion in patients with acute myocardial infarction. Among 17 patients (male/female=11/6, age: 59±13) with acute myocardial infarction, 15 patients were treated with percutaneous transcoronary angioplasty (direct:2, delay:11) and intravenous urokinase (2). Spontaneous resolution occurred in infarct related arteries of 2 patients. We confirmed TIMI 3 flow of infarct-related artery after reperfusion in all patients with coronary angiography. We performed rest Tl-201 perfusion SPECT less then 6 hours after reperfusion and delay Tl-201 perfusion SPECT next day. Tl-201 uptake was visually graded as 4 point score from normal (0) to severe defect (3). Rest Tl-201 uptake ≤2 or combination of rest Tl-201 uptake ≤2 or late reversibility were considered to be viable. Myocardial wall motion was graded as 5 point score from normal (1) to dyskinesia (5). Myocardial wall motion was considered to be improved when a segment showed an improvement ≥1 grade in follow up echo compared with the baseline values. Among 98 segments with wall motion abnormality, the severity of myocardial wall motion decrease was as follow: mild hypokinesia: 18/98 (18%), severe hypokinesia: 28/98 (29%), akinesia: 51/98 (52%), dyskinesia: 1/98 (1%). The wall motion improved in 85%. Redistribution (13%), and reverse redistribution (4%) were observed in 24 hour delay SPECT. Positive predictive value (PPV) and negative predictive value (NPV) of combination of late reversibility and rest Tl-201uptake were 99%, and 54%.PPV and NPV of rest Tl-201 uptake were 100% and 52% respectively. Predictive values of comibination of rest Tl-201 uptake and late reversibility were not significantly different compared with predictive values of rest Tl-201 uptake only. We conclude that early Tl-201 perfusion SPECT predict myocardial wall motion improvement with excellent positive but relatively low negative

  5. Improvement of bottom-quark associated Higgs-boson production predictions for LHC using HERA data

    Energy Technology Data Exchange (ETDEWEB)

    Andrii, Gizhko; Achim, Geiser [Deutsches Elektronen-Synchrotron, Hamburg (Germany)

    2016-07-01

    The dependence of the inclusive total cross section of the bottom-quark associated Higgs-boson production predictions at the LHC, pp → (b anti b)H+X on the treatment of the beauty quark mass is studied in the context of CMS measurements. For two different schemes (four flavour scheme (4FS) and five flavour scheme (5FS)) the theoretical uncertainty due to the beauty quark mass is estimated, and the potential improvement arising from a QCD analysis of HERA beauty data is demonstrated.

  6. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

    Science.gov (United States)

    Zhu, Xiaolei; Xiong, Yi; Kihara, Daisuke

    2015-03-01

    Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects. We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patches that characterize physicochemical properties of the local regions, the method can identify binding pockets of the same ligand even if they do not share globally similar shapes. Properties of local patches are represented by an efficient mathematical representation, 3D Zernike Descriptor. Patch-Surfer2.0 has significant technical improvements over our previous prototype, which includes a new feature that captures approximate patch position with a geodesic distance histogram. Moreover, we constructed a large comprehensive database of ligand binding pockets that will be searched against by a query. The benchmark shows better performance of Patch-Surfer2.0 over existing methods. http://kiharalab.org/patchsurfer2.0/ CONTACT: dkihara@purdue.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects.

    Directory of Open Access Journals (Sweden)

    Chuanyu Sun

    Full Text Available Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs. The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both

  8. Physiological and biochemical studies on the yellowing of spruce trees in higher altitudes. Pt. 1. Protection of pigments in the light-harvesting Chl-a/b-protein against photooxidation - the role of apoprotein and pigment organisation

    Energy Technology Data Exchange (ETDEWEB)

    Siefermanns-Harms, D.; Horsch, F.; Filby, W.G.; Fund, N.; Gross, S.; Hanisch, B.; Kilz, E.; Seidel, A.

    1988-04-01

    The light-harvesting Chl-a/b-protein complex (LHC) from Spinacea oleracia, Lactuca sativa and Picea abies is stable under strong white light (> 350 nm, 1000 w/m/sub 2/). Therefore, LHC preparations were used to examine requirements for the protection of LHC-bound pigments from photooxidation. - The presence of carotenoids in the LHC and their arrangement in close proximity with the chlorophylls are not sufficient for pigment protection under light. - An intact LHC apoprotein is required to protect the pigments from photooxidation. Evidently, the intact LHC apoprotein represents a barrier for O/sub 2/ limiting O/sub 2/ access to the microenvironment of the pigments. - The composition of the pigment fraction destroyed under light depends on the state of the LHC. If only the integrity of the apoprotein is impaired, both, chlorophylls and carotenoids are subjected to photooxidation.

  9. Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on the Improved Backpropagation Neural Network with Momentum

    Directory of Open Access Journals (Sweden)

    Yi-jun Liu

    2015-12-01

    Full Text Available Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

  10. Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach-A Case Study for the City of Wuhan in China.

    Science.gov (United States)

    Gao, Yuan; Zhang, Chuanrong; He, Qingsong; Liu, Yaolin

    2017-06-15

    Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study-simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan.

  11. Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach—A Case Study for the City of Wuhan in China

    Science.gov (United States)

    Gao, Yuan; Zhang, Chuanrong; He, Qingsong; Liu, Yaolin

    2017-01-01

    Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study—simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan. PMID:28617348

  12. Prediction of wall motion improvement after coronary revascularization in patients with postmyocardial infarction. Diagnostic value of dobutamine stress echocardiography and myocardial contrast echocardiography

    International Nuclear Information System (INIS)

    Waku, Sachiko; Ohkubo, Tomoyuki; Takada, Kiyoshi; Ishihara, Tadashi; Ohsawa, Nakaaki; Adachi, Itaru; Narabayashi, Isamu

    1997-01-01

    The diagnostic value of dobutamine stress echocardiography, myocardial contrast echocardiography and dipyridamole stress thallium-201 single photon emission computed tomography (SPECT) for predicting recovery of wall motion abnormality after revascularization was evaluated in 13 patients with postmyocardial infarction. Seventeen segments showed severe wall motion abnormalities before revascularization. Nine segments which had relatively good Tl uptake on delayed SPECT images despite severely abnormal wall motion were opacified during myocardial contrast echocardiography, and showed improved wall motion after revascularization. In contrast, three segments which had poor Tl uptake and severely abnormal wall motion were not opacified during myocardial contrast echocardiography, and showed no improvement in wall motion during dobutamine stress echocardiography and after revascularization. The following three findings were assumed to be signs of myocardial viability: good Tl uptake on delayed SPECT images, improved wall motion by dobutamine stress echocardiography, and positive opacification of the myocardium by myocardiai contrast echocardiography. Myocardial contrast echocardiography had the highest sensitivity (100%) and negative predictive value (100%). Delayed SPECT images had the highest specificity (100%) and positive predictive value (100%). Dobutamine stress echocardiography had a sensitivity of 83.0%, specificity of 80.0%, positive predictive value of 90.9%, and negative predictive value of 66.7%, respectively. Myocardial contrast echocardiography showed the lowest specificity (60.0%). The techniques of dobutamine stress echocardiography and SPECT, though noninvasive, may underestimate wall motion improvement after revascularization. Further examination by myocardial contrast echocardiography is recommended to assess myocardial viability for determining the indications for coronary revascularization in spite of its invasiveness. (author)

  13. Improvement of cardiovascular risk prediction: time to review current knowledge, debates, and fundamentals on how to assess test characteristics.

    Science.gov (United States)

    Romanens, Michel; Ackermann, Franz; Spence, John David; Darioli, Roger; Rodondi, Nicolas; Corti, Roberto; Noll, Georg; Schwenkglenks, Matthias; Pencina, Michael

    2010-02-01

    Cardiovascular risk assessment might be improved with the addition of emerging, new tests derived from atherosclerosis imaging, laboratory tests or functional tests. This article reviews relative risk, odds ratios, receiver-operating curves, posttest risk calculations based on likelihood ratios, the net reclassification improvement and integrated discrimination. This serves to determine whether a new test has an added clinical value on top of conventional risk testing and how this can be verified statistically. Two clinically meaningful examples serve to illustrate novel approaches. This work serves as a review and basic work for the development of new guidelines on cardiovascular risk prediction, taking into account emerging tests, to be proposed by members of the 'Taskforce on Vascular Risk Prediction' under the auspices of the Working Group 'Swiss Atherosclerosis' of the Swiss Society of Cardiology in the future.

  14. Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle.

    Science.gov (United States)

    Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L

    2016-05-01

    Product quality is a high priority for the beef industry because of its importance as a major driver of consumer demand for beef and the ability of the industry to improve it. A 2-prong approach based on implementation of a genetic program to improve eating quality and a system to communicate eating quality and increase the probability that consumers' eating quality expectations are met is outlined. The objectives of this study were 1) to identify the best carcass and meat composition traits to be used in a selection program to improve eating quality and 2) to develop a relatively small number of classes that reflect real and perceptible differences in eating quality that can be communicated to consumers and identify a subset of carcass and meat composition traits with the highest predictive accuracy across all eating quality classes. Carcass traits, meat composition, including Warner-Bratzler shear force (WBSF), intramuscular fat content (IMFC), trained sensory panel scores, and mineral composition traits of 1,666 Angus cattle were used in this study. Three eating quality indexes, EATQ1, EATQ2, and EATQ3, were generated by using different weights for the sensory traits (emphasis on tenderness, flavor, and juiciness, respectively). The best model for predicting eating quality explained 37%, 9%, and 19% of the variability of EATQ1, EATQ2, and EATQ3, and 2 traits, WBSF and IMFC, accounted for most of the variability explained by the best models. EATQ1 combines tenderness, juiciness, and flavor assessed by trained panels with 0.60, 0.15, and 0.25 weights, best describes North American consumers, and has a moderate heritability (0.18 ± 0.06). A selection index (I= -0.5[WBSF] + 0.3[IMFC]) based on phenotypic and genetic variances and covariances can be used to improve eating quality as a correlated trait. The 3 indexes (EATQ1, EATQ2, and EATQ3) were used to generate 3 equal (33.3%) low, medium, and high eating quality classes, and linear combinations of traits that

  15. Improvement of NO and CO predictions for a homogeneous combustion SI engine using a novel emissions model

    International Nuclear Information System (INIS)

    Karvountzis-Kontakiotis, Apostolos; Ntziachristos, Leonidas

    2016-01-01

    Highlights: • Presentation of a novel emissions model to predict pollutants formation in engines. • Model based on detailed chemistry, requires no application-specific calibration. • Combined with 0D and 1D combustion models with low additional computational cost. • Demonstrates accurate prediction of cyclic variability of pollutants emissions. - Abstract: This study proposes a novel emissions model for the prediction of spark ignition (SI) engine emissions at homogeneous combustion conditions, using post combustion analysis and a detailed chemistry mechanism. The novel emissions model considers an unburned and a burned zone, where the latter is considered as a homogeneous reactor and is modeled using a detailed chemical kinetics mechanism. This allows detailed emission predictions at high speed practically based only on combustion pressure and temperature profiles, without the need for calibration of the model parameters. The predictability of the emissions model is compared against the extended Zeldovich mechanism for NO and a simplified two-step reaction kinetic model for CO, which both constitute the most widespread existing approaches in the literature. Under various engine load and speed conditions examined, the mean error in NO prediction was 28% for the existing models and less than 1.3% for the new model proposed. The novel emissions model was also used to predict emissions variation due to cyclic combustion variability and demonstrated mean prediction error of 6% and 3.6% for NO and CO respectively, compared to 36% (NO) and 67% (CO) for the simplified model. The results show that the emissions model proposed offers substantial improvements in the prediction of the results without significant increase in calculation time.

  16. Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix

    Directory of Open Access Journals (Sweden)

    Qingli Li

    2015-01-01

    Full Text Available To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.

  17. An Improved Metabolism Grey Model for Predicting Small Samples with a Singular Datum and Its Application to Sulfur Dioxide Emissions in China

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2016-01-01

    Full Text Available This study proposes an improved metabolism grey model [IMGM(1,1] to predict small samples with a singular datum, which is a common phenomenon in daily economic data. This new model combines the fitting advantage of the conventional GM(1,1 in small samples and the additional advantages of the MGM(1,1 in new real-time data, while overcoming the limitations of both the conventional GM(1,1 and MGM(1,1 when the predicted results are vulnerable at any singular datum. Thus, this model can be classified as an improved grey prediction model. Its improvements are illustrated through a case study of sulfur dioxide emissions in China from 2007 to 2013 with a singular datum in 2011. Some features of this model are presented based on the error analysis in the case study. Results suggest that if action is not taken immediately, sulfur dioxide emissions in 2016 will surpass the standard level required by the Twelfth Five-Year Plan proposed by the China State Council.

  18. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

    Directory of Open Access Journals (Sweden)

    Thomas W. Frazier

    2014-03-01

    Full Text Available This report evaluates whether classification tree algorithms (CTA may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD. Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS cohort (629 youth, 148 with BPSD and 481 without BPSD. Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4% relative to logistic regression (77.6%. However, CTA showed increased sensitivity (0.28 vs. 0.18 at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%. High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%. Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data; these may increase the clinical utility of CTA models further.

  19. Pigment organization and their interactions in reaction centers of photosystem II: optical spectroscopy at 6 K of reaction centers with modified pheophytin composition.

    Science.gov (United States)

    Germano, M; Shkuropatov, A Y; Permentier, H; de Wijn, R; Hoff, A J; Shuvalov, V A; van Gorkom, H J

    2001-09-25

    Photosystem II reaction centers (RC) with selectively exchanged pheophytin (Pheo) molecules as described in [Germano, M., Shkuropatov, A. Ya., Permentier, H., Khatypov, R. A., Shuvalov, V. A., Hoff, A. J., and van Gorkom, H. J. (2000) Photosynth. Res. 64, 189-198] were studied by low-temperature absorption, linear and circular dichroism, and triplet-minus-singlet absorption-difference spectroscopy. The ratio of extinction coefficients epsilon(Pheo)/epsilon(Chl) for Q(Y) absorption in the RC is approximately 0.40 at 6 K and approximately 0.45 at room temperature. The presence of 2 beta-carotenes, one parallel and one perpendicular to the membrane plane, is confirmed. Absorption at 670 nm is due to the perpendicular Q(Y) transitions of the two peripheral chlorophylls (Chl) and not to either Pheo. The "core" pigments, two Pheo and four Chl absorb in the 676-685 nm range. Delocalized excited states as predicted by the "multimer model" are seen in the active branch. The inactive Pheo and the nearby Chl, however, mainly contribute localized transitions at 676 and 680 nm, respectively, although large CD changes indicate that exciton interactions are present on both branches. Replacement of the active Pheo prevents triplet formation, causes an LD increase at 676 and 681 nm, a blue-shift of 680 nm absorbance, and a bleach of the 685 nm exciton band. The triplet state is mainly localized on the Chl corresponding to B(A) in purple bacteria. Both Pheo Q(Y) transitions are oriented out of the membrane plane. Their Q(X) transitions are parallel to that plane, so that the Pheos in PSII are structurally similar to their homologues in purple bacteria.

  20. Prefrontal Cortex Structure Predicts Training-Induced Improvements in Multitasking Performance.

    Science.gov (United States)

    Verghese, Ashika; Garner, K G; Mattingley, Jason B; Dux, Paul E

    2016-03-02

    The ability to perform multiple, concurrent tasks efficiently is a much-desired cognitive skill, but one that remains elusive due to the brain's inherent information-processing limitations. Multitasking performance can, however, be greatly improved through cognitive training (Van Selst et al., 1999, Dux et al., 2009). Previous studies have examined how patterns of brain activity change following training (for review, see Kelly and Garavan, 2005). Here, in a large-scale human behavioral and imaging study of 100 healthy adults, we tested whether multitasking training benefits, assessed using a standard dual-task paradigm, are associated with variability in brain structure. We found that the volume of the rostral part of the left dorsolateral prefrontal cortex (DLPFC) predicted an individual's response to training. Critically, this association was observed exclusively in a task-specific training group, and not in an active-training control group. Our findings reveal a link between DLPFC structure and an individual's propensity to gain from training on a task that taps the limits of cognitive control. Cognitive "brain" training is a rapidly growing, multibillion dollar industry (Hayden, 2012) that has been touted as the panacea for a variety of disorders that result in cognitive decline. A key process targeted by such training is "cognitive control." Here, we combined an established cognitive control measure, multitasking ability, with structural brain imaging in a sample of 100 participants. Our goal was to determine whether individual differences in brain structure predict the extent to which people derive measurable benefits from a cognitive training regime. Ours is the first study to identify a structural brain marker-volume of left hemisphere dorsolateral prefrontal cortex-associated with the magnitude of multitasking performance benefits induced by training at an individual level. Copyright © 2016 the authors 0270-6474/16/362638-08$15.00/0.

  1. Graphene quantum dots /LaCoO{sub 3}/attapulgite heterojunction photocatalysts with improved photocatalytic activity

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Wei; Li, Xiazhang [Changzhou University, Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology, School of Petrochemical Engineering, Changzhou (China); Chinese Academy of Science, R and D Center of Xuyi Attapulgite Applied Technology, Xuyi (China)

    2017-04-15

    A new nanocomposite of graphene quantum dots/LaCoO{sub 3}/attapulgite (GQDs/LaCoO{sub 3}/ATP) was prepared by a facile impregnation method and was applied to degradation of the organic pollutants as photocatalyst under visible light irradiation. Multiple techniques were used to characterize the structures, morphologies and photocatalytic activities of samples. The photocatalytic activity of the GQDs/LaCoO{sub 3}/ATP nanocomposites was effectively evaluated using Methylene blue (MB), antibiotic agent chlortetracycline (CHL) and tetracycline hydrochloride (TC). The as-synthesized GQDs/LaCoO{sub 3}/ATP nanocomposites exhibited higher photocatalytic activities than LaCoO{sub 3}/ATP, which showed a broad spectrum of photocatalytic degradation activity. The results of ESR and free radicals trapping experiments indicated that {sup circle} OH and h {sup +} were the main species for the photocatalytic degradation. GQDs played a significant role in the photocatalytic activity improvement of LaCoO{sub 3}/ATP, increasing the visible light absorption, slowing the recombination and improving the charge transfer. (orig.)

  2. Pathological fracture prediction in patients with metastatic lesions can be improved with quantitative computed tomography based computer models

    NARCIS (Netherlands)

    Tanck, Esther; van Aken, Jantien B.; van der Linden, Yvette M.; Schreuder, H.W. Bart; Binkowski, Marcin; Huizenga, Henk; Verdonschot, Nico

    2009-01-01

    Purpose: In clinical practice, there is an urgent need to improve the prediction of fracture risk for cancer patients with bone metastases. The methods that are currently used to estimate fracture risk are dissatisfying, hence affecting the quality of life of patients with a limited life expectancy.

  3. On the importance of paleoclimate modelling for improving predictions of future climate change

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2009-12-01

    Full Text Available We use an ensemble of runs from the MIROC3.2 AGCM with slab-ocean to explore the extent to which mid-Holocene simulations are relevant to predictions of future climate change. The results are compared with similar analyses for the Last Glacial Maximum (LGM and pre-industrial control climate. We suggest that the paleoclimate epochs can provide some independent validation of the models that is also relevant for future predictions. Considering the paleoclimate epochs, we find that the stronger global forcing and hence larger climate change at the LGM makes this likely to be the more powerful one for estimating the large-scale changes that are anticipated due to anthropogenic forcing. The phenomena in the mid-Holocene simulations which are most strongly correlated with future changes (i.e., the mid to high northern latitude land temperature and monsoon precipitation do, however, coincide with areas where the LGM results are not correlated with future changes, and these are also areas where the paleodata indicate significant climate changes have occurred. Thus, these regions and phenomena for the mid-Holocene may be useful for model improvement and validation.

  4. An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

    Directory of Open Access Journals (Sweden)

    L. Cenci

    2017-11-01

    Full Text Available The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT, characterized by low spatial/high temporal resolution, the Sentinel 1 (S1 mission provides an excellent opportunity to monitor systematically soil moisture (SM at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be

  5. An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture-data assimilation

    Science.gov (United States)

    Cenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzareno

    2017-11-01

    The assimilation of satellite-derived soil moisture estimates (soil moisture-data assimilation, SM-DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM-DA in recent years (e.g. the Advanced SCATterometer - ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM-DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014-February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM-DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM-DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further

  6. SU-D-BRB-02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality

    Energy Technology Data Exchange (ETDEWEB)

    Robertson, SP; Moore, JA; Hui, X; Cheng, Z; McNutt, TR [Johns Hopkins University, Baltimore, MD (United States); DeWeese, TL; Tran, P; Quon, H [John Hopkins Hospital, Baltimore, MD (United States); Bzdusek, K [Philips, Fitchburg, WI (United States); Kumar, P [Philips India Limited, Bangalore, Karnataka (India)

    2016-06-15

    Purpose: Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality. Methods: Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan-A: traditional IMRT optimization using clinically relevant default objectives; Plan-B: traditional IMRT optimization using database dose predictions; Plan-C: autoplanning using default objectives; and Plan-D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volume (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%). Results: All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman’s χ{sup 2} ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A-D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A-D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B-D were significantly different from Plan-A for 3, 17, and 19 objectives, respectively. Plans C-D were also significantly different from Plan-B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan-D provided significantly lower dose than plan C (p = 0.003). Conclusion: Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted

  7. Improving prediction of Alzheimer’s disease using patterns of cortical thinning and homogenizing images according to disease stage

    DEFF Research Database (Denmark)

    Eskildsen, Simon Fristed; Coupé, Pierrick; García-Lorenzo, Daniel

    Predicting Alzheimer’s disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain...... and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study, we investigated the possibility of using patterns of cortical thickness measurements for predicting AD...... of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD....

  8. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Science.gov (United States)

    Drzewiecki, Wojciech

    2016-12-01

    In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

  9. Numerical prediction of cavitating flow around a hydrofoil using pans and improved shear stress transport k-omega model

    Directory of Open Access Journals (Sweden)

    Zhang De-Sheng

    2015-01-01

    Full Text Available The prediction accuracies of partially-averaged Navier-Stokes model and improved shear stress transport k-ω turbulence model for simulating the unsteady cavitating flow around the hydrofoil were discussed in this paper. Numerical results show that the two turbulence models can effectively reproduce the cavitation evolution process. The numerical prediction for the cycle time of cavitation inception, development, detachment, and collapse agrees well with the experimental data. It is found that the vortex pair induced by the interaction between the re-entrant jet and mainstream is responsible for the instability of the cavitation shedding flow.

  10. Specific Components of Pediatricians' Medication-Related Care Predict Attention-Deficit/Hyperactivity Disorder Symptom Improvement.

    Science.gov (United States)

    Epstein, Jeffery N; Kelleher, Kelly J; Baum, Rebecca; Brinkman, William B; Peugh, James; Gardner, William; Lichtenstein, Phil; Langberg, Joshua M

    2017-06-01

    The development of attention-deficit/hyperactivity disorder (ADHD) care quality measurements is a prerequisite to improving the quality of community-based pediatric care of children with ADHD. Unfortunately, the evidence base for existing ADHD care quality metrics is poor. The objective of this study was to identify which components of ADHD care best predict patient outcomes. Parents of 372 medication-naïve children in grades 1 to 5 presenting to their community-based pediatrician (N = 195) for an ADHD-related concern and who were subsequently prescribed ADHD medication were identified. Parents completed the Vanderbilt ADHD Parent Rating Scale (VAPRS) at the time ADHD was raised as a concern and then approximately 12 months after starting ADHD medication. Each patient's chart was reviewed to measure 12 different components of ADHD care. Across all children, the mean decrease in VAPRS total symptom score during the first year of treatment was 11.6 (standard deviation 10.1). Of the 12 components of ADHD care, shorter times to first contact and more teacher ratings collected in the first year of treatment significantly predicted greater decreases in patient total symptom scores. Notably, it was timeliness of contacts, defined as office visits, phone calls, or email communication, that predicted more ADHD symptom decreases. Office visits alone, in terms of number or timeliness, did not predict patient outcomes. The magnitude of ADHD symptom decrease that can be achieved with the use of ADHD medications was associated with specific components of ADHD care. Future development and modifications of ADHD quality care metrics should include these ADHD care components. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

  11. The approximate number system and domain-general abilities as predictors of math ability in children with normal hearing and hearing loss.

    Science.gov (United States)

    Bull, Rebecca; Marschark, Marc; Nordmann, Emily; Sapere, Patricia; Skene, Wendy A

    2018-06-01

    Many children with hearing loss (CHL) show a delay in mathematical achievement compared to children with normal hearing (CNH). This study examined whether there are differences in acuity of the approximate number system (ANS) between CHL and CNH, and whether ANS acuity is related to math achievement. Working memory (WM), short-term memory (STM), and inhibition were considered as mediators of any relationship between ANS acuity and math achievement. Seventy-five CHL were compared with 75 age- and gender-matched CNH. ANS acuity, mathematical reasoning, WM, and STM of CHL were significantly poorer compared to CNH. Group differences in math ability were no longer significant when ANS acuity, WM, or STM was controlled. For CNH, WM and STM fully mediated the relationship of ANS acuity to math ability; for CHL, WM and STM only partially mediated this relationship. ANS acuity, WM, and STM are significant contributors to hearing status differences in math achievement, and to individual differences within the group of CHL. Statement of contribution What is already known on this subject? Children with hearing loss often perform poorly on measures of math achievement, although there have been few studies focusing on basic numerical cognition in these children. In typically developing children, the approximate number system predicts math skills concurrently and longitudinally, although there have been some contradictory findings. Recent studies suggest that domain-general skills, such as inhibition, may account for the relationship found between the approximate number system and math achievement. What does this study adds? This is the first robust examination of the approximate number system in children with hearing loss, and the findings suggest poorer acuity of the approximate number system in these children compared to hearing children. The study addresses recent issues regarding the contradictory findings of the relationship of the approximate number system to math ability

  12. Noninvasive work of breathing improves prediction of post-extubation outcome.

    Science.gov (United States)

    Banner, Michael J; Euliano, Neil R; Martin, A Daniel; Al-Rawas, Nawar; Layon, A Joseph; Gabrielli, Andrea

    2012-02-01

    We hypothesized that non-invasively determined work of breathing per minute (WOB(N)/min) (esophageal balloon not required) may be useful for predicting extubation outcome, i.e., appropriate work of breathing values may be associated with extubation success, while inappropriately increased values may be associated with failure. Adult candidates for extubation were divided into a training set (n = 38) to determine threshold values of indices for assessing extubation and a prospective validation set (n = 59) to determine the predictive power of the threshold values for patients successfully extubated and those who failed extubation. All were evaluated for extubation during a spontaneous breathing trial (5 cmH(2)O pressure support ventilation, 5 cmH(2)O positive end expiratory pressure) using routine clinical practice standards. WOB(N)/min data were blinded to attending physicians. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive and negative predictive values of all extubation indices were determined. AUC for WOB(N)/min was 0.96 and significantly greater (p indices. WOB(N)/min had a specificity of 0.83, the highest sensitivity at 0.96, positive predictive value at 0.84, and negative predictive value at 0.96 compared to all indices. For 95% of those successfully extubated, WOB(N)/min was ≤10 J/min. WOB(N)/min had the greatest overall predictive accuracy for extubation compared to traditional indices. WOB(N)/min warrants consideration for use in a complementary manner with spontaneous breathing pattern data for predicting extubation outcome.

  13. An improved method for predicting the lightning performance of high and extra-high-voltage substation shielding

    Science.gov (United States)

    Vinh, T.

    1980-08-01

    There is a need for better and more effective lightning protection for transmission and switching substations. In the past, a number of empirical methods were utilized to design systems to protect substations and transmission lines from direct lightning strokes. The need exists for convenient analytical lightning models adequate for engineering usage. In this study, analytical lightning models were developed along with a method for improved analysis of the physical properties of lightning through their use. This method of analysis is based upon the most recent statistical field data. The result is an improved method for predicting the occurrence of sheilding failure and for designing more effective protection for high and extra high voltage substations from direct strokes.

  14. Improve accuracy and sensibility in glycan structure prediction by matching glycan isotope abundance

    International Nuclear Information System (INIS)

    Xu Guang; Liu Xin; Liu Qingyan; Zhou Yanhong; Li Jianjun

    2012-01-01

    Highlights: ► A glycan isotope pattern recognition strategy for glycomics. ► A new data preprocessing procedure to detect ion peaks in a giving MS spectrum. ► A linear soft margin SVM classification for isotope pattern recognition. - Abstract: Mass Spectrometry (MS) is a powerful technique for the determination of glycan structures and is capable of providing qualitative and quantitative information. Recent development in computational method offers an opportunity to use glycan structure databases and de novo algorithms for extracting valuable information from MS or MS/MS data. However, detecting low-intensity peaks that are buried in noisy data sets is still a challenge and an algorithm for accurate prediction and annotation of glycan structures from MS data is highly desirable. The present study describes a novel algorithm for glycan structure prediction by matching glycan isotope abundance (mGIA), which takes isotope masses, abundances, and spacing into account. We constructed a comprehensive database containing 808 glycan compositions and their corresponding isotope abundance. Unlike most previously reported methods, not only did we take into count the m/z values of the peaks but also their corresponding logarithmic Euclidean distance of the calculated and detected isotope vectors. Evaluation against a linear classifier, obtained by training mGIA algorithm with datasets of three different human tissue samples from Consortium for Functional Glycomics (CFG) in association with Support Vector Machine (SVM), was proposed to improve the accuracy of automatic glycan structure annotation. In addition, an effective data preprocessing procedure, including baseline subtraction, smoothing, peak centroiding and composition matching for extracting correct isotope profiles from MS data was incorporated. The algorithm was validated by analyzing the mouse kidney MS data from CFG, resulting in the identification of 6 more glycan compositions than the previous annotation

  15. Seasonal climate prediction for North Eurasia

    International Nuclear Information System (INIS)

    Kryjov, Vladimir N

    2012-01-01

    An overview of the current status of the operational seasonal climate prediction for North Eurasia is presented. It is shown that the performance of existing climate models is rather poor in seasonal prediction for North Eurasia. Multi-model ensemble forecasts are more reliable than single-model ones; however, for North Eurasia they tend to be close to climatological ones. Application of downscaling methods may improve predictions for some locations (or regions). However, general improvement of the reliability of seasonal forecasts for North Eurasia requires improvement of the climate prediction models. (letter)

  16. Leaf gas exchange, fv/fm ratio, ion content and growth conditions of the two moringa species under magnetic water treatment

    International Nuclear Information System (INIS)

    Hasan, M.M.; Alharby, H.F.; Hajar, A.; Hakeem, K.R.

    2017-01-01

    The current greenhouse experiment investigates the role of magnetic water on the two Moringa species (Moringa oleifera and Moringa peregrina). Both species were exposed to the magnetic field (30 mT). The magnetic water increased the plant height, leaf number, leaflet number, and internode distances in both the species, respectively. Relative water content (RWC) and leaf area in both the species showed changes under magnetic water treatment. The results showed in magnetic water treatment, the leaf gas exchange parameters such as assimilation (A), stomatal conductance (gs), transpiration rate (E), and vapor pressure deficit (VPD) were increased. Similarly, Photosynthetic pigments (Chl a, Chl b, Chl (a+b), Carotenoids), photosynthetic water use efficiency (WUE) were also increased significantly. Magnetized water had also significant effects on the maximal efficiency of PSII photochemistry (Fv/Fm). Our study suggested that magnetic water treatment could be used as an environment-friendly technology for improving the growth and physiology of Moringa species. In addition, this technology could be further incorporated into the traditional methods of agriculture for the improvement of crop plants, particularly in the arid and sub-arid areas of the world. (author)

  17. Analysis and Prediction on Vehicle Ownership Based on an Improved Stochastic Gompertz Diffusion Process

    Directory of Open Access Journals (Sweden)

    Huapu Lu

    2017-01-01

    Full Text Available This paper aims at introducing a new improved stochastic differential equation related to Gompertz curve for the projection of vehicle ownership growth. This diffusion model explains the relationship between vehicle ownership and GDP per capita, which has been studied as a Gompertz-like function before. The main innovations of the process lie in two parts: by modifying the deterministic part of the original Gompertz equation, the model can present the remaining slow increase when the S-shaped curve has reached its saturation level; by introducing the stochastic differential equation, the model can better fit the real data when there are fluctuations. Such comparisons are carried out based on data from US, UK, Japan, and Korea with a time span of 1960–2008. It turns out that the new process behaves better in fitting curves and predicting short term growth. Finally, a prediction of Chinese vehicle ownership up to 2025 is presented with the new model, as China is on the initial stage of motorization with much fluctuations in growth.

  18. Transverse charge and magnetization densities: Improved chiral predictions down to b=1 fms

    Energy Technology Data Exchange (ETDEWEB)

    Alarcon, Jose Manuel [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Hiller Blin, Astrid N. [Johannes Gutenberg Univ., Mainz (Germany); Vicente Vacas, Manuel J. [Spanish National Research Council (CSIC), Valencia (Spain). Univ. of Valencia (UV), Inst. de Fisica Corpuscular; Weiss, Christian [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

    2018-03-01

    The transverse charge and magnetization densities provide insight into the nucleon’s inner structure. In the periphery, the isovector components are clearly dominant, and can be computed in a model-independent way by means of a combination of chiral effective field theory (cEFT) and dispersion analysis. With a novel N=D method, we incorporate the pion electromagnetic formfactor data into the cEFT calculation, thus taking into account the pion-rescattering effects and r-meson pole. As a consequence, we are able to reliably compute the densities down to distances b1 fm, therefore achieving a dramatic improvement of the results compared to traditional cEFT calculations, while remaining predictive and having controlled uncertainties.

  19. Genomic prediction using subsampling.

    Science.gov (United States)

    Xavier, Alencar; Xu, Shizhong; Muir, William; Rainey, Katy Martin

    2017-03-24

    Genome-wide assisted selection is a critical tool for the genetic improvement of plants and animals. Whole-genome regression models in Bayesian framework represent the main family of prediction methods. Fitting such models with a large number of observations involves a prohibitive computational burden. We propose the use of subsampling bootstrap Markov chain in genomic prediction. Such method consists of fitting whole-genome regression models by subsampling observations in each round of a Markov Chain Monte Carlo. We evaluated the effect of subsampling bootstrap on prediction and computational parameters. Across datasets, we observed an optimal subsampling proportion of observations around 50% with replacement, and around 33% without replacement. Subsampling provided a substantial decrease in computation time, reducing the time to fit the model by half. On average, losses on predictive properties imposed by subsampling were negligible, usually below 1%. For each dataset, an optimal subsampling point that improves prediction properties was observed, but the improvements were also negligible. Combining subsampling with Gibbs sampling is an interesting ensemble algorithm. The investigation indicates that the subsampling bootstrap Markov chain algorithm substantially reduces computational burden associated with model fitting, and it may slightly enhance prediction properties.

  20. Missing Value Imputation Improves Mortality Risk Prediction Following Cardiac Surgery: An Investigation of an Australian Patient Cohort.

    Science.gov (United States)

    Karim, Md Nazmul; Reid, Christopher M; Tran, Lavinia; Cochrane, Andrew; Billah, Baki

    2017-03-01

    The aim of this study was to evaluate the impact of missing values on the prediction performance of the model predicting 30-day mortality following cardiac surgery as an example. Information from 83,309 eligible patients, who underwent cardiac surgery, recorded in the Australia and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) database registry between 2001 and 2014, was used. An existing 30-day mortality risk prediction model developed from ANZSCTS database was re-estimated using the complete cases (CC) analysis and using multiple imputation (MI) analysis. Agreement between the risks generated by the CC and MI analysis approaches was assessed by the Bland-Altman method. Performances of the two models were compared. One or more missing predictor variables were present in 15.8% of the patients in the dataset. The Bland-Altman plot demonstrated significant disagreement between the risk scores (prisk of mortality. Compared to CC analysis, MI analysis resulted in an average of 8.5% decrease in standard error, a measure of uncertainty. The MI model provided better prediction of mortality risk (observed: 2.69%; MI: 2.63% versus CC: 2.37%, Pvalues improved the 30-day mortality risk prediction following cardiac surgery. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

  1. Method 446.0: In Vitro Determination of Chlorophylls a, b, c + c and Pheopigments in 1 2Marine And Freshwater Algae by Visible Spectrophotometry

    Science.gov (United States)

    This method provides a procedure for determination of chlorophylls a (chl a), b (chl b), c + c 1 2 (chl c + c ) and pheopigments of chlorophyll a (pheo a) 1 2 found in marine and freshwater phytoplankton. Chlorophyllide a is determined as chl a. Visible wavelength spectrophotomet...

  2. Common and rare variants in the exons and regulatory regions of osteoporosis-related genes improve osteoporotic fracture risk prediction.

    Science.gov (United States)

    Lee, Seung Hun; Kang, Moo Il; Ahn, Seong Hee; Lim, Kyeong-Hye; Lee, Gun Eui; Shin, Eun-Soon; Lee, Jong-Eun; Kim, Beom-Jun; Cho, Eun-Hee; Kim, Sang-Wook; Kim, Tae-Ho; Kim, Hyun-Ju; Yoon, Kun-Ho; Lee, Won Chul; Kim, Ghi Su; Koh, Jung-Min; Kim, Shin-Yoon

    2014-11-01

    Osteoporotic fracture risk is highly heritable, but genome-wide association studies have explained only a small proportion of the heritability to date. Genetic data may improve prediction of fracture risk in osteopenic subjects and assist early intervention and management. To detect common and rare variants in coding and regulatory regions related to osteoporosis-related traits, and to investigate whether genetic profiling improves the prediction of fracture risk. This cross-sectional study was conducted in three clinical units in Korea. Postmenopausal women with extreme phenotypes (n = 982) were used for the discovery set, and 3895 participants were used for the replication set. We performed targeted resequencing of 198 genes. Genetic risk scores from common variants (GRS-C) and from common and rare variants (GRS-T) were calculated. Nineteen common variants in 17 genes (of the discovered 34 functional variants in 26 genes) and 31 rare variants in five genes (of the discovered 87 functional variants in 15 genes) were associated with one or more osteoporosis-related traits. Accuracy of fracture risk classification was improved in the osteopenic patients by adding GRS-C to fracture risk assessment models (6.8%; P risk in an osteopenic individual.

  3. Chlorophyll biosynthesis and assembly into chlorophyll-protein complexes in isolated developing chloroplasts

    International Nuclear Information System (INIS)

    Bhaya, D.; Castelfranco, P.A.

    1985-01-01

    Isolated developing plastids from greening cucumber cotyledons or from photoperiodically grown pea seedlings incorporated 14 C-labeled 5-aminolevulinic acid (ALA) into chlorophyll (Chl). Incorporation was light dependent, enhanced by S-adenosylmethionine, and linear for 1 hr. The in vitro rate of Chl synthesis from ALA was comparable to the in vivo rate of Chl accumulation. Levulinic acid and dioxoheptanoic acid strongly inhibited Chl synthesis but not plastid protein synthesis. Neither chloramphenicol nor spectinomycin affected Chl synthesis, although protein synthesis was strongly inhibited. Components of thylakoid membranes from plastids incubated with [ 14 C]ALA were resolved by electrophoresis and then subjected to autoradiography. This work showed that (i) newly synthesized Chl was assembled into Chl-protein complexes and (ii) the inhibition of protein synthesis during the incubation did not alter the labeling pattern. Thus, there was no observable short-term coregulation between Chl synthesis (from ALA) and the synthesis of membrane proteins in isolated plastids

  4. Using self-similarity compensation for improving inter-layer prediction in scalable 3D holoscopic video coding

    Science.gov (United States)

    Conti, Caroline; Nunes, Paulo; Ducla Soares, Luís.

    2013-09-01

    Holoscopic imaging, also known as integral imaging, has been recently attracting the attention of the research community, as a promising glassless 3D technology due to its ability to create a more realistic depth illusion than the current stereoscopic or multiview solutions. However, in order to gradually introduce this technology into the consumer market and to efficiently deliver 3D holoscopic content to end-users, backward compatibility with legacy displays is essential. Consequently, to enable 3D holoscopic content to be delivered and presented on legacy displays, a display scalable 3D holoscopic coding approach is required. Hence, this paper presents a display scalable architecture for 3D holoscopic video coding with a three-layer approach, where each layer represents a different level of display scalability: Layer 0 - a single 2D view; Layer 1 - 3D stereo or multiview; and Layer 2 - the full 3D holoscopic content. In this context, a prediction method is proposed, which combines inter-layer prediction, aiming to exploit the existing redundancy between the multiview and the 3D holoscopic layers, with self-similarity compensated prediction (previously proposed by the authors for non-scalable 3D holoscopic video coding), aiming to exploit the spatial redundancy inherent to the 3D holoscopic enhancement layer. Experimental results show that the proposed combined prediction can improve significantly the rate-distortion performance of scalable 3D holoscopic video coding with respect to the authors' previously proposed solutions, where only inter-layer or only self-similarity prediction is used.

  5. A robust model predictive control strategy for improving the control performance of air-conditioning systems

    International Nuclear Information System (INIS)

    Huang Gongsheng; Wang Shengwei; Xu Xinhua

    2009-01-01

    This paper presents a robust model predictive control strategy for improving the supply air temperature control of air-handling units by dealing with the associated uncertainties and constraints directly. This strategy uses a first-order plus time-delay model with uncertain time-delay and system gain to describe air-conditioning process of an air-handling unit usually operating at various weather conditions. The uncertainties of the time-delay and system gain, which imply the nonlinearities and the variable dynamic characteristics, are formulated using an uncertainty polytope. Based on this uncertainty formulation, an offline LMI-based robust model predictive control algorithm is employed to design a robust controller for air-handling units which can guarantee a good robustness subject to uncertainties and constraints. The proposed robust strategy is evaluated in a dynamic simulation environment of a variable air volume air-conditioning system in various operation conditions by comparing with a conventional PI control strategy. The robustness analysis of both strategies under different weather conditions is also presented.

  6. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  7. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

  8. Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

    Science.gov (United States)

    Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

    2017-12-01

    Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent

  9. Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

    KAUST Repository

    Dreano, Denis

    2017-05-24

    Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.

  10. Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

    KAUST Repository

    Dreano, Denis; Tsiaras, Kostas; Triantafyllou, George; Hoteit, Ibrahim

    2017-01-01

    Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.

  11. Improvement of injury severity prediction (ISP) of AACN during on-site triage using vehicle deformation pattern for car-to-car (C2C) side impacts.

    Science.gov (United States)

    Pal, Chinmoy; Hirayama, Shigeru; Narahari, Sangolla; Jeyabharath, Manoharan; Prakash, Gopinath; Kulothungan, Vimalathithan; Combest, John

    2018-02-28

    The Advanced Automatic Crash Notification (AACN) system needs to predict injury accurately, to provide appropriate treatment for seriously injured occupants involved in motor vehicle crashes. This study investigates the possibility of improving the accuracy of the AACN system, using vehicle deformation parameters in car-to-car (C2C) side impacts. This study was based on car-to-car (C2C) crash data from NASS-CDS, CY 2004-2014. Variables from Kononen's algorithm (published in 2011) were used to build a "base model" for this study. Two additional variables, intrusion magnitude and max deformation location, are added to Kononen's algorithm variables (age, belt usage, number of events, and delta-v) to build a "proposed model." This proposed model operates in two stages: In the first stage, the AACN system uses Kononen's variables and predicts injury severity, based on which emergency medical services (EMS) is dispatched; in the second stage, the EMS team conveys deformation-related information, for accurate prediction of serious injury. Logistic regression analysis reveals that the vehicle deformation location and intrusion magnitude are significant parameters in predicting the level of injury. The percentage of serious injury decreases as the deformation location shifts away from the driver sitting position. The proposed model can improve the sensitivity (serious injury correctly predicted as serious) from 50% to 63%, and overall prediction accuracy increased from 83.5% to 85.9%. The proposed method can improve the accuracy of injury prediction in side-impact collisions. Similar opportunities exist for other crash modes also.

  12. Long Range Aircraft Trajectory Prediction

    OpenAIRE

    Magister, Tone

    2009-01-01

    The subject of the paper is the improvement of the aircraft future trajectory prediction accuracy for long-range airborne separation assurance. The strategic planning of safe aircraft flights and effective conflict avoidance tactics demand timely and accurate conflict detection based upon future four–dimensional airborne traffic situation prediction which is as accurate as each aircraft flight trajectory prediction. The improved kinematics model of aircraft relative flight considering flight ...

  13. MEMS based shock pulse detection sensor for improved rotary Stirling cooler end of life prediction

    Science.gov (United States)

    Hübner, M.; Münzberg, M.

    2018-05-01

    The widespread use of rotary Stirling coolers in high performance thermal imagers used for critical 24/7 surveillance tasks justifies any effort to significantly enhance the reliability and predictable uptime of those coolers. Typically the lifetime of the whole imaging device is limited due to continuous wear and finally failure of the rotary compressor of the Stirling cooler, especially due to failure of the comprised bearings. MTTF based lifetime predictions, even based on refined MTTF models taking operational scenario dependent scaling factors into account, still lack in precision to forecast accurately the end of life (EOL) of individual coolers. Consequently preventive maintenance of individual coolers to avoid failures of the main sensor in critical operational scenarios are very costly or even useless. We have developed an integrated test method based on `Micro Electromechanical Systems', so called MEMS sensors, which significantly improves the cooler EOL prediction. The recently commercially available MEMS acceleration sensors have mechanical resonance frequencies up to 50 kHz. They are able to detect solid borne shock pulses in the cooler structure, originating from e.g. metal on metal impacts driven by periodical forces acting on moving inner parts of the rotary compressor within wear dependent slack and play. The impact driven transient shock pulse analyses uses only the high frequency signal <10kHz and differs therefore from the commonly used broadband low frequencies vibrational analysis of reciprocating machines. It offers a direct indicator of the individual state of wear. The predictive cooler lifetime model based on the shock pulse analysis is presented and results are discussed.

  14. Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

    Science.gov (United States)

    Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

    2017-10-12

    This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

  15. Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

    Science.gov (United States)

    Kulinkina, Alexandra V; Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jürg; Naumova, Elena N

    2018-06-04

    Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing

  16. Prediction method of long-term reliability in improving residual stresses by means of surface finishing

    International Nuclear Information System (INIS)

    Sera, Takehiko; Hirano, Shinro; Chigusa, Naoki; Okano, Shigetaka; Saida, Kazuyoshi; Mochizuki, Masahito; Nishimoto, Kazutoshi

    2012-01-01

    Surface finishing methods, such as Water Jet Peening (WJP), have been applied to welds in some major components of nuclear power plants as a counter measure to Primary Water Stress Corrosion Cracking (PWSCC). In addition, the methods of surface finishing (buffing treatment) is being standardized, and thus the buffing treatment has been also recognized as the well-established method of improving stress. On the other hand, the long-term stability of peening techniques has been confirmed by accelerated test. However, the effectiveness of stress improvement by surface treatment is limited to thin layers and the effect of complicated residual stress distribution in the weld metal beneath the surface is not strictly taken into account for long-term stability. This paper, therefore, describes the accelerated tests, which confirmed that the long-term stability of the layer subjected to buffing treatment was equal to that subjected to WJP. The long-term reliability of very thin stress improved layer was also confirmed through a trial evaluation by thermal elastic-plastic creep analysis, even if the effect of complicated residual stress distribution in the weld metal was excessively taken into account. Considering the above findings, an approach is proposed for constructing the prediction method of the long-term reliability of stress improvement by surface finishing. (author)

  17. Density-dependent microbial turnover improves soil carbon model predictions of long-term litter manipulations

    Science.gov (United States)

    Georgiou, Katerina; Abramoff, Rose; Harte, John; Riley, William; Torn, Margaret

    2017-04-01

    Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity via abiotic effects on soil or mediated by changes in plant inputs. Recently, many promising microbial models of soil organic carbon (SOC) decomposition have been proposed to advance understanding and prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the sources of instability in four models that span the range of complexity of these recent microbial models, by sequentially adding complexity to a simple model to include microbial physiology, a mineral sorption isotherm, and enzyme dynamics. We propose a formulation that introduces density-dependence of microbial turnover, which acts to limit population sizes and reduce oscillations. We compare these models to results from 24 long-term C-input field manipulations, including the Detritus Input and Removal Treatment (DIRT) experiments, to show that there are clear metrics that can be used to distinguish and validate the inherent dynamics of each model structure. We find that widely used first-order models and microbial models without density-dependence cannot readily capture the range of long-term responses observed across the DIRT experiments as a direct consequence of their model structures. The proposed formulation improves predictions of long-term C-input changes, and implies greater SOC storage associated with CO2-fertilization-driven increases in C inputs over the coming century compared to common microbial models. Finally, we discuss our findings in the context of improving microbial model behavior for inclusion in Earth System Models.

  18. Molecular orbital study of the primary electron donor P700 of photosystem I based on a recent X-ray single crystal structure analysis

    International Nuclear Information System (INIS)

    Plato, Martin; Krauss, Norbert; Fromme, Petra; Lubitz, Wolfgang

    2003-01-01

    The X-ray structure analysis of photosystem (PS) I single crystals showed that the primary electron donor P700 is a heterodimer formed by one chlorophyll (Chl) a and one Chl a ' [Nature 411 (2001) 909]. The electronic structure of the cation radical P700 +· of the primary donor, which is created in the charge separation process, has been probed by semiempirical molecular orbital calculations including spin polarization effects (RHF-INDO/SP). The calculations, which were based on the X-ray structure, clearly show that P700 is a supermolecule formed by two chlorophyll species. They furthermore predict an asymmetrical charge and spin density distribution in favor of the monomeric Chl a half of this dimer in accordance with results from earlier EPR and ENDOR studies [J. Phys. Chem. B 105 (2000) 1225]. The stepwise inclusion of various electrostatic interactions of the dimer with its nearest surrounding (one threonine forming a hydrogen bond to the keto group of Chl a ' and two histidines liganding the Mg atoms of the two chlorophylls) leads to a systematic enhancement of this electronic asymmetry yielding a spin density ratio of almost 5:1 as also found experimentally. A large part of this value is caused by spin polarization effects. This result is only weakly affected by the electrostatic field of more remote amino acid residues and other pigment molecules ('accessory' Chl a molecules) present in PS I. A separate group of calculations involving local geometry optimizations by energy minimization techniques yields a further enhancement of the spin density asymmetry. A particularly strong effect is obtained by allowing for variations of the geometry of the vinyl groups on both chlorophylls of the P700 dimer. Theoretical results for individual isotropic proton and nitrogen hyperfine coupling constants, showing a satisfactory agreement with experimental findings, are also presented

  19. Using isotopes to improve impact and hydrological predictions of land-surface schemes in global climate models

    International Nuclear Information System (INIS)

    McGuffie, K.; Henderson-Sellers, A.

    2002-01-01

    Global climate model (GCM) predictions of the impact of large-scale land-use change date back to 1984 as do the earliest isotopic studies of large-basin hydrology. Despite this coincidence in interest and geography, with both papers focussed on the Amazon, there have been few studies that have tried to exploit isotopic information with the goal of improving climate model simulations of the land-surface. In this paper we analyze isotopic results from the IAEA global data base specifically with the goal of identifying signatures of potential value for improving global and regional climate model simulations of the land-surface. Evaluation of climate model predictions of the impacts of deforestation of the Amazon has been shown to be of significance by recent results which indicate impacts occurring distant from the Amazon i.e. tele-connections causing climate change elsewhere around the globe. It is suggested that these could be similar in magnitude and extent to the global impacts of ENSO events. Validation of GCM predictions associated with Amazonian deforestation are increasingly urgently required because of the additional effects of other aspects of climate change, particularly synergies occurring between forest removal and greenhouse gas increases, especially CO 2 . Here we examine three decades distributions of deuterium excess across the Amazon and use the results to evaluate the relative importance of the fractionating (partial evaporation) and non-fractionating (transpiration) processes. These results illuminate GCM scenarios of importance to the regional climate and hydrology: (i) the possible impact of increased stomatal resistance in the rainforest caused by higher levels of atmospheric CO2 [4]; and (ii) the consequences of the combined effects of deforestation and global warming on the regions climate and hydrology

  20. Evaluating and predicting the effectiveness of farmland consolidation on improving agricultural productivity in China.

    Science.gov (United States)

    Fan, Yeting; Jin, Xiaobin; Xiang, Xiaomin; Gan, Le; Yang, Xuhong; Zhang, Zhihong; Zhou, Yinkang

    2018-01-01

    Food security has always been a focus issue in China. Farmland consolidation (FC) was regarded as a critical way to increase the quantity and improve the quality of farmland to ensure food security by Chinese government. FC projects have been nationwide launched, however few studies focused on evaluating the effectiveness of FC at a national scale. As such, an efficient way to evaluate the effectiveness of FC on improving agricultural productivity in China will be needed and it is critical for future national land consolidation planning. In this study, we selected 7505 FC projects completed between 2006 and 2013 with good quality Normalized Difference Vegetation Index (NDVI) as samples to evaluate the effectiveness of FC. We used time-series Moderate Resolution Imaging Spectroradiometer NDVI from 2001 to 2013, to extract four indicators to characterize agricultural productivity change of 4442 FC projects completed between 2006 and 2010, i.e., productivity level (PL), productivity variation (PV), productivity potential (PP), and multi-cropping index (MI). On this basis, we further predicted the same four characteristics for 3063 FC projects completed between 2011 and 2013, respectively, using Support Vector Machines (SVM). We found FC showed an overall effective status on improving agricultural productivity between 2006 and 2013 in China, especially on upgrading PL and improving PP. The positive effect was more prominent in the southeast and eastern China. It is noteworthy that 27.30% of all the 7505 projects were still ineffective on upgrading PL, the elementary improvement of agricultural productivity. Finally, we proposed that location-specific factors should be taken into consideration for launching FC projects and diverse financial sources are also needed for supporting FC. The results provide a reference for government to arrange FC projects reasonably and to formulate land consolidation planning in a proper way that better improve the effectiveness of FC.

  1. Evaluating and predicting the effectiveness of farmland consolidation on improving agricultural productivity in China

    Science.gov (United States)

    Xiang, Xiaomin; Gan, Le; Yang, Xuhong; Zhang, Zhihong; Zhou, Yinkang

    2018-01-01

    Food security has always been a focus issue in China. Farmland consolidation (FC) was regarded as a critical way to increase the quantity and improve the quality of farmland to ensure food security by Chinese government. FC projects have been nationwide launched, however few studies focused on evaluating the effectiveness of FC at a national scale. As such, an efficient way to evaluate the effectiveness of FC on improving agricultural productivity in China will be needed and it is critical for future national land consolidation planning. In this study, we selected 7505 FC projects completed between 2006 and 2013 with good quality Normalized Difference Vegetation Index (NDVI) as samples to evaluate the effectiveness of FC. We used time-series Moderate Resolution Imaging Spectroradiometer NDVI from 2001 to 2013, to extract four indicators to characterize agricultural productivity change of 4442 FC projects completed between 2006 and 2010, i.e., productivity level (PL), productivity variation (PV), productivity potential (PP), and multi-cropping index (MI). On this basis, we further predicted the same four characteristics for 3063 FC projects completed between 2011 and 2013, respectively, using Support Vector Machines (SVM). We found FC showed an overall effective status on improving agricultural productivity between 2006 and 2013 in China, especially on upgrading PL and improving PP. The positive effect was more prominent in the southeast and eastern China. It is noteworthy that 27.30% of all the 7505 projects were still ineffective on upgrading PL, the elementary improvement of agricultural productivity. Finally, we proposed that location-specific factors should be taken into consideration for launching FC projects and diverse financial sources are also needed for supporting FC. The results provide a reference for government to arrange FC projects reasonably and to formulate land consolidation planning in a proper way that better improve the effectiveness of FC

  2. Plaque Structural Stress Estimations Improve Prediction of Future Major Adverse Cardiovascular Events After Intracoronary Imaging.

    Science.gov (United States)

    Brown, Adam J; Teng, Zhongzhao; Calvert, Patrick A; Rajani, Nikil K; Hennessy, Orla; Nerlekar, Nitesh; Obaid, Daniel R; Costopoulos, Charis; Huang, Yuan; Hoole, Stephen P; Goddard, Martin; West, Nick E J; Gillard, Jonathan H; Bennett, Martin R

    2016-06-01

    Although plaque rupture is responsible for most myocardial infarctions, few high-risk plaques identified by intracoronary imaging actually result in future major adverse cardiovascular events (MACE). Nonimaging markers of individual plaque behavior are therefore required. Rupture occurs when plaque structural stress (PSS) exceeds material strength. We therefore assessed whether PSS could predict future MACE in high-risk nonculprit lesions identified on virtual-histology intravascular ultrasound. Baseline nonculprit lesion features associated with MACE during long-term follow-up (median: 1115 days) were determined in 170 patients undergoing 3-vessel virtual-histology intravascular ultrasound. MACE was associated with plaque burden ≥70% (hazard ratio: 8.6; 95% confidence interval, 2.5-30.6; P<0.001) and minimal luminal area ≤4 mm(2) (hazard ratio: 6.6; 95% confidence interval, 2.1-20.1; P=0.036), although absolute event rates for high-risk lesions remained <10%. PSS derived from virtual-histology intravascular ultrasound was subsequently estimated in nonculprit lesions responsible for MACE (n=22) versus matched control lesions (n=22). PSS showed marked heterogeneity across and between similar lesions but was significantly increased in MACE lesions at high-risk regions, including plaque burden ≥70% (13.9±11.5 versus 10.2±4.7; P<0.001) and thin-cap fibroatheroma (14.0±8.9 versus 11.6±4.5; P=0.02). Furthermore, PSS improved the ability of virtual-histology intravascular ultrasound to predict MACE in plaques with plaque burden ≥70% (adjusted log-rank, P=0.003) and minimal luminal area ≤4 mm(2) (P=0.002). Plaques responsible for MACE had larger superficial calcium inclusions, which acted to increase PSS (P<0.05). Baseline PSS is increased in plaques responsible for MACE and improves the ability of intracoronary imaging to predict events. Biomechanical modeling may complement plaque imaging for risk stratification of coronary nonculprit lesions. © 2016

  3. Day 100 Peripheral Blood Absolute Lymphocyte/Monocyte Ratio and Survival in Classical Hodgkin's Lymphoma Postautologous Peripheral Blood Hematopoietic Stem Cell Transplantation

    Directory of Open Access Journals (Sweden)

    Luis F. Porrata

    2013-01-01

    Full Text Available Day 100 prognostic factors of postautologous peripheral blood hematopoietic stem cell transplantation (APBHSCT to predict clinical outcome in classical Hodgkin lymphoma (cHL patients have not been evaluated. Thus, we studied if the day 100 peripheral blood absolute lymphocyte/monocyte ratio (Day 100 ALC/AMC affects clinical outcomes by landmark analysis from day 100 post-APBHSCT. Only cHL patients achieving a complete remission at day 100 post-APBHSCT were studied. From 2000 to 2010, 131 cHL consecutive patients qualified for the study. The median followup from day 100 was 4.1 years (range: 0.2–12.3 years. Patients with a Day 100 ALC/AMC ≥ 1.3 experienced superior overall survival (OS and progression-free survival (PFS compared with Day 100 ALC/AMC < 1.3 (from day 100: OS, median not reached versus 2.8 years; 5 years OS rates of 93% (95% CI, 83%–97% versus 35% (95% CI, 19%–51%, resp., P<0.0001; from day 100: PFS, median not reached versus 1.2 years; 5 years PFS rates of 79% (95% CI, 69%–86% versus 27% (95% CI, 14%–45%, resp., P<0.0001. Day ALC/AMC ratio was an independent predictor for OS and PFS. Thus, Day 100 ALC/AMC ratio is a simple biomarker that can help to assess clinical outcomes from day 100 post-APBHSCT in cHL patients.

  4. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

    International Nuclear Information System (INIS)

    Shen Yang; Bax, Ad

    2010-01-01

    NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13 C β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and 13 C β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ 15 N, δ 13 C', δ 13 C α , δ 13 C β , δ 1 H α and δ 1 H N , respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

  5. Shellfish Aquaculture from Space: Potential of Sentinel2 to Monitor Tide-Driven Changes in Turbidity, Chlorophyll Concentration and Oyster Physiological Response at the Scale of an Oyster Farm

    Directory of Open Access Journals (Sweden)

    Pierre Gernez

    2017-05-01

    Full Text Available The algorithms of Novoa et al. (2017 and Gons et al. (2005 were recalibrated and applied to Sentinel2 data to retrieve suspended particulate matter (SPM and chlorophyll a (chl a concentration in the environmentally and economically important intertidal zones. Sentinel2-derived chl a and SPM concentration distributions were analyzed at the scale of an oyster farm over a variety of tidal conditions. Sentinel2 imagery was then coupled with ecophysiological modeling to analyze the influence of tide-driven chl a and SPM dynamics on oyster clearance and chl consumption rates. Within the studied oyster farming site (Bourgneuf Bay along the French Atlantic coast, chl consumption rate mirrored the changes in chl a concentration during neap tides, whereas oyster clearance and chl consumption rates were both negatively impacted by high SPM concentration during spring tides.

  6. The C21-formyl group in chlorophyll f originates from molecular oxygen.

    Science.gov (United States)

    Garg, Harsh; Loughlin, Patrick C; Willows, Robert D; Chen, Min

    2017-11-24

    Chlorophylls (Chls) are the most important cofactors for capturing solar energy to drive photosynthetic reactions. Five spectral types of Chls have been identified to date, with Chl f having the most red-shifted absorption maximum because of a C2 1 -formyl group substitution of Chl f However, the biochemical provenance of this formyl group is unknown. Here, we used a stable isotope labeling technique ( 18 O and 2 H) to determine the origin of the C2 1 -formyl group of Chl f and to verify whether Chl f is synthesized from Chl a in the cyanobacterial species Halomicronema hongdechloris. In the presence of either H 2 18 O or 18 O 2 , the origin of oxygen atoms in the newly synthesized chlorophylls was investigated. The pigments were isolated with HPLC, followed by MS analysis. We found that the oxygen atom of the C2 1 -formyl group originates from molecular oxygen and not from H 2 O. Moreover, we examined the kinetics of the labeling of Chl a and Chl f from H. hongdechloris grown in 50% D 2 O-seawater medium under different light conditions. When cells were shifted from white light D 2 O-seawater medium to far-red light H 2 O-seawater medium, the observed deuteration in Chl f indicated that Chl(ide) a is the precursor of Chl f Taken together, our results advance our understanding of the biosynthesis pathway of the chlorophylls and the formation of the formyl group in Chl f . © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  7. The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients.

    Science.gov (United States)

    Terluin, Berend; Eekhout, Iris; Terwee, Caroline B

    2017-03-01

    Patients have their individual minimal important changes (iMICs) as their personal benchmarks to determine whether a perceived health-related quality of life (HRQOL) change constitutes a (minimally) important change for them. We denote the mean iMIC in a group of patients as the "genuine MIC" (gMIC). The aims of this paper are (1) to examine the relationship between the gMIC and the anchor-based minimal important change (MIC), determined by receiver operating characteristic analysis or by predictive modeling; (2) to examine the impact of the proportion of improved patients on these MICs; and (3) to explore the possibility to adjust the MIC for the influence of the proportion of improved patients. Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of HRQOL (change) scores and distributions of iMICs. In addition, a real data set is analyzed for illustration. The receiver operating characteristic-based and predictive modeling MICs equal the gMIC when the proportion of improved patients equals 0.5. The MIC is estimated higher than the gMIC when the proportion improved is greater than 0.5, and the MIC is estimated lower than the gMIC when the proportion improved is less than 0.5. Using an equation including the predictive modeling MIC, the log-odds of improvement, the standard deviation of the HRQOL change score, and the correlation between the HRQOL change score and the anchor results in an adjusted MIC reflecting the gMIC irrespective of the proportion of improved patients. Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC. We assumed normal distributions and global perceived change scores that were independent on the follow-up score. Additionally, floor and ceiling effects were not taken into account. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. MODELING AND ANALYSIS OF ALGAL BLOOMS IN ARAS DAM BY ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JAHANGIRI-RAD MAHSA

    2015-03-01

    Full Text Available Man made practices have contributed to large-scale algal blooms that have caused serious ecological, aesthetic, water purification and water distribution problems. Aras Dam, which provides Arasful city with drinking water, has chronic algal blooms since 1990. This study addresses the use of artificial neural network (ANN model to anticipate the chlorophyll-a concentration in water of dam reservoir. Operation tests carried out by collecting water samples from 5 stations and examined for physical quality parameters namely: water temperature, total suspended solids (TSS, biochemical oxygen demands (BOD, ortophosphate, total phosphorous and nitrate concentrations using standard methods. Chlorophyll-a was also checked separately in order to investigate the accuracy of the predicted results by ANN. The results showed that a network was highly accurate in predicting the Chl-a concentration. A good agreement between actual data and the ANN outputs for training was observed, indicating the validation of testing data sets. The initial results of the research indicate that the dam is enriched with nutrients (phosphorus and nitrogen. The Chl-a concentration that were predicted by the model were beyond the standard levels; indicating the possibility of eutrophication especially during fall season.

  9. Land-based nutrient enrichment of the Buccoo Reef Complex and fringing coral reefs of Tobago, West Indies

    International Nuclear Information System (INIS)

    Lapointe, Brian E.; Langton, Richard; Bedford, Bradley J.; Potts, Arthur C.; Day, Owen; Hu, Chuanmin

    2010-01-01

    Tobago's fringing coral reefs (FR) and Buccoo Reef Complex (BRC) can be affected locally by wastewater and stormwater, and regionally by the Orinoco River. In 2001, seasonal effects of these inputs on water-column nutrients and phytoplankton (Chl a), macroalgal C:N:P and δ 15 N values, and biocover at FR and BRC sites were examined. Dissolved inorganic nitrogen (DIN, particularly ammonium) increased and soluble reactive phosphorus (SRP) decreased from the dry to wet season. Wet season satellite and Chl a data showed that Orinoco runoff reaching Tobago contained chromophoric dissolved organic matter (CDOM) but little Chl a, suggesting minimal riverine nutrient transport to Tobago. C:N ratios were lower (16 vs. 21) and macroalgal δ 15 N values higher (6.6 per mille vs. 5.5 per mille ) in the BRC vs. FR, indicating relatively more wastewater N in the BRC. High macroalgae and low coral cover in the BRC further indicated that better wastewater treatment could improve the health of Tobago's coral reefs.

  10. Improved Prediction of Phosphorus Dynamics in Biotechnological Processes by Considering Precipitation and Polyphosphate Formation: A Case Study on Antibiotic Production with Streptomyces coelicolor

    DEFF Research Database (Denmark)

    Bürger, Patrick; Flores-Alsina, Xavier; Arellano-Garcia, Harvey

    2018-01-01

    The multiplicity of physicochemical and biological processes, where phosphorus is involved, makes their accurate prediction using current mathematical models in biotechnology quite a challenge. In this work, an antibiotic production model of Streptomyces coelicolor is chosen as a representative...... approach describing intracellular polyphosphate accumulation and consumption has been developed and implemented. A heuristic re-estimation of selected parameters is carried out to improve overall model performance. The improved process model predicts phosphate dynamics (root mean squared error ≤52h: −90...

  11. Beyond clay: Towards an improved set of variables for predicting soil organic matter content

    Science.gov (United States)

    Rasmussen, Craig; Heckman, Katherine; Wieder, William R.; Keiluweit, Marco; Lawrence, Corey R.; Berhe, Asmeret Asefaw; Blankinship, Joseph C.; Crow, Susan E.; Druhan, Jennifer; Hicks Pries, Caitlin E.; Marin-Spiotta, Erika; Plante, Alain F.; Schadel, Christina; Schmiel, Joshua P.; Sierra, Carlos A.; Thompson, Aaron; Wagai, Rota

    2018-01-01

    Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron- and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to better reflect the role of soil physicochemical properties in SOM cycling.

  12. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  13. A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters

    International Nuclear Information System (INIS)

    Gitelson, Anatoly A; Gurlin, Daniela; Moses, Wesley J; Barrow, Tadd

    2009-01-01

    The objective of this work was to test the performance of a recently developed three-band model and its special case, a two-band model, for the remote estimation of the chlorophyll- a (chl-a) concentration in turbid productive case 2 waters. We specifically focused on (a) determining the ability of the models to estimate chl- a -3 , typical for coastal and estuarine waters, and (b) assessing the potential of MODIS and MERIS to estimate chl-a concentrations in turbid productive waters, using red and near-infrared (NIR) bands. Reflectance spectra and water samples were collected in 89 stations over lakes in the United States with a wide variability in optical parameters (i.e. 2.1 -3 ; 0.5 -1 ). The three-band model, using wavebands around 670, 710 and 750 nm, explains more than 89% of the chl- a variation for chl- a ranging from 2 to 20 mg m -3 and can be used to estimate chlorophyll-a concentrations with a root mean square error (RMSE) of -3 . MODIS (bands 13 and 15) and MERIS (bands 7, 9, and 10) red and NIR reflectances were simulated from the collected reflectance spectra and potential estimation errors were assessed. The MODIS two-band model is able to estimate chl- a concentrations with a RMSE of -3 for chl-a ranging from 2 to 50 mg m -3 ; however, the model loses its sensitivity for chl- a -3 . Benefiting from the higher spectral resolution of the MERIS data, the MERIS three-band model accounts for 93% of chl- a variation and is able to estimate chl-a concentrations with a RMSE of -3 for chl-a ranging from 2 to 50 mg m -3 , and a RMSE of -3 for chl-a ranging from 2 to 20 mg m -3 . These findings imply that, provided that an atmospheric correction scheme specific to the red and NIR spectral region is available, the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl- a in case 2 waters.

  14. Improved Storm Monitoring and Prediction for the San Francisco Bay Area

    Science.gov (United States)

    Cifelli, R.; Chandrasekar, V.; Anderson, M.; Davis, G.

    2017-12-01

    The Advanced Quantitative Precipitation Information (AQPI) System is a multi-faceted project to improve precipitation and hydrologic monitoring, prediction, and decision support for the San Francisco Bay Area. The Bay Area faces a multitude of threats from extreme events, including disrupted transportation from flooded roads and railroad lines, water management challenges related to storm water, river and reservoir management and storm-related damage demanding emergency response. The threats occur on spatial scales ranging from local communities to the entire region and time scales ranging from hours to days. These challenges will be exacerbated by future sea level rise, more extreme weather events and increased vulnerabilities. AQPI is a collaboration of federal, state and local governments with assistance from the research community. Led by NOAA's Earth System Research Laboratory, in partnership with the Cooperative Institute for Research in the Atmosphere, USGS, and Scripps, AQPI is a four-year effort funded in part by a grant from the California Department of Water Resource's Integrated Regional Water Management Program. The Sonoma County Water Agency is serving as the local sponsor of the project. Other local participants include the Santa Clara Valley Water District, San Francisco Public Utilities Commission, and the Bay Area Flood Protection Agencies Association. AQPI will provide both improved observing capabilities and a suite of numerical forecast models to produce accurate and timely information for benefit of flood management, emergency response, water quality, ecosystem services, water supply and transportation management for the Bay Area. The resulting information will support decision making to mitigate flood risks, secure water supplies, minimize water quality impacts to the Bay from combined sewer overflows, and have improved lead-time on coastal and Bay inundation from extreme storms like Atmospheric Rivers (ARs). The project is expected to

  15. Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern South Pacific Ocean

    Directory of Open Access Journals (Sweden)

    Y. Huot

    2008-04-01

    Full Text Available The particulate scattering, bp, and backscattering, bbp, coefficients are determined by the concentration and physical properties of suspended particles in the ocean. They provide a simple description of the influence of these particles on the scattering of light within the water column. For the remote observation of ocean color, bbp along with the total absorption coefficient govern the amount and spectral qualities of light leaving the sea surface. However, for the construction and validation of ocean color models measurements of bbp are still lacking, especially at low chlorophyll a concentrations ([Chl]. Here, we examine the relationships between spectral bbp and bp vs. [Chl] along an 8000 km transect crossing the Case 1 waters of the eastern South Pacific Gyre. In these waters, over the entire range of [Chl] encountered (~0.02–2 mg m3, both bbp and bp can be related to [Chl] by power functions (i.e. bp or bbp=α[Chl]β. Regression analyses are carried out to provide the parameters α and β for several wavelengths throughout the visible for both bbp and bp. When applied to the data, these functions retrieve the same fraction of variability in bbp and bp (coefficients of determination between 0.82 and 0.88. The bbp coefficient fall within the bounds of previous measurements at intermediate and high [Chl] recently published. Its dependence on [Chl] below ~0.1 mg m−3 is described for the first time with in situ data. The backscattering ratio (i.e. bbp/bp with values near 0.01 for all stations appears to be spectrally neutral and not significantly dependent on [Chl]. These results should foster the

  16. Spatial-Temporal Variations of Chlorophyll-a in the Adjacent Sea Area of the Yangtze River Estuary Influenced by Yangtze River Discharge

    Science.gov (United States)

    Wang, Ying; Jiang, Hong; Jin, Jiaxin; Zhang, Xiuying; Lu, Xuehe; Wang, Yueqi

    2015-01-01

    Carrying abundant nutrition, terrigenous freshwater has a great impact on the spatial and temporal heterogeneity of phytoplankton in coastal waters. The present study analyzed the spatial-temporal variations of Chlorophyll-a (Chl-a) concentration under the influence of discharge from the Yangtze River, based on remotely sensed Chl-a concentrations. The study area was initially zoned to quantitatively investigate the spatial variation patterns of Chl-a. Then, the temporal variation of Chl-a in each zone was simulated by a sinusoidal curve model. The results showed that in the inshore waters, the terrigenous discharge was the predominant driving force determining the pattern of Chl-a, which brings the risk of red tide disasters; while in the open sea areas, Chl-a was mainly affected by meteorological factors. Furthermore, a diversity of spatial and temporal variations of Chl-a existed based on the degree of influences from discharge. The diluted water extended from inshore to the east of Jeju Island. This process affected the Chl-a concentration flowing through the area, and had a potential impact on the marine environment. The Chl-a from September to November showed an obvious response to the discharge from July to September with a lag of 1 to 2 months. PMID:26006121

  17. HSP90 is essential for Jak-STAT signaling in classical Hodgkin lymphoma cells

    Directory of Open Access Journals (Sweden)

    Kube Dieter

    2009-07-01

    Full Text Available Abstract In classical Hodgkin lymphoma (cHL chemotherapeutic regimens are associated with stagnant rates of secondary malignancies requiring the development of new therapeutic strategies. We and others have shown that permanently activated Signal Transducer and Activator of Transcription (STAT molecules are essential for cHL cells. Recently an overexpression of heat-shock protein 90 (HSP90 in cHL cells has been shown and inhibition of HSP90 seems to affect cHL cell survival. Here we analysed the effects of HSP90 inhibition by geldanamycin derivative 17-AAG or RNA interference (RNAi on aberrant Jak-STAT signaling in cHL cells. Treatment of cHL cell lines with 17-AAG led to reduced cell proliferation and a complete inhibition of STAT1, -3, -5 and -6 tyrosine phosphorylation probably as a result of reduced protein expression of Janus kinases (Jaks. RNAi-mediated inhibition of HSP90 showed similar effects on Jak-STAT signaling in L428 cHL cells. These results suggest a central role of HSP90 in permanently activated Jak-STAT signaling in cHL cells. Therapeutics targeting HSP90 may be a promising strategy in cHL and other cancer entities associated with deregulated Jak-STAT pathway activation.

  18. Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multisensor data

    Science.gov (United States)

    Pitarch, J.; Volpe, G.; Colella, S.; Krasemann, H.; Santoleri, R.

    2015-09-01

    Fifteen-year (1997-2012) time series of chlorophyll a (CHL) in the Baltic Sea, based on merged multisensor satellite data provided by the European projects Globcolour and ESA-OC-CCI were analysed. Several available CHL algorithms were sea-truthed against a large in situ CHL dataset consisting of data by Seadatanet, HELCOM and NOAA. Matchups were calculated for three separate areas (1) Skagerrak and Kattegat, (2) Baltic Proper plus gulfs of Riga and Finland, called here "Central Baltic", (3) Gulf of Bothnia, and for the three areas as a whole. Statistics showed low linearity. The OC4v6 algorithm (R2 = 0.46, BIAS = +60 %, RMS = 79 % for the whole dataset) was linearly transformed by using the best linear fit (OC4corr). By construction, the bias was corrected, but RMS was increased instead. Despite this shortcoming, we demonstrated that errors between OC4corr and in situ data were log-normally distributed and centred at zero. Consequently, unbiased estimators of the horizontally-averaged CHL could be obtained, the error of which tends to zero when a large amount of pixels is averaged. From the basin-wide time series, the climatology and the annual anomalies were separated. The climatologies revealed completely different CHL dynamics among regions: in Skagerrak and Kattegat, CHL strongly peaks in late winter, with a minimum in summer and a secondary peak in spring. In the Central Baltic, CHL follows a dynamics of a spring CHL peak, followed by a much stronger summer bloom, with decreasing CHL towards winter. The Gulf of Bothnia shows a similar CHL dynamics as the central Baltic, although the summer bloom is absent. Across years, CHL showed great variability. Supported by auxiliary satellite sea-surface temperature (SST) data, we found that phytoplankton growth was inhibited in the central Baltic Sea in the years of colder summers or when the SST happened to increase later in the season. Extremely high CHL in spring 2008 was detected and linked to an exceptionally warm

  19. Assessment of chlorophyll variability along the northwestern coast of Iberian Peninsula

    Science.gov (United States)

    Picado, A.; Alvarez, I.; Vaz, N.; Varela, R.; Gomez-Gesteira, M.; Dias, J. M.

    2014-10-01

    The northwestern coast of the Iberian Peninsula is characterized by a high primary production mainly supported by coastal upwelling, creating an extraordinary commercial interest for fisheries and aquaculture. Considering chlorophyll-a (Chl-a) as an indicator of primary production, its spatio-temporal variability was researched in this study in the surface water of this upwelling region from 1998 to 2007. Satellite derived Chl-a, Sea Surface Temperature (SST) and Ekman transport data as well as the inflow of the main rivers discharging into the study area were used to investigate the origin of the Chl-a concentration. Empirical Orthogonal Function (EOF) analysis of weekly Chl-a images was performed, as well as correlation analysis between Chl-a concentration, Ekman transport and river discharge. EOF results suggest that the highest Chl-a concentration occurs near the coast up to 60 km offshore. The interannual variability of Chl-a, SST and Ekman transport was also studied considering summer and winter months. Generally, 2005, 2006 and 2007 were the most productive years during the summer months with high Chl-a concentrations along the coast associated to the strong upwelling conditions observed. Otherwise, 1998 seemed to be the most productive year during winter. The absence of upwelling favorable conditions together with localized low SST and considerable discharges, suggests that the high Chl-a concentrations observed during this period are mainly due to the entrance of nutrients through river runoff. However, in winter, high concentrations of colored dissolved organic matter (CDOM), associated with river runoff, are present in the ocean surface, leading to an erroneous strong signal of the satellite. During winter correlations of 0.58 and 0.49 were found between Chl-a concentration and Douro and Minho discharges, respectively, evidencing that high Chl-a concentration was related with river runoff. Otherwise, during summer, Chl-a and Ekman transport exhibited a

  20. The role of atmospheric diagnosis and Big Data science in improving hydroclimatic extreme prediction and the merits of climate informed prediction for future water resources management

    Science.gov (United States)

    Lu, Mengqian; Lall, Upmanu

    2017-04-01

    The threats that hydroclimatic extremes pose to sustainable development, safety and operation of infrastructure are both severe and growing. Recent heavy precipitation triggered flood events in many regions and increasing frequency and intensity of extreme precipitation suggested by various climate projections highlight the importance of understanding the associated hydrometeorological patterns and space-time variability of such extreme events, and developing a new approach to improve predictability with a better estimation of uncertainty. This clear objective requires the optimal utility of Big Data analytics on multi-source datasets to extract informative predictors from the complex ocean-atmosphere coupled system and develop a statistical and physical based framework. The proposed presentation includes the essence of our selected works in the past two years, as part of our Global Floods Initiatives. Our approach for an improved extreme prediction begins with a better understanding of the associated atmospheric circulation patterns, under the influence and regulation of slowly changing oceanic boundary conditions [Lu et al., 2013, 2016a; Lu and Lall, 2016]. The study of the associated atmospheric circulation pattern and the regulation of teleconnected climate signals adopted data science techniques and statistical modeling recognizing the nonstationarity and nonlinearity of the system, as the underlying statistical assumptions of the classical extreme value frequency analysis are challenged in hydroclimatic studies. There are two main factors that are considered important for understanding how future flood risk will change. One is the consideration of moisture holding capacity as a function of temperature, as suggested by Clausius-Clapeyron equation. The other is the strength of the convergence or convection associated with extreme precipitation. As convergence or convection gets stronger, rain rates can be expected to increase if the moisture is available. For

  1. Ensemble approach combining multiple methods improves human transcription start site prediction

    LENUS (Irish Health Repository)

    Dineen, David G

    2010-11-30

    Abstract Background The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (\\'Profisi Ensemble\\') using predictions from 7 programs, along with 2 other data sources. Support vector machines using \\'full\\' and \\'reduced\\' data sets are combined in an either\\/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions Supervised learning methods are a useful way to combine predictions from diverse sources.

  2. Improved predictions of nuclear reaction rates for astrophysics applications with the TALYS reaction code

    International Nuclear Information System (INIS)

    Goriely, S.; Hilaire, S.; Koning, A.J.

    2008-01-01

    Nuclear reaction rates for astrophysics applications are traditionally determined on the basis of Hauser-Feshbach reaction codes, like MOST. These codes use simplified schemes to calculate the capture reaction cross section on a given target nucleus, not only in its ground state but also on the different thermally populated states of the stellar plasma at a given temperature. Such schemes include a number of approximations that have never been tested, such as an approximate width fluctuation correction, the neglect of delayed particle emission during the electromagnetic decay cascade or the absence of the pre-equilibrium contribution at increasing incident energies. New developments have been brought to the reaction code TALYS to estimate the Maxwellian-averaged reaction rates of astrophysics relevance. These new developments give us the possibility to calculate with an improved accuracy the reaction cross sections and the corresponding astrophysics rates. The TALYS predictions for the thermonuclear rates of astrophysics relevance are presented and compared with those obtained with the MOST code on the basis of the same nuclear ingredients for nuclear structure properties, optical model potential, nuclear level densities and γ-ray strength. It is shown that, in particular, the pre-equilibrium process significantly influences the astrophysics rates of exotic neutron-rich nuclei. The reciprocity theorem traditionally used in astrophysics to determine photo-rates is also shown no to be valid for exotic nuclei. The predictions obtained with different nuclear inputs are also analyzed to provide an estimate of the theoretical uncertainties still affecting the reaction rate prediction far away from the experimentally known regions. (authors)

  3. Improving students' meaningful learning on the predictive nature of quantum mechanics

    Directory of Open Access Journals (Sweden)

    Rodolfo Alves de Carvalho Neto

    2009-03-01

    Full Text Available This paper deals with research about teaching quantum mechanics to 3rd year high school students and their meaningful learning of its predictive aspect; it is based on the Master’s dissertation of one of the authors (CARVALHO NETO, 2006. While teaching quantum mechanics, we emphasized its predictive and essentially probabilistic nature, based on Niels Bohr’s complementarity interpretation (BOHR, 1958. In this context, we have discussed the possibility of predicting measurement results in well-defined experimental contexts, even for individual events. Interviews with students reveal that they have used quantum mechanical ideas, suggesting their meaningful learning of the essentially probabilistic predictions of quantum mechanics.

  4. Does increasing steps per day predict improvement in physical function and pain interference in adults with fibromyalgia?

    Science.gov (United States)

    Kaleth, Anthony S; Slaven, James E; Ang, Dennis C

    2014-12-01

    To examine the concurrent and predictive associations between the number of steps taken per day and clinical outcomes in patients with fibromyalgia (FM). A total of 199 adults with FM (mean age 46.1 years, 95% women) who were enrolled in a randomized clinical trial wore a hip-mounted accelerometer for 1 week and completed self-report measures of physical function (Fibromyalgia Impact Questionnaire-Physical Impairment [FIQ-PI], Short Form 36 [SF-36] health survey physical component score [PCS], pain intensity and interference (Brief Pain Inventory [BPI]), and depressive symptoms (Patient Health Questionnaire-8 [PHQ-8]) as part of their baseline and followup assessments. Associations of steps per day with self-report clinical measures were evaluated from baseline to week 12 using multivariate regression models adjusted for demographic and baseline covariates. Study participants were primarily sedentary, averaging 4,019 ± 1,530 steps per day. Our findings demonstrate a linear relationship between the change in steps per day and improvement in health outcomes for FM. Incremental increases on the order of 1,000 steps per day were significantly associated with (and predictive of) improvements in FIQ-PI, SF-36 PCS, BPI pain interference, and PHQ-8 (all P physical activity. An exercise prescription that includes recommendations to gradually accumulate at least 5,000 additional steps per day may result in clinically significant improvements in outcomes relevant to patients with FM. Future studies are needed to elucidate the dose-response relationship between steps per day and patient outcomes in FM. Copyright © 2014 by the American College of Rheumatology.

  5. Energy transfer in isolated LHC II studied by femtosecond pump-probe technique

    CERN Document Server

    Yang Yi; Liu Yuan; Liu Wei Min; Zhu Rong Yi; Qian Shi Xiong; Xu Chun He

    2003-01-01

    Excitation energy transfer in the isolated light-harvesting chlorophyll (Chl)-a/b protein complex of photosystem II (LHC II) was studied by the one-colour pump-probe technique with femtosecond time resolution. After exciting Chl-b by 638nm beam, the dynamic behaviour shows that the ultrafast energy transfer from Chl-b at positions of B2, B3, and B5 to the corresponding Chl-a molecules in monomeric subunit of LHC II is in the time scale of 230fs. While with the excitation of Chl-a at 678nm, the energy transfer between excitons of Chl-a molecules has the lifetime of about 370 fs, and two other slow decay components are due to the energy transfer between different Chl-a molecules in a monomeric subunit of LHC II or in different subunits, or due to change of molecular conformation. (20 refs).

  6. Visual speech alters the discrimination and identification of non-intact auditory speech in children with hearing loss.

    Science.gov (United States)

    Jerger, Susan; Damian, Markus F; McAlpine, Rachel P; Abdi, Hervé

    2017-03-01

    Understanding spoken language is an audiovisual event that depends critically on the ability to discriminate and identify phonemes yet we have little evidence about the role of early auditory experience and visual speech on the development of these fundamental perceptual skills. Objectives of this research were to determine 1) how visual speech influences phoneme discrimination and identification; 2) whether visual speech influences these two processes in a like manner, such that discrimination predicts identification; and 3) how the degree of hearing loss affects this relationship. Such evidence is crucial for developing effective intervention strategies to mitigate the effects of hearing loss on language development. Participants were 58 children with early-onset sensorineural hearing loss (CHL, 53% girls, M = 9;4 yrs) and 58 children with normal hearing (CNH, 53% girls, M = 9;4 yrs). Test items were consonant-vowel (CV) syllables and nonwords with intact visual speech coupled to non-intact auditory speech (excised onsets) as, for example, an intact consonant/rhyme in the visual track (Baa or Baz) coupled to non-intact onset/rhyme in the auditory track (/-B/aa or/-B/az). The items started with an easy-to-speechread/B/or difficult-to-speechread/G/onset and were presented in the auditory (static face) vs. audiovisual (dynamic face) modes. We assessed discrimination for intact vs. non-intact different pairs (e.g., Baa:/-B/aa). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more same-as opposed to different-responses in the audiovisual than auditory mode. We assessed identification by repetition of nonwords with non-intact onsets (e.g.,/-B/az). We predicted that visual speech would cause the non-intact onset to be perceived as intact and would therefore generate more Baz-as opposed to az- responses in the audiovisual than auditory mode. Performance in the audiovisual mode showed more same

  7. A community effort to assess and improve drug sensitivity prediction algorithms.

    Science.gov (United States)

    Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

    2014-12-01

    Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

  8. Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

    Directory of Open Access Journals (Sweden)

    Trine Krogh-Madsen

    2017-12-01

    Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

  9. Improving LMA predictions with non standard interactions

    CERN Document Server

    Das, C R

    2010-01-01

    It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

  10. Trajectory Analysis and Prediction for Improved Pedestrian Safety

    DEFF Research Database (Denmark)

    Møgelmose, Andreas; Trivedi, Mohan M.; Moeslund, Thomas B.

    2015-01-01

    This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pede...

  11. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  12. Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features

    Science.gov (United States)

    Hou, Rui; Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

    2018-02-01

    Predicting whether ductal carcinoma in situ (DCIS) identified at core biopsy contains occult invasive disease is an import task since these "upstaged" cases will affect further treatment planning. Therefore, a prediction model that better classifies pure DCIS and upstaged DCIS can help avoid overtreatment and overdiagnosis. In this work, we propose to improve this classification performance with the aid of two other related classes: Atypical Ductal Hyperplasia (ADH) and Invasive Ductal Carcinoma (IDC). Our data set contains mammograms for 230 cases. Specifically, 66 of them are ADH cases; 99 of them are biopsy-proven DCIS cases, of whom 25 were found to contain invasive disease at the time of definitive surgery. The remaining 65 cases were diagnosed with IDC at core biopsy. Our hypothesis is that knowledge can be transferred from training with the easier and more readily available cases of benign but suspicious ADH versus IDC that is already apparent at initial biopsy. Thus, embedding both ADH and IDC cases to the classifier will improve the performance of distinguishing upstaged DCIS from pure DCIS. We extracted 113 mammographic features based on a radiologist's annotation of clusters.Our method then added both ADH and IDC cases during training, where ADH were "force labeled" or treated by the classifier as pure DCIS (negative) cases, and IDC were labeled as upstaged DCIS (positive) cases. A logistic regression classifier was built based on the designed training dataset to perform a prediction of whether biopsy-proven DCIS cases contain invasive cancer. The performance was assessed by repeated 5-fold CrossValidation and Receiver Operating Characteristic(ROC) curve analysis. While prediction performance with only training on DCIS dataset had an average AUC of 0.607(%95CI, 0.479-0.721). By adding both ADH and IDC cases for training, we improved the performance to 0.691(95%CI, 0.581-0.801).

  13. Model predictive control as a tool for improving the process operation of MSW combustion plants

    International Nuclear Information System (INIS)

    Leskens, M.; Kessel, L.B.M. van; Bosgra, O.H.

    2005-01-01

    In this paper a feasibility study is presented on the application of the advanced control strategy called model predictive control (MPC) as a tool for obtaining improved process operation performance for municipal solid waste (MSW) combustion plants. The paper starts with a discussion of the operational objectives and control of such plants, from which a motivation follows for applying MPC to them. This is followed by a discussion on the basic idea behind this advanced control strategy. After that, an MPC-based combustion control system is proposed aimed at tackling a typical MSW combustion control problem and, using this proposed control system, an assessment is made of the improvement in performance that an MPC-based MSW combustion control system can provide in comparison to conventional MSW combustion control systems. This assessment is based on simulations using an experimentally obtained process and disturbance model of a real-life large-scale MSW combustion plant

  14. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yang; Bax, Ad, E-mail: bax@nih.go [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

    2010-09-15

    NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and {sup 13}C{sup {beta}} chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and {sup 13}C{sup {beta}} atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for {delta}{sup 15}N, {delta}{sup 13}C', {delta}{sup 13}C{sup {alpha}}, {delta}{sup 13}C{sup {beta}}, {delta}{sup 1}H{sup {alpha}} and {delta}{sup 1}H{sup N}, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

  15. Standard deviation of carotid young's modulus and presence or absence of plaque improves prediction of coronary heart disease risk.

    Science.gov (United States)

    Niu, Lili; Zhang, Yanling; Qian, Ming; Xiao, Yang; Meng, Long; Zheng, Rongqin; Zheng, Hairong

    2017-11-01

    The stiffness of large arteries and the presence or absence of plaque are associated with coronary heart disease (CHD). Because arterial walls are biologically heterogeneous, the standard deviation of Young's modulus (YM-std) of the large arteries may better predict coronary atherosclerosis. However, the role of YM-std in the occurrence of coronary events has not been addressed so far. Therefore, this study investigated whether the carotid YM-std and the presence or absence of plaque improved CHD risk prediction. One hundred and three patients with CHD (age 66 ± 11 years) and 107 patients at high risk of atherosclerosis (age 61 ± 7 years) were recruited. Carotid YM was measured by the vessel texture matching method, and YM-std was calculated. Carotid intima-media thickness was measured by the MyLab 90 ultrasound Platform employed dedicated software RF-tracking technology. In logistic regression analysis, YM-std (OR = 1·010; 95% CI = 1·003-1·016), carotid plaque (OR = 16·759; 95% CI = 3·719-75·533) and YM-std plus plaque (OR = 0·989; 95% CI = 0·981-0·997) were independent predictors of CHD. The traditional risk factors (TRF) plus YM-std plus plaque model showed a significant improvement in area under the receiver-operating characteristic curve (AUC), which increased from 0·717 (TRF only) to 0·777 (95% CI for the difference in adjusted AUC: 0·010-0·110). Carotid YM-std is a powerful independent predictor of CHD. Adding plaque and YM-std to TRF improves CHD risk prediction. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

  16. Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey.

    Science.gov (United States)

    Su, G; Ma, P; Nielsen, U S; Aamand, G P; Wiggans, G; Guldbrandtsen, B; Lund, M S

    2016-06-01

    Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such like Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing size of reference population in Danish Jersey. The first approach was to include North American Jersey bulls in Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were: (1) about 1050 Danish bulls, (2) about 1050 Danish bulls and about 1150 US bulls. In validation on cows, about 3000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were: (1) about 1250 Danish bulls, (2) about 1250 Danish bulls and about 1150 US bulls, (3) about 1250 Danish bulls and about 4800 cows, (4) about 1250 Danish bulls, 1150 US bulls and 4800 Danish cows. Genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint DK-US bull reference population led to higher reliability of genomic prediction than the DK bull reference population for six traits, but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of US bull in reference population was 6.6 percentage points in average over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and US bulls were not accumulative. The total gain of including both US bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference

  17. Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch.

    Science.gov (United States)

    Madai, Vince Istvan; Wood, Carla N; Galinovic, Ivana; Grittner, Ulrike; Piper, Sophie K; Revankar, Gajanan S; Martin, Steve Z; Zaro-Weber, Olivier; Moeller-Hartmann, Walter; von Samson-Himmelstjerna, Federico C; Heiss, Wolf-Dieter; Ebinger, Martin; Fiebach, Jochen B; Sobesky, Jan

    2016-01-01

    With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction

  18. Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation.

    Science.gov (United States)

    Duan, Jinli; Jiao, Feng; Zhang, Qishan; Lin, Zhibin

    2017-08-06

    The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

  19. Progress in Space Weather Modeling and Observations Needed to Improve the Operational NAIRAS Model Aircraft Radiation Exposure Predictions

    Science.gov (United States)

    Mertens, C. J.; Kress, B. T.; Wiltberger, M. J.; Tobiska, W.; Xu, X.

    2011-12-01

    The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) is a prototype operational model for predicting commercial aircraft radiation exposure from galactic and solar cosmic rays. NAIRAS predictions are currently streaming live from the project's public website, and the exposure rate nowcast is also available on the SpaceWx smartphone app for iPhone, IPad, and Android. Cosmic rays are the primary source of human exposure to high linear energy transfer radiation at aircraft altitudes, which increases the risk of cancer and other adverse health effects. Thus, the NAIRAS model addresses an important national need with broad societal, public health and economic benefits. The processes responsible for the variability in the solar wind, interplanetary magnetic field, solar energetic particle spectrum, and the dynamical response of the magnetosphere to these space environment inputs, strongly influence the composition and energy distribution of the atmospheric ionizing radiation field. During the development of the NAIRAS model, new science questions were identified that must be addressed in order to obtain a more reliable and robust operational model of atmospheric radiation exposure. Addressing these science questions require improvements in both space weather modeling and observations. The focus of this talk is to present these science questions, the proposed methodologies for addressing these science questions, and the anticipated improvements to the operational predictions of atmospheric radiation exposure. The overarching goal of this work is to provide a decision support tool for the aviation industry that will enable an optimal balance to be achieved between minimizing health risks to passengers and aircrew while simultaneously minimizing costs to the airline companies.

  20. Improved understanding of physics processes in pedestal structure, leading to improved predictive capability for ITER

    International Nuclear Information System (INIS)

    Groebner, R.J.; Snyder, P.B.; Leonard, A.W.; Chang, C.S.; Maingi, R.; Boyle, D.P.; Diallo, A.; Hughes, J.W.; Davis, E.M.; Ernst, D.R.; Landreman, M.; Xu, X.Q.; Boedo, J.A.; Cziegler, I.; Diamond, P.H.; Eldon, D.P.; Callen, J.D.; Canik, J.M.; Elder, J.D.; Fulton, D.P.

    2013-01-01

    Joint experiment/theory/modelling research has led to increased confidence in predictions of the pedestal height in ITER. This work was performed as part of a US Department of Energy Joint Research Target in FY11 to identify physics processes that control the H-mode pedestal structure. The study included experiments on C-Mod, DIII-D and NSTX as well as interpretation of experimental data with theory-based modelling codes. This work provides increased confidence in the ability of models for peeling–ballooning stability, bootstrap current, pedestal width and pedestal height scaling to make correct predictions, with some areas needing further work also being identified. A model for pedestal pressure height has made good predictions in existing machines for a range in pressure of a factor of 20. This provides a solid basis for predicting the maximum pedestal pressure height in ITER, which is found to be an extrapolation of a factor of 3 beyond the existing data set. Models were studied for a number of processes that are proposed to play a role in the pedestal n e and T e profiles. These processes include neoclassical transport, paleoclassical transport, electron temperature gradient turbulence and neutral fuelling. All of these processes may be important, with the importance being dependent on the plasma regime. Studies with several electromagnetic gyrokinetic codes show that the gradients in and on top of the pedestal can drive a number of instabilities. (paper)