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Sample records for predicts improved response

  1. Improving models to predict phenological responses to global change

    Energy Technology Data Exchange (ETDEWEB)

    Richardson, Andrew D. [Harvard College, Cambridge, MA (United States)

    2015-11-25

    The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms. Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall). We conducted a study to investigate the phenological response of northern peatland communities to global change. Field work was conducted at the SPRUCE experiment in northern Minnesota, where we installed 10 digital cameras. Imagery from the cameras is being used to track shifts in plant phenology driven by elevated carbon dioxide and elevated temperature in the different SPRUCE experimental treatments. Camera imagery and derived products (“greenness”) is being posted in near-real time on a publicly available web page (http://phenocam.sr.unh.edu/webcam/gallery/). The images will provide a permanent visual record of the progression of the experiment over the next 10 years. Integrated with other measurements collected as part of the SPRUCE program, this study is providing insight into the degree to which phenology may mediate future shifts in carbon uptake and storage by peatland ecosystems. In the future, these data will be used to develop improved models of vegetation phenology, which will be tested against ground observations collected by a local collaborator.

  2. Improved Transient Response Estimations in Predicting 40 Hz Auditory Steady-State Response Using Deconvolution Methods

    Directory of Open Access Journals (Sweden)

    Xiaodan Tan

    2017-12-01

    Full Text Available The auditory steady-state response (ASSR is one of the main approaches in clinic for health screening and frequency-specific hearing assessment. However, its generation mechanism is still of much controversy. In the present study, the linear superposition hypothesis for the generation of ASSRs was investigated by comparing the relationships between the classical 40 Hz ASSR and three synthetic ASSRs obtained from three different templates for transient auditory evoked potential (AEP. These three AEPs are the traditional AEP at 5 Hz and two 40 Hz AEPs derived from two deconvolution algorithms using stimulus sequences, i.e., continuous loop averaging deconvolution (CLAD and multi-rate steady-state average deconvolution (MSAD. CLAD requires irregular inter-stimulus intervals (ISIs in the sequence while MSAD uses the same ISIs but evenly-spaced stimulus sequences which mimics the classical 40 Hz ASSR. It has been reported that these reconstructed templates show similar patterns but significant difference in morphology and distinct frequency characteristics in synthetic ASSRs. The prediction accuracies of ASSR using these templates show significant differences (p < 0.05 in 45.95, 36.28, and 10.84% of total time points within four cycles of ASSR for the traditional, CLAD, and MSAD templates, respectively, as compared with the classical 40 Hz ASSR, and the ASSR synthesized from the MSAD transient AEP suggests the best similarity. And such a similarity is also demonstrated at individuals only in MSAD showing no statistically significant difference (Hotelling's T2 test, T2 = 6.96, F = 0.80, p = 0.592 as compared with the classical 40 Hz ASSR. The present results indicate that both stimulation rate and sequencing factor (ISI variation affect transient AEP reconstructions from steady-state stimulation protocols. Furthermore, both auditory brainstem response (ABR and middle latency response (MLR are observed in contributing to the composition of ASSR but

  3. 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.

  4. Predicting the effective response of bulk polycrystalline ferroelectric ceramics via improved spectral phase field methods

    Science.gov (United States)

    Vidyasagar, A.; Tan, W. L.; Kochmann, D. M.

    2017-09-01

    Understanding the electromechanical response of bulk polycrystalline ferroelectric ceramics requires scale-bridging approaches. Recent advances in fast numerical methods to compute the homogenized mechanical response of materials with heterogeneous microstructure have enabled the solution of hitherto intractable systems. In particular, the use of a Fourier-based spectral method as opposed to the traditional finite element method has gained significant interest in the homogenization of periodic microstructures. Here, we solve the periodic, electro-mechanically-coupled boundary value problem at the mesoscale of polycrystalline ferroelectrics in order to extract the effective response of barium titanate (BaTiO3) and lead zirconate titanate (PZT) under applied electric fields. Results include the effective electric hysteresis and the associated butterfly curve of strain vs. electric field for mean stress-free electric loading. Computational predictions of the 3D polycrystalline response show convincing agreement with our experimental electric cycling and strain hysteresis data for PZT-5A. In addition to microstructure-dependent effective physics, we also show how finite-difference-based approximations in the spectral solution scheme significantly reduce instability and ringing phenomena associated with spectral techniques and lead to spatial convergence with h-refinement, which have been major challenges when modeling high-contrast systems such as polycrystals.

  5. Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response

    Science.gov (United States)

    Richard A. Shakesby; John A. Moody; Deborah A. Martin; Pete Robichaud

    2016-01-01

    Advances in research into wildfire impacts on runoff and erosion have demonstrated increasing complexity of controlling factors and responses, which, combined with changing fire frequency, present challenges for modellers. We convened a conference attended by experts and practitioners in post-wildfire impacts, meteorology and related research, including...

  6. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

    Science.gov (United States)

    Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha C.; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora

    2018-01-01

    Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

  7. Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen

    NARCIS (Netherlands)

    De Luca, Andrea; Flandre, Philippe; Dunn, David; Zazzi, Maurizio; Wensing, Annemarie; Santoro, Maria Mercedes; Günthard, Huldrych F.; Wittkop, Linda; Kordossis, Theodoros; Garcia, Federico; Castagna, Antonella; Cozzi-Lepri, Alessandro; Churchill, Duncan; De Wit, Stéphane; Brockmeyer, Norbert H.; Imaz, Arkaitz; Mussini, Cristina; Obel, Niels; Perno, Carlo Federico; Roca, Bernardino; Reiss, Peter; Schülter, Eugen; Torti, Carlo; van Sighem, Ard; Zangerle, Robert; Descamps, Diane; Mocroft, Amanda; Kirk, Ole; Sabin, Caroline; De Wit, Stéphane; Casabona, Jordi; Miró, Jose M.; Touloumi, Giota; Garrido, Myriam; Teira, Ramon; Wit, Ferdinand; Warszawski, Josiane; Meyer, Laurence; Dabis, François; Krause, Murielle Mary; Ghosn, Jade; Leport, Catherine; Prins, Maria; Bucher, Heiner; Gibb, Diana; Fätkenheuer, Gerd; del Amo, Julia; Thorne, Claire; Stephan, Christoph; Pérez-Hoyos, Santiago; Hamouda, Osamah; Bartmeyer, Barbara; Chkhartishvili, Nikoloz; Noguera-Julian, Antoni; Antinori, Andrea; d'Arminio Monforte, Antonella; Prieto, Luis; Conejo, Pablo Rojo; Soriano-Arandes, Antoni; Battegay, Manuel; Kouyos, Roger; Tookey, Pat; Konopnick, Deborah; Goetghebuer, Tessa; Sönnerborg, Anders; Haerry, David; de Wit, Stéphane; Costagliola, Dominique; Raben, Dorthe; Chêne, Geneviève; Ceccherini-Silberstein, Francesca; Günthard, Huldrych; Judd, Ali; Barger, Diana; Schwimmer, Christine; Termote, Monique; Campbell, Maria; Frederiksen, Casper M.; Friis-Møller, Nina; Kjaer, Jesper; Brandt, Rikke Salbøl; Berenguer, Juan; Bohlius, Julia; Bouteloup, Vincent; Davies, Mary Anne; Dorrucci, Maria; Egger, Matthias; Furrer, Hansjakob; Guiguet, Marguerite; Grabar, Sophie; Lambotte, Olivier; Leroy, Valériane; Lodi, Sara; Matheron, Sophie; Monge, Susana; Nakagawa, Fumiyo; Paredes, Roger; Phillips, Andrew; Puoti, Massimo; Schomaker, Michael; Smit, Colette; Sterne, Jonathan; Thiebaut, Rodolphe; van der Valk, Marc; Wyss, Natasha; Aubert, V.; Battegay, M.; Bernasconi, E.; Böni, J.; Burton-Jeangros, C.; Calmy, A.; Cavassini, M.; Dollenmaier, G.; Egger, M.; Elzi, L.; Fehr, J.; Fellay, J.; Furrer, H.; Fux, C. A.; Gorgievski, M.; Günthard, H.; Haerry, D.; Hasse, B.; Hirsch, H. H.; Hoffmann, M.; Hösli, I.; Kahlert, C.; Kaiser, L.; Keiser, O.; Klimkait, T.; Kouyos, R.; Kovari, H.; Ledergerber, B.; Martinetti, G.; Martinez de Tejada, B.; Metzner, K.; Müller, N.; Nadal, D.; Nicca, D.; Pantaleo, G.; Rauch, A.; Regenass, S.; Rickenbach, M.; Rudin, C.; Schöni-Affolter, F.; Schmid, P.; Schüpbach, J.; Speck, R.; Tarr, P.; Telenti, A.; Trkola, A.; Vernazza, P.; Weber, R.; Yerly, S.

    2016-01-01

    Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large

  8. Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen

    DEFF Research Database (Denmark)

    De Luca, Andrea; Flandre, Philippe; Dunn, David

    2016-01-01

    OBJECTIVES: The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. METHODS: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from...... was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). RESULTS: TCEs were...

  9. Environmental change and hydrological responses in the interior of western Canada: Towards improved understanding, diagnosis, and prediction by the Changing Cold Regions Network

    Science.gov (United States)

    DeBeer, C. M.; Wheater, H. S.; Carey, S. K.; Pomeroy, J. W.; Stewart, R. E.

    2016-12-01

    The past several decades have been a period of rapid climatic and environmental change. In western Canada, as in other areas globally, warming and changes in precipitation have led to vast reductions in seasonal snowcover and freshwater ice cover, retreating glaciers, thawing permafrost, changing forest composition and structure, increasing northern shrub coverage, and earlier timing of river flows in spring. Yet streamflow volume has exhibited a variety of responses across the region and over different time scales, and patterns of change are not easily generalizable. Improved understanding, diagnosis, and prediction of the rapidly changing components of the Earth system are key to managing uncertain water futures, but this is challenging due to complex system behavior and sometimes compensatory responses. The Changing Cold Regions Network (CCRN) is a Canadian research network and GEWEX Regional Hydroclimate Project that is addressing these issues, with a geographic focus on the Saskatchewan and Mackenzie River basins. This paper will present examples of the changes that have been observed at a set of long-term and well-studied headwater research basins, and highlight how various processes confound hydrological responses here, pointing to the need for careful diagnosis. We will discuss some recent CCRN activities and progress toward improving conceptual understanding and developing scenarios of change for the 21st century, which can then be applied within process-based hydrological models for future prediction. Several priority research areas that will be a focus of continued work in CCRN will be recommended.

  10. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

    Science.gov (United States)

    Xiaohui Feng; María Uriarte; Grizelle González; Sasha Reed; Jill Thompson; Jess K. Zimmerman; Lora Murphy

    2018-01-01

    Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very...

  11. Metformin improves glucose effectiveness, not insulin sensitivity: predicting treatment response in women with polycystic ovary syndrome in an open-label, interventional study.

    Science.gov (United States)

    Pau, Cindy T; Keefe, Candace; Duran, Jessica; Welt, Corrine K

    2014-05-01

    Although metformin is widely used to improve insulin resistance in women with polycystic ovary syndrome (PCOS), its mechanism of action is complex, with inconsistent effects on insulin sensitivity and variability in treatment response. The aim of the study was to delineate the effect of metformin on glucose and insulin parameters, determine additional treatment outcomes, and predict patients with PCOS who will respond to treatment. We conducted an open-label, interventional study at an academic medical center. Women with PCOS (n = 36) diagnosed by the National Institutes of Health criteria participated in the study. Subjects underwent fasting blood sampling, an IV glucose tolerance test, dual-energy x-ray absorptiometry scan, transvaginal ultrasound, and measurement of human chorionic gonadotropin-stimulated androgen levels before and after 12 weeks of treatment with metformin extended release 1500 mg/d. Interval visits were performed to monitor anthropometric measurements and menstrual cycle parameters. Changes in glucose and insulin parameters, androgen levels, anthropometric measurements, and ovulatory menstrual cycles were evaluated. Insulin sensitivity did not change despite weight loss. Glucose effectiveness (P = .002) and the acute insulin response to glucose (P = .002) increased, and basal glucose levels (P = .001) decreased after metformin treatment. T levels also decreased. Women with improved ovulatory function (61%) had lower baseline T levels and lower baseline and stimulated T and androstenedione levels after metformin treatment (all P effectiveness and insulin sensitivity, metformin does not improve insulin sensitivity in women with PCOS but does improve glucose effectiveness. The improvement in glucose effectiveness may be partially mediated by decreased glucose levels. T levels also decreased with metformin treatment. Ovulation during metformin treatment was associated with lower baseline T levels and greater T and androstenedione decreases during

  12. Understanding chemical-potential-related transient pore-pressure response to improve real-time borehole (in)stability predictions

    Energy Technology Data Exchange (ETDEWEB)

    Tare, U.A.; Mody, F.K.; Mese, A.I. [Halliburton Energy Services, Cairo (Egypt)

    2000-11-01

    Experimental studies were conducted to explain the concept of a real-time wellbore (in)stability logging methodology. The role of the chemical potential of drilling fluids on transient pore pressure and time-dependent rock property alterations of shale formations was examined by providing details about a pore pressure transmission (PPT) test. The PPT experiments exposed formation (shale) cores under simulated downhole conditions to various salt solutions and drilling fluids. The main objective was to translate the results of the PPT tests to actual drilling conditions. A 20 per cent w/w calcium chloride solution was exposed to a Pierre II shale under high pressure in the PPT apparatus. The PPT test was used to estimate the impact of a drilling fluid on shale pore pressure. The efficiency of the salt solution/shale system was also estimated. Estimates of the dynamic rock properties were made based on the obtained acoustic data. It was determined that in order to accurately model time-dependent wellbore (in)stability in the field, it is important to calibrate representative shale core response to drilling fluids under realistic in-situ conditions. The 20 per cent w/w calcium chloride solution showed very low membrane efficiency of 4.45 per cent. It was concluded that changes in the shale dynamic rock properties as a function of test fluid exposure can be obtained from the simultaneous acquisition of sonic compression and shear wave velocity data. 12 refs., 5 figs.

  13. Predicting response to epigenetic therapy

    DEFF Research Database (Denmark)

    Treppendahl, Marianne B; Sommer Kristensen, Lasse; Grønbæk, Kirsten

    2014-01-01

    of good pretreatment predictors of response is of great value. Many clinical parameters and molecular targets have been tested in preclinical and clinical studies with varying results, leaving room for optimization. Here we provide an overview of markers that may predict the efficacy of FDA- and EMA...

  14. Improving tumour response

    International Nuclear Information System (INIS)

    Bentzen, S.

    2003-01-01

    Radiation oncology is in the middle of the most exciting developments in its 100-year history. Progress in treatment planning and delivery, in medical imaging and in basic cancer and normal tissue biology is likely to change the indication for radiotherapy as well as the way it is prescribed and delivered. Technological and conceptual advances, in particular the development of the multi-leaf collimator and the concept of inverse treatment planning, have led to the introduction of intensity modulated radiation therapy (IMRT) with its capability to plan and deliver non-uniform dose distributions in the clinic. This has forced us to re-think radiation oncology: refining the indication for radiotherapy, optimizing the prescription of dose distributions and considering how, based on clinical evidence, radiation can best be combined with other treatment modalities, surgery, cytotoxic chemotherapy and biologically targeted therapies. The attraction of radiation therapy as an element of multi-modality cancer therapy is that it induces DNA damage that can be modulated in space and time. Progress in basic cancer biology, genomics and proteomics, as well as biological imaging provides novel avenues for individualization of cancer therapy and for biological optimization of radiotherapy. In improving cancer care, it is the therapeutic ratio, rather than tumour control per se, that must be optimised. Interestingly, the two main avenues for improving the effectiveness of radiotherapy currently being actively pursued in the clinic generally aim at different sides of the therapeutic ratio: 3D conformal radiotherapy and IMRT predominantly aim to reduce normal-tissue side effects - and by doing this, open the way for dose escalation that may lead to increased tumour control rates - whereas combined radio-chemotherapy aims to improve tumour response - while keeping the fingers crossed that this will not increase normal-tissue complications to the same extent. In parallel with these

  15. Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL

    Energy Technology Data Exchange (ETDEWEB)

    Mikhaeel, N.G.; Smith, Daniel [Guy' s and St Thomas' NHS Foundation Trust, Department of Clinical Oncology, London (United Kingdom); Dunn, Joel T.; Phillips, Michael; Barrington, Sally F. [King' s College London, PET Imaging Centre at St Thomas' Hospital, Division of Imaging Sciences and Biomedical Engineering, London (United Kingdom); Moeller, Henrik [King' s College London, Department of Cancer Epidemiology and Population Health, London (United Kingdom); Fields, Paul A.; Wrench, David [Guy' s and St Thomas' NHS Foundation Trust, Department of Haematology, London (United Kingdom)

    2016-07-15

    The study objectives were to assess the prognostic value of quantitative PET and to test whether combining baseline metabolic tumour burden with early PET response could improve predictive power in DLBCL. A total of 147 patients with DLBCL underwent FDG-PET/CT scans before and after two cycles of RCHOP. Quantitative parameters including metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were measured, as well as the percentage change in these parameters. Cox regression analysis was used to test the relationship between progression-free survival (PFS) and the study variables. Receiver operator characteristics (ROC) analysis determined the optimal cut-off for quantitative variables, and Kaplan-Meier survival analysis was performed. The median follow-up was 3.8 years. As MTV and TLG measures correlated strongly, only MTV measures were used for multivariate analysis (MVA). Baseline MTV (MTV-0) was the only statistically significant predictor of PFS on MVA. The optimal cut-off for MTV-0 was 396 cm{sup 3}. A model combing MTV-0 and Deauville score (DS) separated the population into three distinct prognostic groups: good (MTV-0 < 400; 5-year PFS > 90 %), intermediate (MTV-0 ≥ 400+ DS1-3; 5-year PFS 58.5 %) and poor (MTV-0 ≥ 400+ DS4-5; 5-year PFS 29.7 %) MTV-0 is an important prognostic factor in DLBCL. Combining MTV-0 and early PET/CT response improves the predictive power of interim PET and defines a poor-prognosis group in whom most of the events occur. (orig.)

  16. BCL-2, in combination with MVP and IGF-1R expression, improves prediction of clinical outcome in complete response cervical carcinoma patients treated by radiochemotherapy.

    Science.gov (United States)

    Henríquez-Hernández, Luis Alberto; Lloret, Marta; Pinar, Beatriz; Bordón, Elisa; Rey, Agustín; Lubrano, Amina; Lara, Pedro Carlos

    2011-09-01

    To investigate whether BCL-2 expression would improve MVP/IGF-1R prediction of clinical outcome in cervix carcinoma patients treated by radiochemotherapy, and suggest possible mechanisms behind this effect. Fifty consecutive patients, who achieved complete response to treatment, from a whole series of 60 cases suffering from non-metastatic localized cervical carcinoma, were prospectively included in this study from July 1999 to December 2003. Follow-up was closed in January 2011. All patients received pelvic radiation (45-64.80 Gy in 1.8-2 Gy fractions) with concomitant cisplatin at 40 mg/m2/week doses followed by brachytherapy. Oncoprotein expression was studied by immunohistochemistry in paraffin-embedded tumour tissue. No relation was found between BCL-2 and clinicopathological variables. High MVP/IGF-1R/BCL-2 tumour expression was strongly related to poor local and regional disease-free survival (PMVP, and IGF-1R overexpression were related to poorer clinical outcome in cervical cancer patients who achieved clinical complete response to radiochemotherapy. The NHEJ repair protein Ku70/80 expression could be involved in the regulation of these oncoproteins. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Improving Federal Response to Drought.

    Science.gov (United States)

    Wilhite, Donald A.; Rosenberg, Norman J.; Glantz, Michael H.

    1986-03-01

    Severe and widespread drought occurred over a large portion of the United States between 1974 and 1977. Impacts on agriculture and other industries, as well as local water supplies, were substantial. The federal government responded with forty assistance programs administered by sixteen federal agencies. Assistance was provided primarily in the form of loans and grants to people, businesses and governments experiencing hardship caused by drought. The total cost of the program is estimated at $7-8 billion.Federal response to the mid-1970s drought was largely untimely, ineffective and poorly coordinated. Four recommendations are offered that, if implemented, would improve future drought assessment and response efforts: 1) reliable and timely informational products and dissemination plans; 2) improved impact assessment techniques, especially in the agricultural sector, for use by government to identify periods of enhanced risk and to trigger assistance measures; 3) administratively centralized drought declaration procedures that are well publicized and consistently applied; and 4) standby assistance measures that encourage appropriate levels of risk management by producers and that are equitable, consistent and predictable. The development of a national drought plan that incorporates these four items is recommended. Atmospheric scientists have an important role to play in the collection and interpretation of near-real time weather data for use by government decision makers.

  18. Caregiver Responsiveness to the Family Bereavement Program: What predicts responsiveness? What does responsiveness predict?

    OpenAIRE

    Schoenfelder, Erin N.; Sandler, Irwin N.; Millsap, Roger E.; Wolchik, Sharlene A.; Berkel, Cady; Ayers, Timothy S.

    2013-01-01

    The study developed a multi-dimensional measure to assess participant responsiveness to a preventive intervention, and applied this measure to study how participant baseline characteristics predict responsiveness and how responsiveness predicts program outcomes. The study was conducted with caregivers who participated in the parenting-focused component of the Family Bereavement Program (FBP), a prevention program for families that have experienced parental death. The sample consisted of 89 ca...

  19. 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.

  20. Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

    Science.gov (United States)

    Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

    2017-05-12

    A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set

  1. Mood Predicts Response to Placebo CPAP

    Directory of Open Access Journals (Sweden)

    Carl J. Stepnowsky

    2012-01-01

    Full Text Available Study Objectives. Continuous positive airway pressure (CPAP therapy is efficacious for treating obstructive sleep apnea (OSA, but recent studies with placebo CPAP (CPAP administered at subtherapeutic pressure have revealed nonspecific (or placebo responses to CPAP treatment. This study examined baseline psychological factors associated with beneficial effects from placebo CPAP treatment. Participants. Twenty-five participants were studied with polysomnography at baseline and after treatment with placebo CPAP. Design. Participants were randomized to either CPAP treatment or placebo CPAP. Baseline mood was assessed with the Profile of Mood States (POMS. Total mood disturbance (POMS-Total was obtained by summing the six POMS subscale scores, with Vigor weighted negatively. The dependent variable was changed in apnea-hypopnea index (ΔAHI, calculated by subtracting pre- from post-CPAP AHI. Negative values implied improvement. Hierarchical regression analysis was performed, with pre-CPAP AHI added as a covariate to control for baseline OSA severity. Results. Baseline emotional distress predicted the drop in AHI in response to placebo CPAP. Highly distressed patients showed greater placebo response, with a 34% drop (i.e., improvement in AHI. Conclusion. These findings underscore the importance of placebo-controlled studies of CPAP treatment. Whereas such trials are routinely included in drug trials, this paper argues for their importance even in mechanical-oriented sleep interventions.

  2. 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.

  3. Predicting responses from Rasch measures.

    Science.gov (United States)

    Linacre, John M

    2010-01-01

    There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.

  4. Resting state functional connectivity predicts neurofeedback response

    Directory of Open Access Journals (Sweden)

    Dustin eScheinost

    2014-09-01

    Full Text Available Tailoring treatments to the specific needs and biology of individual patients – personalized medicine – requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD. Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI, to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC and anterior prefrontal cortex, Brodmann area (BA 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety.

  5. Prediction of treatment response to adalimumab

    DEFF Research Database (Denmark)

    Krintel, S B; Dehlendorff, C; Hetland, M L

    2016-01-01

    At least 30% of patients with rheumatoid arthritis (RA) do not respond to biologic agents, which emphasizes the need of predictive biomarkers. We aimed to identify microRNAs (miRNAs) predictive of response to adalimumab in 180 treatment-naïve RA patients enrolled in the OPtimized treatment algori...... of low expression of miR-22 and high expression of miR-886.3p was associated with EULAR good response. Future studies to assess the utility of these miRNAs as predictive biomarkers are needed.The Pharmacogenomics Journal advance online publication, 5 May 2015; doi:10.1038/tpj.2015.30....

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Predicting and measuring fluid responsiveness with echocardiography

    Directory of Open Access Journals (Sweden)

    Ashley Miller

    2016-06-01

    Full Text Available Echocardiography is ideally suited to guide fluid resuscitation in critically ill patients. It can be used to assess fluid responsiveness by looking at the left ventricle, aortic outflow, inferior vena cava and right ventricle. Static measurements and dynamic variables based on heart–lung interactions all combine to predict and measure fluid responsiveness and assess response to intravenous fluid esuscitation. Thorough knowledge of these variables, the physiology behind them and the pitfalls in their use allows the echocardiographer to confidently assess these patients and in combination with clinical judgement manage them appropriately.

  11. Response predictions using the observed autocorrelation function

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam; H. Brodtkorb, Astrid; Jensen, Jørgen Juncher

    2018-01-01

    This article studies a procedure that facilitates short-time, deterministic predictions of the wave-induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine......-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample autocorrelation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input...... show that predictions can be successfully made in a time horizon corresponding to about 8-9 wave periods ahead of current time (the moment of action)....

  12. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  13. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  14. Improving plant availability by predicting reactor trips

    International Nuclear Information System (INIS)

    Frank, M.V.; Epstein, S.A.

    1986-01-01

    Management Ahnalysis Company (MAC) has developed and applied two complementary software packages called RiTSE and RAMSES. Together they provide an mini-computer workstation for maintenance and operations personnel to dramatically reduce inadvertent reactor trips. They are intended to be used by those responsible at the plant for authorizing work during operation (such as a clearance coordinator or shift foreman in U.S. plants). They discover and represent all components, processes, and their interactions that could case a trip. They predict if future activities at the plant would cause a reactor trip, provide a reactor trip warning system and aid in post-trip cause analysis. RAMSES is a general reliability engineering software package that uses concepts of artificial intelligence to provide unique capabilities on personal and mini-computers

  15. 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.

  16. Prediction of psilocybin response in healthy volunteers.

    Science.gov (United States)

    Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X

    2012-01-01

    Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

  17. Prediction of psilocybin response in healthy volunteers.

    Directory of Open Access Journals (Sweden)

    Erich Studerus

    Full Text Available Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

  18. Improv Chat: Second Response Generation for Chatbot

    OpenAIRE

    Wei, Furu

    2018-01-01

    Existing research on response generation for chatbot focuses on \\textbf{First Response Generation} which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user's query). In this paper, we introduce a new task \\textbf{Second Response Generation}, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden ...

  19. 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…

  20. 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.

  1. 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.

  2. 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...

  3. 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

  4. 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 $\

  5. Humanitarian response: improving logistics to save lives.

    Science.gov (United States)

    McCoy, Jessica

    2008-01-01

    Each year, millions of people worldwide are affected by disasters, underscoring the importance of effective relief efforts. Many highly visible disaster responses have been inefficient and ineffective. Humanitarian agencies typically play a key role in disaster response (eg, procuring and distributing relief items to an affected population, assisting with evacuation, providing healthcare, assisting in the development of long-term shelter), and thus their efficiency is critical for a successful disaster response. The field of disaster and emergency response modeling is well established, but the application of such techniques to humanitarian logistics is relatively recent. This article surveys models of humanitarian response logistics and identifies promising opportunities for future work. Existing models analyze a variety of preparation and response decisions (eg, warehouse location and the distribution of relief supplies), consider both natural and manmade disasters, and typically seek to minimize cost or unmet demand. Opportunities to enhance the logistics of humanitarian response include the adaptation of models developed for general disaster response; the use of existing models, techniques, and insights from the literature on commercial supply chain management; the development of working partnerships between humanitarian aid organizations and private companies with expertise in logistics; and the consideration of behavioral factors relevant to a response. Implementable, realistic models that support the logistics of humanitarian relief can improve the preparation for and the response to disasters, which in turn can save lives.

  6. 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.

  7. 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.

  8. Response to intravenous fentanyl infusion predicts subsequent response to transdermal fentanyl.

    Science.gov (United States)

    Hayashi, Norihito; Kanai, Akifumi; Suzuki, Asaha; Nagahara, Yuki; Okamoto, Hirotsugu

    2016-04-01

    Prediction of the response to transdermal fentanyl (FENtd) before its use for chronic pain is desirable. We tested the hypothesis that the response to intravenous fentanyl infusion (FENiv) can predict the response to FENtd, including the analgesic and adverse effects. The study subjects were 70 consecutive patients with chronic pain. The response to fentanyl at 0.1 mg diluted in 50 ml of physiological saline and infused over 30 min was tested. This was followed by treatment with FENtd (Durotep MT patch 2.1 mg) at a dose of 12.5 µg/h for 2 weeks. Pain intensity before and after FENiv and 2 weeks after FENtd, and the response to treatment, were assessed by the numerical rating scale (NRS), clinical global impression-improvement scale (CGI-I), satisfaction scale (SS), and adverse effects. The NRS score decreased significantly from 7 (4-9) [median (range)] at baseline to 3 (0-8) after FENiv (p 0.04, each). The analgesic and side effects after intravenous fentanyl infusion can be used to predict the response to short-term transdermal treatment with fentanyl.

  9. Challenges and progress in predicting biological responses to incorporated radioactivity

    International Nuclear Information System (INIS)

    Howell, R. W.; Neti, P. V. S. V.; Pinto, M.; Gerashchenko, B. I.; Narra, V. R.; Azzam, E. I.

    2006-01-01

    Prediction of risks and therapeutic outcome in nuclear medicine largely rely on calculation of the absorbed dose. Absorbed dose specification is complex due to the wide variety of radiations emitted, non-uniform activity distribution, biokinetics, etc. Conventional organ absorbed dose estimates assumed that radioactivity is distributed uniformly throughout the organ. However, there have been dramatic improvements in dosimetry models that reflect the substructure of organs as well as tissue elements within them. These models rely on improved nuclear medicine imaging capabilities that facilitate determination of activity within voxels that represent tissue elements of ∼0.2-1 cm 3 . However, even these improved approaches assume that all cells within the tissue element receive the same dose. The tissue element may be comprised of a variety of cells having different radiosensitivities and different incorporated radioactivity. Furthermore, the extent to which non-uniform distributions of radioactivity within a small tissue element impact the absorbed dose distribution is strongly dependent on the number, type, and energy of the radiations emitted by the radionuclide. It is also necessary to know whether the dose to a given cell arises from radioactive decays within itself (self-dose) or decays in surrounding cells (cross-dose). Cellular response to self-dose can be considerably different than its response to cross-dose from the same radiopharmaceutical. Bystander effects can also play a role in the response. Evidence shows that even under conditions of 'uniform' distribution of radioactivity, a combination of organ dosimetry, voxel dosimetry and dosimetry at the cellular and multicellular levels can be required to predict response. (authors)

  10. Improving the Material Response for Slow Heat of Energetic Materials

    Energy Technology Data Exchange (ETDEWEB)

    Nichols, A L

    2010-03-08

    The goal of modern high explosive slow heat cookoff modeling is to understand the level of mechanical violence. This requires understanding the coupled thermal-mechanical-chemical system that such an environment creates. Recent advances have improved our ability to predict the time to event, and we have been making progress on predicting the mechanical response. By adding surface tension to the product gas pores in the high explosive, we have been able to reduce the current model's tendency to overpressurize confinement vessels. We describe the model and demonstrate how it affects a LX-10 STEX experiment. Issues associated with current product gas equations of state are described and examined.

  11. 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

  12. Quality improvement through multiple response optimization

    International Nuclear Information System (INIS)

    Noorossana, R.; Alemzad, H.

    2003-01-01

    The performance of a product is often evaluated by several quality characteristics. Optimizing the manufacturing process with respect to only one quality characteristic will not always lead to the optimum values for other characteristics. Hence, it would be desirable to improve the overall quality of a product by improving quality characteristics, which are considered to be important. The problem consists of optimizing several responses using multiple objective decision making approach and design of experiments. A case study will be discussed to show the application of the proposal method

  13. Slot Machine Response Frequency Predicts Pathological Gambling

    DEFF Research Database (Denmark)

    Linnet, Jakob; Rømer Thomsen, Kristine; Møller, Arne

    2013-01-01

    . This study tested the hypothesis that response frequency is associated with symptom severity in pathological gambling. We tested response frequency among twenty-two pathological gambling sufferers and twenty-one non-problem gamblers on a commercially available slot machine, and screened for pathological...... in individuals with exacerbated pathological gambling symptoms. These findings may have important implications for detecting behaviors underlying pathological gambling....

  14. Nonlinear piping damping and response predictions

    International Nuclear Information System (INIS)

    Severud, L.K.; Weiner, E.O.; Lindquist, M.R.; Anderson, M.J.; Wagner, S.E.

    1986-10-01

    The high level dynamic testing of four prototypic piping systems, used to provide benchmarks for analytical prediction comparisons, is overviewed. The size of pipe tested ranged from one-inch to six-inches in diameter and consisted of carbon steel or stainless steel material. Failure of the tested systems included progressive gross deformation or some combination of ratchetting-fatigue. Pretest failure predictions and post test comparisons using simplified elastic and elasto-plastic methods are presented. Detailed non-linear inelastic analyses are also shown, along with a typical ratchet-fatigue failure calculation. A simplified method for calculating modal equivalent viscous damping for snubbers and plastic hinges is also described. Conclusions are made regarding the applicability of the various analytical failure predictive methods and recommendations are made for future analytic and test efforts

  15. An observation on the variance of a predicted response in ...

    African Journals Online (AJOL)

    ... these properties and computational simplicity. To avoid over fitting, along with the obvious advantage of having a simpler equation, it is shown that the addition of a variable to a regression equation does not reduce the variance of a predicted response. Key words: Linear regression; Partitioned matrix; Predicted response ...

  16. Extreme wave and wind response predictions

    DEFF Research Database (Denmark)

    Jensen, Jørgen Juncher; Olsen, Anders S.; Mansour, Alaa E.

    2011-01-01

    codes and the short duration of the time domain simulations needed (typically 60–300s to cover the hydro- and aerodynamic memory effects in the response) the calculation of the mean out-crossing rates of a given response is fast. Thus non-linear effects can be included. Furthermore, the FORM analysis...... also identifies the most probable wave episodes leading to given responses.Because of the linearization of the failure surface in the FORM procedure the results are only asymptotically exact and thus MCS often also needs to be performed. In the present paper a scaling property inherent in the FORM...

  17. Slot Machine Response Frequency Predicts Pathological Gambling

    DEFF Research Database (Denmark)

    Linnet, Jakob; Rømer Thomsen, Kristine; Møller, Arne

    2013-01-01

    Slot machines are among the most addictive forms of gambling, and pathological gambling slot machine players represent the largest group of treatment seekers, accounting for 35% to 93% of the population. Pathological gambling sufferers have significantly higher response frequency (games / time......) on slot machines compared with non-problem gamblers, which may suggest increased reinforcement of the gambling behavior in pathological gambling. However, to date it is unknown whether or not the increased response frequency in pathological gambling is associated with symptom severity of the disorder....... This study tested the hypothesis that response frequency is associated with symptom severity in pathological gambling. We tested response frequency among twenty-two pathological gambling sufferers and twenty-one non-problem gamblers on a commercially available slot machine, and screened for pathological...

  18. 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.

  19. 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.

  20. 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

  1. Assessing Prediction Performance of Neoadjuvant Chemotherapy Response in Bladder Cancer

    OpenAIRE

    Cremer, Chris

    2016-01-01

    Neoadjuvant chemotherapy is a treatment routinely prescribed to patients diagnosed with muscle-invasive bladder cancer. Unfortunately, not all patients are responsive to this treatment and would greatly benefit from an accurate prediction of their expected response to chemotherapy. In this project, I attempt to develop a model that will predict response using tumour microarray data. I show that using my dataset, every method is insufficient at accurately classifying responders and non-respond...

  2. Field Response Prediction: Framing the problem.

    Energy Technology Data Exchange (ETDEWEB)

    Cabrera-Palmer, Belkis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-06-01

    Predicting the performance of radiation detection systems at field sites based on measured performance acquired under controlled conditions at test locations, e.g., the Nevada National Security Site (NNSS), remains an unsolved and standing issue within DNDO’s testing methodology. Detector performance can be defined in terms of the system’s ability to detect and/or identify a given source or set of sources, and depends on the signal generated by the detector for the given measurement configuration (i.e., source strength, distance, time, surrounding materials, etc.) and on the quality of the detection algorithm. Detector performance is usually evaluated in the performance and operational testing phases, where the measurement configurations are selected to represent radiation source and background configurations of interest to security applications.

  3. Music-related reward responses predict episodic memory performance.

    Science.gov (United States)

    Ferreri, Laura; Rodriguez-Fornells, Antoni

    2017-12-01

    Music represents a special type of reward involving the recruitment of the mesolimbic dopaminergic system. According to recent theories on episodic memory formation, as dopamine strengthens the synaptic potentiation produced by learning, stimuli triggering dopamine release could result in long-term memory improvements. Here, we behaviourally test whether music-related reward responses could modulate episodic memory performance. Thirty participants rated (in terms of arousal, familiarity, emotional valence, and reward) and encoded unfamiliar classical music excerpts. Twenty-four hours later, their episodic memory was tested (old/new recognition and remember/know paradigm). Results revealed an influence of music-related reward responses on memory: excerpts rated as more rewarding were significantly better recognized and remembered. Furthermore, inter-individual differences in the ability to experience musical reward, measured through the Barcelona Music Reward Questionnaire, positively predicted memory performance. Taken together, these findings shed new light on the relationship between music, reward and memory, showing for the first time that music-driven reward responses are directly implicated in higher cognitive functions and can account for individual differences in memory performance.

  4. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...

  5. Predicting response to incretin-based therapy

    Directory of Open Access Journals (Sweden)

    Agrawal N

    2011-04-01

    Full Text Available Sanjay Kalra1, Bharti Kalra2, Rakesh Sahay3, Navneet Agrawal41Department of Endocrinology, 2Department of Diabetology, Bharti Hospital, Karnal, India; 3Department of Endocrinology, Osmania Medical College, Hyderabad, India; 4Department of Medicine, GR Medical College, Gwalior, IndiaAbstract: There are two important incretin hormones, glucose-dependent insulin tropic polypeptide (GIP and glucagon-like peptide-1 (GLP-1. The biological activities of GLP-1 include stimulation of glucose-dependent insulin secretion and insulin biosynthesis, inhibition of glucagon secretion and gastric emptying, and inhibition of food intake. GLP-1 appears to have a number of additional effects in the gastrointestinal tract and central nervous system. Incretin based therapy includes GLP-1 receptor agonists like human GLP-1 analogs (liraglutide and exendin-4 based molecules (exenatide, as well as DPP-4 inhibitors like sitagliptin, vildagliptin and saxagliptin. Most of the published studies showed a significant reduction in HbA1c using these drugs. A critical analysis of reported data shows that the response rate in terms of target achievers of these drugs is average. One of the first actions identified for GLP-1 was the glucose-dependent stimulation of insulin secretion from islet cell lines. Following the detection of GLP-1 receptors on islet beta cells, a large body of evidence has accumulated illustrating that GLP-1 exerts multiple actions on various signaling pathways and gene products in the ß cell. GLP-1 controls glucose homeostasis through well-defined actions on the islet ß cell via stimulation of insulin secretion and preservation and expansion of ß cell mass. In summary, there are several factors determining the response rate to incretin therapy. Currently minimal clinical data is available to make a conclusion. Key factors appear to be duration of diabetes, obesity, presence of autonomic neuropathy, resting energy expenditure, plasma glucagon levels and

  6. Predicting response times for the Spotify backend

    OpenAIRE

    Yanggratoke, Rerngvit; Kreitz, Gunnar; Goldmann, Mikael; Stadler, Rolf

    2012-01-01

    We model and evaluate the performance of a distributed key-value storage system that is part of the Spotify backend. Spotify is an on-demand music streaming service, offering low-latency access to a library of over 16 million tracks and serving over 10 million users currently. We first present a simplified model of the Spotify storage architecture, in order to make its analysis feasible. We then introduce an analytical model for the distribution of the response time, a key metric in the Spoti...

  7. A model for predicting lung cancer response to therapy

    International Nuclear Information System (INIS)

    Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-01-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  8. Improving ecological response monitoring of environmental flows.

    Science.gov (United States)

    King, Alison J; Gawne, Ben; Beesley, Leah; Koehn, John D; Nielsen, Daryl L; Price, Amina

    2015-05-01

    Environmental flows are now an important restoration technique in flow-degraded rivers, and with the increasing public scrutiny of their effectiveness and value, the importance of undertaking scientifically robust monitoring is now even more critical. Many existing environmental flow monitoring programs have poorly defined objectives, nonjustified indicator choices, weak experimental designs, poor statistical strength, and often focus on outcomes from a single event. These negative attributes make them difficult to learn from. We provide practical recommendations that aim to improve the performance, scientific robustness, and defensibility of environmental flow monitoring programs. We draw on the literature and knowledge gained from working with stakeholders and managers to design, implement, and monitor a range of environmental flow types. We recommend that (1) environmental flow monitoring programs should be implemented within an adaptive management framework; (2) objectives of environmental flow programs should be well defined, attainable, and based on an agreed conceptual understanding of the system; (3) program and intervention targets should be attainable, measurable, and inform program objectives; (4) intervention monitoring programs should improve our understanding of flow-ecological responses and related conceptual models; (5) indicator selection should be based on conceptual models, objectives, and prioritization approaches; (6) appropriate monitoring designs and statistical tools should be used to measure and determine ecological response; (7) responses should be measured within timeframes that are relevant to the indicator(s); (8) watering events should be treated as replicates of a larger experiment; (9) environmental flow outcomes should be reported using a standard suite of metadata. Incorporating these attributes into future monitoring programs should ensure their outcomes are transferable and measured with high scientific credibility.

  9. Improving Ecological Response Monitoring of Environmental Flows

    Science.gov (United States)

    King, Alison J.; Gawne, Ben; Beesley, Leah; Koehn, John D.; Nielsen, Daryl L.; Price, Amina

    2015-05-01

    Environmental flows are now an important restoration technique in flow-degraded rivers, and with the increasing public scrutiny of their effectiveness and value, the importance of undertaking scientifically robust monitoring is now even more critical. Many existing environmental flow monitoring programs have poorly defined objectives, nonjustified indicator choices, weak experimental designs, poor statistical strength, and often focus on outcomes from a single event. These negative attributes make them difficult to learn from. We provide practical recommendations that aim to improve the performance, scientific robustness, and defensibility of environmental flow monitoring programs. We draw on the literature and knowledge gained from working with stakeholders and managers to design, implement, and monitor a range of environmental flow types. We recommend that (1) environmental flow monitoring programs should be implemented within an adaptive management framework; (2) objectives of environmental flow programs should be well defined, attainable, and based on an agreed conceptual understanding of the system; (3) program and intervention targets should be attainable, measurable, and inform program objectives; (4) intervention monitoring programs should improve our understanding of flow-ecological responses and related conceptual models; (5) indicator selection should be based on conceptual models, objectives, and prioritization approaches; (6) appropriate monitoring designs and statistical tools should be used to measure and determine ecological response; (7) responses should be measured within timeframes that are relevant to the indicator(s); (8) watering events should be treated as replicates of a larger experiment; (9) environmental flow outcomes should be reported using a standard suite of metadata. Incorporating these attributes into future monitoring programs should ensure their outcomes are transferable and measured with high scientific credibility.

  10. Influential Factors for Accurate Load Prediction in a Demand Response Context

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Kjærgaard, Mikkel Baun; Jørgensen, Bo Nørregaard

    2016-01-01

    Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence....... Next, the time of day that is being predicted greatly influence the prediction which is related to the weather pattern. By presenting these results we hope to improve the modeling of building loads and algorithms for Demand Response planning.......Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence...

  11. Pre-treatment amygdala volume predicts electroconvulsive therapy response

    NARCIS (Netherlands)

    ten Doesschate, Freek; van Eijndhoven, Philip; Tendolkar, Indira; van Wingen, Guido A.; van Waarde, Jeroen A.

    2014-01-01

    Electroconvulsive therapy (ECT) is an effective treatment for patients with severe depression. Knowledge on factors predicting therapeutic response may help to identify patients who will benefit most from the intervention. Based on the neuroplasticity hypothesis, volumes of the amygdala and

  12. Getting the Most out of Audience Response Systems: Predicting Student Reactions

    Science.gov (United States)

    Trew, Jennifer L.; Nelsen, Jacqueline L.

    2012-01-01

    Audience response systems (ARS) are effective tools for improving learning outcomes and student engagement in large undergraduate classes. However, if students do not accept ARS and do not find them to be useful, ARS may be less effective. Predicting and improving student perceptions of ARS may help to ensure positive outcomes. The present study…

  13. Deterministic Predictions of Vessel Responses Based on Past Measurements

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam; Jensen, Jørgen Juncher

    2017-01-01

    The paper deals with a prediction procedure from which global wave-induced responses can be deterministically predicted a short time, 10-50 s, ahead of current time. The procedure relies on the autocorrelation function and takes into account prior measurements only; i.e. knowledge about wave...

  14. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  15. Predicting primate responses to "Stochastic" demographic events.

    Science.gov (United States)

    Strier, K B

    1999-01-01

    Comparative approaches in contemporary primate behavioral ecology have tended to emphasize the deterministic properties of stochastic ecological variables. Yet, primate responses to ecological fluctuations may be mediated by the interactions among demographic processes at the levels of individuals, groups, and populations. In this paper I examine long-term data collected from June 1982-July 1998 on one expanding group of muriquis (Brachyteles arachnoides) at the Estação Biologica de Caratinga, Minas Gerais, Brazil to explore the demographic and life history correlates of reproductive seasonality and skewed infant sex ratios. Variation in the size of annual birth cohorts (≥2 infants) was positively related to variation in the annual distribution of births (r (s)=0.96,n=10,p<0.01), indicating the importance of considering the effects that the number of reproductive females may have on interpretations of reproductive seasonality. The female-biased infants sex ratio documented from 59 births was attributed exclusively to multiparous mothers. Primiparous mothers produced comparable numbers of sons (n=6) and daughters (n=7), and were increasingly likely to produce daughters with each subsequent reproductive event. Seven of the 11 females that have produced≥3 infants to date exhibited biases in favor of daughters whereas only 1 was biased in favor of sons. Variation in female sensitivity to local resource competition at different stages of their life histories may account for the female-biased infant sex ration in this population.

  16. 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

  17. 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

  18. Improving Science Communication with Responsive Web Design

    Science.gov (United States)

    Hilverda, M.

    2013-12-01

    the world use low-bandwidth connections. Communicating science effectively includes efficient delivery of the information to the reader. To meet this criteria, responsive designs should also incorporate "mobile first" elements such as serving ideal image sizes (a low resolution cell phone does not need to receive a large desktop image) and a focus on fast, readable content delivery. The technical implementation of responsive web design is constantly changing as new web standards and approaches become available. However, fundamental design principles such as grid layouts, clear typography, and proper use of white space should be an important part of content delivery within any responsive design. This presentation will discuss current responsive design approaches for improving scientific communication across multiple devices, operating systems, and bandwidth capacities. The presentation will also include example responsive designs for scientific papers and websites. Implementing a responsive design approach with a focus on content and fundamental design principles is an important step to ensuring scientific information remains clear and accessible as screens and devices continue to evolve.

  19. 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.

  20. 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.

  1. 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...

  2. 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

  3. Interventions Aimed at Improving Child Language by Improving Maternal Responsivity

    OpenAIRE

    Brady, Nancy; Warren, Steven F.; Sterling, Audra

    2009-01-01

    Maternal responsivity, or the ways in which mothers provide for, interact with, and respond to their children, helps to shape their children’s development, including language development. In this chapter, we describe maternal responsivity as a multilevel construct with different measures appropriate for each level. Molar responsivity refers to aspects of interaction style such as affect that can best be measured with rating scales. Molecular responsivity refers to contingent maternal behavior...

  4. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

    Science.gov (United States)

    Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

    2017-04-12

    Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

  5. Predictive display design for the vehicles with time delay in dynamic response

    Science.gov (United States)

    Efremov, A. V.; Tiaglik, M. S.; Irgaleev, I. H.; Efremov, E. V.

    2018-02-01

    The two ways for the improvement of flying qualities are considered: the predictive display (PD) and the predictive display integrated with the flight control system (FCS). The both ways allow to transforming the controlled element dynamics in the crossover frequency range, to improve the accuracy of tracking and to suppress the effect of time delay in the vehicle response too. The technique for optimization of the predictive law is applied to the landing task. The results of the mathematical modeling and experimental investigations carried out for this task are considered in the paper.

  6. Prediction of Early Response to Chemotherapy in Lung Cancer by Using Diffusion-Weighted MR Imaging

    Directory of Open Access Journals (Sweden)

    Jing Yu

    2014-01-01

    Full Text Available Purpose. To determine whether change of apparent diffusion coefficient (ADC value could predict early response to chemotherapy in lung cancer. Materials and Methods. Twenty-five patients with advanced non-small cell lung cancer underwent chest MR imaging including DWI before and at the end of the first cycle of chemotherapy. The tumor’s mean ADC value and diameters on MR images were calculated and compared. The grouping reference was based on serial CT scans according to Response Evaluation Criteria in Solid Tumors. Logistic regression was applied to assess treatment response prediction ability of ADC value and diameters. Results. The change of ADC value in partial response group was higher than that in stable disease group (P=0.004. ROC curve showed that ADC value could predict treatment response with 100% sensitivity, 64.71% specificity, 57.14% positive predictive value, 100% negative predictive value, and 82.7% accuracy. The area under the curve for combination of ADC value and longest diameter change was higher than any parameter alone (P≤0.01. Conclusions. The change of ADC value may be a sensitive indicator to predict early response to chemotherapy in lung cancer. Prediction ability could be improved by combining the change of ADC value and longest diameter.

  7. 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.

  8. 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.

  9. 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.

  10. Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

    Science.gov (United States)

    Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

    2017-07-01

    Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

  11. 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.

  12. 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.

  13. 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....

  14. 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

  15. 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.

  16. 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

  17. Outcome Prediction in Mathematical Models of Immune Response to Infection.

    Directory of Open Access Journals (Sweden)

    Manuel Mai

    Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

  18. Performance of immunological response in predicting virological failure.

    Science.gov (United States)

    Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

    2013-03-01

    In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

  19. Predicting responsiveness to intervention in dyslexia using dynamic assessment

    NARCIS (Netherlands)

    Aravena, S.; Tijms, J.; Snellings, P.; van der Molen, M.W.

    In the current study we examined the value of a dynamic test for predicting responsiveness to reading intervention for children diagnosedwith dyslexia. The test consisted of a 20-minute training aimed at learning eight basic letter–speech sound correspondences within an artificial orthography,

  20. Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders

    Science.gov (United States)

    2015-09-01

    early behavioral interventions are the most effective treatment for Autism Spectrum Disorder (ASD), but almost half of the children do not make...behavioral intervention . 2. KEYWORDS Autism Spectrum Disorder , implicit learning, associative learning, individual differences, functional Magnetic...2 AWARD NUMBER: W81XWH-14-1-0261 TITLE: Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders PRINCIPAL

  1. Improving Survey Response Rates in Online Panels

    DEFF Research Database (Denmark)

    Pedersen, Mogens Jin; Nielsen, Christian Videbæk

    2016-01-01

    Identifying ways to efficiently maximize the response rate to surveys is important to survey-based research. However, evidence on the response rate effect of donation incentives and especially altruistic and egotistic-type text appeal interventions is sparse and ambiguous. By a randomized survey...... experiment among 6,162 members of an online survey panel, this article shows how low-cost incentives and cost-free text appeal interventions may impact the survey response rate in online panels. The experimental treatments comprise (a) a cash prize lottery incentive, (b) two donation incentives equating...... survey response with a monetary donation to a good cause, (c) an egotistic-type text appeal, and (d) an altruistic-type text appeal. Relative to a control group, we find higher response rates among the recipients of the egotistic-type text appeal and the lottery incentive. Donation incentives yield lower...

  2. 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.

  3. Advanced Computational Modeling Approaches for Shock Response Prediction

    Science.gov (United States)

    Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

    2015-01-01

    Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.

  4. Analytical predictions of SGEMP response and comparisons with computer calculations

    International Nuclear Information System (INIS)

    de Plomb, E.P.

    1976-01-01

    An analytical formulation for the prediction of SGEMP surface current response is presented. Only two independent dimensionless parameters are required to predict the peak magnitude and rise time of SGEMP induced surface currents. The analysis applies to limited (high fluence) emission as well as unlimited (low fluence) emission. Cause-effect relationships for SGEMP response are treated quantitatively, and yield simple power law dependencies between several physical variables. Analytical predictions for a large matrix of SGEMP cases are compared with an array of about thirty-five computer solutions of similar SGEMP problems, which were collected from three independent research groups. The theoretical solutions generally agree with the computer solutions as well as the computer solutions agree with one another. Such comparisons typically show variations less than a ''factor of two.''

  5. 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

  6. Angiotensinogen and HLA class II predict bevacizumab response in recurrent glioblastoma patients

    DEFF Research Database (Denmark)

    Urup, Thomas; Michaelsen, Signe Regner; Olsen, Lars Rønn

    2016-01-01

    Background: Bevacizumab combination therapy is among the most frequently used treatments in recurrent glioblastoma and patients who achieve response to bevacizumab have improved survival as well as quality of life. Accordingly, the aim of this study was to identify predictive biomarkers for bevac......Background: Bevacizumab combination therapy is among the most frequently used treatments in recurrent glioblastoma and patients who achieve response to bevacizumab have improved survival as well as quality of life. Accordingly, the aim of this study was to identify predictive biomarkers...... for bevacizumab response in recurrent glioblastoma patients. Methods: The study included a total of 82 recurrent glioblastoma patients treated with bevacizumab combination therapy whom were both response and biomarker evaluable. Gene expression of tumor tissue was analyzed by using a customized Nano...

  7. The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors.

    Directory of Open Access Journals (Sweden)

    Peter R Murphy

    Full Text Available Reaction time (RT is commonly observed to slow down after an error. This post-error slowing (PES has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES.

  8. Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

    OpenAIRE

    Marasco, Addolorata; De Paris, Alessandro; Migliore, Michele

    2016-01-01

    The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific o...

  9. 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

  10. Prediction of permeability changes in an excavation response zone

    International Nuclear Information System (INIS)

    Kinoshita, Naoto; Ishii, Takashi; Kuroda, Hidetaka; Tada, Hiroyuki

    1992-01-01

    In geologic disposal of radioactive wastes, stress changes due to cavern excavation may expand the existing fractures and create possible bypasses for groundwater. This paper proposes a simple method for predicting permeability changes in the excavation response zones. Numerical analyses using this method predict that the response zones created by cavern excavation would differ greatly in thickness and permeability depending on the depth of the cavern site and the initial in-situ stress, that when the cavern site is deeper, response zones would expand more and permeability would increases more, and that if the ratio of horizontal to vertical in-situ stress is small, extensive permeable zones at the crown and the bottom would occur, whereas if the ratio is large, extensive permeable zones would occur in the side walls. (orig.)

  11. Attachment predicts cortisol response and closeness in dyadic social interaction.

    Science.gov (United States)

    Ketay, Sarah; Beck, Lindsey A

    2017-06-01

    The present study examined how the interplay of partners' attachment styles influences cortisol response, actual closeness, and desired closeness during friendship initiation. Participants provided salivary cortisol samples at four timepoints throughout either a high or low closeness task that facilitated high or low levels of self-disclosure with a potential friend (i.e., another same-sex participant). Levels of actual closeness and desired closeness following the task were measured via inclusion of other in the self. Results from multi-level modeling indicated that the interaction of both participants' attachment avoidance predicted cortisol response patterns, with participants showing the highest cortisol response when there was a mismatch between their own and their partners' attachment avoidance. Further, the interaction between both participants' attachment anxiety predicted actual closeness and desired closeness, with participants both feeling and wanting the most closeness with partners when both they and their partners were low in attachment anxiety. Copyright © 2017 Elsevier Ltd. All rights reserved.

  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. 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.

  14. 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

  15. 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

  16. 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)

  17. 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

  18. 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.

  19. 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.

  20. Dopamine reward prediction error responses reflect marginal utility.

    Science.gov (United States)

    Stauffer, William R; Lak, Armin; Schultz, Wolfram

    2014-11-03

    Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  1. 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.

  2. 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.

  3. 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...

  4. Mothers' labeling responses to infants' gestures predict vocabulary outcomes.

    Science.gov (United States)

    Olson, Janet; Masur, Elise Frank

    2015-11-01

    Twenty-nine infants aged 1;1 and their mothers were videotaped while interacting with toys for 18 minutes. Six experimental stimuli were presented to elicit infant communicative bids in two communicative intent contexts - proto-declarative and proto-imperative. Mothers' verbal responses to infants' gestural and non-gestural communicative bids were coded for object and action labels. Relations between maternal labeling responses and infants' vocabularies at 1;1 and 1;5 were examined. Mothers' labeling responses to infants' gestural communicative bids were concurrently and predictively related to infants' vocabularies, whereas responses to non-gestural communicative bids were not. Mothers' object labeling following gestures in the proto-declarative context mediated the association from infants' gesturing in the proto-declarative context to concurrent noun lexicons and was the strongest predictor of subsequent noun lexicons. Mothers' action labeling after infants' gestural bids in the proto-imperative context predicted infants' acquisition of action words at 1;5. Findings show that mothers' responsive labeling explain specific relations between infants' gestures and their vocabulary development.

  5. Predicting the response of olfactory sensory neurons to odor mixtures from single odor response

    Science.gov (United States)

    Marasco, Addolorata; de Paris, Alessandro; Migliore, Michele

    2016-04-01

    The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

  6. Predictive coding of music--brain responses to rhythmic incongruity.

    Science.gov (United States)

    Vuust, Peter; Ostergaard, Leif; Pallesen, Karen Johanne; Bailey, Christopher; Roepstorff, Andreas

    2009-01-01

    During the last decades, models of music processing in the brain have mainly discussed the specificity of brain modules involved in processing different musical components. We argue that predictive coding offers an explanatory framework for functional integration in musical processing. Further, we provide empirical evidence for such a network in the analysis of event-related MEG-components to rhythmic incongruence in the context of strong metric anticipation. This is seen in a mismatch negativity (MMNm) and a subsequent P3am component, which have the properties of an error term and a subsequent evaluation in a predictive coding framework. There were both quantitative and qualitative differences in the evoked responses in expert jazz musicians compared with rhythmically unskilled non-musicians. We propose that these differences trace a functional adaptation and/or a genetic pre-disposition in experts which allows for a more precise rhythmic prediction.

  7. 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.

  8. 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

  9. 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.

  10. Improving Treatment Response for Paediatric Anxiety Disorders

    DEFF Research Database (Denmark)

    Ege, Sarah; Reinholdt-Dunne, Marie Louise

    2016-01-01

    Cognitive behavioural therapy (CBT) is considered the treatment of choice for paediatric anxiety disorders, yet there remains substantial room for improvement in treatment outcomes. This paper examines whether theory and research into the role of information-processing in the underlying psychopat......Cognitive behavioural therapy (CBT) is considered the treatment of choice for paediatric anxiety disorders, yet there remains substantial room for improvement in treatment outcomes. This paper examines whether theory and research into the role of information-processing in the underlying...... interpretational biases, evidence regarding the effects of CBT on attentional biases is mixed. Novel treatment methods including attention bias modification training, attention feedback awareness and control training, and mindfulness-based therapy may hold potential in targeting attentional biases, and thereby...

  11. Can quantitative sensory testing predict responses to analgesic treatment?

    Science.gov (United States)

    Grosen, K; Fischer, I W D; Olesen, A E; Drewes, A M

    2013-10-01

    The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980-2013). Additional studies were identified by chain searching. Search terms included 'quantitative sensory testing', 'sensory testing' and 'analgesics'. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm. © 2013 European Federation of International Association for the Study of Pain Chapters.

  12. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  13. Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

    Science.gov (United States)

    Baker, Christopher M; Gordon, Ascelin; Bode, Michael

    2017-04-01

    Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.

  14. OMG Earthquake! Can Twitter improve earthquake response?

    Science.gov (United States)

    Earle, P. S.; Guy, M.; Ostrum, C.; Horvath, S.; Buckmaster, R. A.

    2009-12-01

    The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public, text messages, can augment its earthquake response products and the delivery of hazard information. The goal is to gather near real-time, earthquake-related messages (tweets) and provide geo-located earthquake detections and rough maps of the corresponding felt areas. Twitter and other social Internet technologies are providing the general public with anecdotal earthquake hazard information before scientific information has been published from authoritative sources. People local to an event often publish information within seconds via these technologies. In contrast, depending on the location of the earthquake, scientific alerts take between 2 to 20 minutes. Examining the tweets following the March 30, 2009, M4.3 Morgan Hill earthquake shows it is possible (in some cases) to rapidly detect and map the felt area of an earthquake using Twitter responses. Within a minute of the earthquake, the frequency of “earthquake” tweets rose above the background level of less than 1 per hour to about 150 per minute. Using the tweets submitted in the first minute, a rough map of the felt area can be obtained by plotting the tweet locations. Mapping the tweets from the first six minutes shows observations extending from Monterey to Sacramento, similar to the perceived shaking region mapped by the USGS “Did You Feel It” system. The tweets submitted after the earthquake also provided (very) short first-impression narratives from people who experienced the shaking. Accurately assessing the potential and robustness of a Twitter-based system is difficult because only tweets spanning the previous seven days can be searched, making a historical study impossible. We have, however, been archiving tweets for several months, and it is clear that significant limitations do exist. The main drawback is the lack of quantitative information

  15. Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively.

    Science.gov (United States)

    Lahner, D; Kabon, B; Marschalek, C; Chiari, A; Pestel, G; Kaider, A; Fleischmann, E; Hetz, H

    2009-09-01

    Fluid management guided by oesophageal Doppler monitor has been reported to improve perioperative outcome. Stroke volume variation (SVV) is considered a reliable clinical predictor of fluid responsiveness. Consequently, the aim of the present trial was to evaluate the accuracy of SVV determined by arterial pulse contour (APCO) analysis, using the FloTrac/Vigileo system, to predict fluid responsiveness as measured by the oesophageal Doppler. Patients undergoing major abdominal surgery received intraoperative fluid management guided by oesophageal Doppler monitoring. Fluid boluses of 250 ml each were administered in case of a decrease in corrected flow time (FTc) to 10%. The ability of SVV to predict fluid responsiveness was assessed by calculation of the area under the receiver operating characteristic (ROC) curve. Twenty patients received 67 fluid boluses. Fifty-two of the 67 fluid boluses administered resulted in fluid responsiveness. SVV achieved an area under the ROC curve of 0.512 [confidence interval (CI) 0.32-0.70]. A cut-off point for fluid responsiveness was found for SVV > or =8.5% (sensitivity: 77%; specificity: 43%; positive predictive value: 84%; and negative predictive value: 33%). This prospective, interventional observer-blinded study demonstrates that SVV obtained by APCO, using the FloTrac/Vigileo system, is not a reliable predictor of fluid responsiveness in the setting of major abdominal surgery.

  16. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  17. 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.

  18. Footbridge Response Predictions and Their Sensitivity to Stochastic Load Assumptions

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2011-01-01

    Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency, pedestr......Knowledge about footbridges response to actions of walking is important in assessments of vibration serviceability. In a number of design codes for footbridges, the vibration serviceability limit state is assessed using a walking load model in which the walking parameters (step frequency...... of pedestrians for predicting footbridge response, which is meaningful, and a step forward. Modelling walking parameters stochastically, however, requires decisions to be made in terms of their statistical distribution and the parameters describing the statistical distribution. The paper investigates...... the sensitivity of results of computations of bridge response to some of the decisions to be made in this respect. This is a useful approach placing focus on which decisions (and which information) are important for sound estimation of bridge response. The studies involve estimating footbridge responses using...

  19. 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.

  20. Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

    Science.gov (United States)

    Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

    2014-01-01

    Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of

  1. 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.

  2. 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.

  3. Integrating environmental and genetic effects to predict responses of tree populations to climate.

    Science.gov (United States)

    Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

    2010-01-01

    Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

  4. Expression of estrogen-related gene markers in breast cancer tissue predicts aromatase inhibitor responsiveness.

    Directory of Open Access Journals (Sweden)

    Irene Moy

    Full Text Available Aromatase inhibitors (AIs are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC. Messenger RNA (mRNA levels of 5 estrogen-related genes-AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR-were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy-defined as the combined complete response, partial response, and stable disease rates for at least 6 months-was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36% and identical sensitivity (96% to the current clinical practice (ERα/PR-IHC. In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection.

  5. Predictive and prognostic factors associated with soft tissue sarcoma response to chemotherapy

    DEFF Research Database (Denmark)

    Young, Robin J; Litière, Saskia; Lia, Michela

    2017-01-01

    BACKGROUND: The European Organization for Research and Treatment of Cancer (EORTC) 62012 study was a Phase III trial of doxorubicin versus doxorubicin-ifosfamide chemotherapy in 455 patients with advanced soft tissue sarcoma (STS). Analysis of the main study showed that combination chemotherapy...... improved tumor response and progression-free survival, but differences in overall survival (OS) were not statistically significant. We analyzed factors prognostic for tumor response and OS, and assessed histological subgroup and tumor grade as predictive factors to identify patients more likely to benefit...... patients had improved tumor response compared to other histological subgroups, whilst patients with metastases other than lung, liver or bone had a poorer response [odds ratio (OR) 0.42, 95% confidence interval (CI) 0.23-0.78; p = 0.006]. Patients with bone metastases had reduced OS [hazard ratio (HR) 1...

  6. Children's biological responsivity to acute stress predicts concurrent cognitive performance.

    Science.gov (United States)

    Roos, Leslie E; Beauchamp, Kathryn G; Giuliano, Ryan; Zalewski, Maureen; Kim, Hyoun K; Fisher, Philip A

    2018-04-10

    Although prior research has characterized stress system reactivity (i.e. hypothalamic-pituitary-adrenal axis, HPAA; autonomic nervous system, ANS) in children, it has yet to examine the extent to which biological reactivity predicts concurrent goal-directed behavior. Here, we employed a stressor paradigm that allowed concurrent assessment of both stress system reactivity and performance on a speeded-response task to investigate the links between biological reactivity and cognitive function under stress. We further investigated gender as a moderator given previous research suggesting that the ANS may be particularly predictive of behavior in males due to gender differences in socialization. In a sociodemographically diverse sample of young children (N = 58, M age = 5.38 yrs; 44% male), individual differences in sociodemographic covariates (age, household income), HPAA (i.e. cortisol), and ANS (i.e. respiratory sinus arrhythmia, RSA, indexing the parasympathetic branch; pre-ejection period, PEP, indexing the sympathetic branch) function were assessed as predictors of cognitive performance under stress. We hypothesized that higher income, older age, and greater cortisol reactivity would be associated with better performance overall, and flexible ANS responsivity (i.e. RSA withdrawal, PEP shortening) would be predictive of performance for males. Overall, females performed better than males. Two-group SEM analyses suggest that, for males, greater RSA withdrawal to the stressor was associated with better performance, while for females, older age, higher income, and greater cortisol reactivity were associated with better performance. Results highlight the relevance of stress system reactivity to cognitive performance under stress. Future research is needed to further elucidate for whom and in what situations biological reactivity predicts goal-directed behavior.

  7. Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment F-18-FDG PET/CT Imaging

    NARCIS (Netherlands)

    Beukinga, Roelof J.; Hulshoff, Jan B.; van Dijk, Lisanne V.; Muijs, Christina T.; Burgerhof, Johannes G. M.; Kats-Ugurlu, Gursah; Slart, Riemer H. J. A.; Slump, Cornelis H.; Mul, Veronique E. M.; Plukker, John Th. M.

    Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUVmax in F-18-FDG PET/ CT imaging. To improve the prediction of

  8. 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.

  9. Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

    Science.gov (United States)

    Dang-Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O'Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean-Philippe

    2017-11-01

    While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to

  10. 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.

  11. 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.

  12. 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.

  13. Assays for predicting and monitoring responses to lung cancer immunotherapy

    International Nuclear Information System (INIS)

    Teixidó, Cristina; Karachaliou, Niki; González-Cao, Maria; Morales-Espinosa, Daniela; Rosell, Rafael

    2015-01-01

    Immunotherapy has become a key strategy for cancer treatment, and two immune checkpoints, namely, programmed cell death 1 (PD-1) and its ligand (PD-L1), have recently emerged as important targets. The interaction blockade of PD-1 and PD-L1 demonstrated promising activity and antitumor efficacy in early phase clinical trials for advanced solid tumors such as non-small cell lung cancer (NSCLC). Many cell types in multiple tissues express PD-L1 as well as several tumor types, thereby suggesting that the ligand may play important roles in inhibiting immune responses throughout the body. Therefore, PD-L1 is a critical immunomodulating component within the lung microenvironment, but the correlation between PD-L1 expression and prognosis is controversial. More evidence is required to support the use of PD-L1 as a potential predictive biomarker. Clinical trials have measured PD-L1 in tumor tissues by immunohistochemistry (IHC) with different antibodies, but the assessment of PD-L1 is not yet standardized. Some commercial antibodies lack specificity and their reproducibility has not been fully evaluated. Further studies are required to clarify the optimal IHC assay as well as to predict and monitor the immune responses of the PD-1/PD-L1 pathway

  14. Texture analysis on MR images helps predicting non-response to NAC in breast cancer

    International Nuclear Information System (INIS)

    Michoux, N.; Van den Broeck, S.; Lacoste, L.; Fellah, L.; Galant, C.; Berlière, M.; Leconte, I.

    2015-01-01

    To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model

  15. Stress responsiveness predicts individual variation in mate selectivity.

    Science.gov (United States)

    Vitousek, Maren N; Romero, L Michael

    2013-06-15

    Steroid hormones, including glucocorticoids, mediate a variety of behavioral and physiological processes. Circulating hormone concentrations vary substantially within populations, and although hormone titers predict reproductive success in several species, little is known about how individual variation in circulating hormone concentrations is linked with most reproductive behaviors in free-living organisms. Mate choice is an important and often costly component of reproduction that also varies substantially within populations. We examined whether energetically costly mate selection behavior in female Galápagos marine iguanas (Amblyrhynchus cristatus) was associated with individual variation in the concentrations of hormones previously shown to differ between reproductive and non-reproductive females during the breeding season (corticosterone and testosterone). Stress-induced corticosterone levels - which are suppressed in female marine iguanas during reproduction - were individually repeatable throughout the seven-week breeding period. Mate selectivity was strongly predicted by individual variation in stress-induced corticosterone: reproductive females that secreted less corticosterone in response to a standardized stressor assessed more displaying males. Neither baseline corticosterone nor testosterone predicted variation in mate selectivity. Scaled body mass was not significantly associated with mate selectivity, but females that began the breeding period in lower body condition showed a trend towards being less selective about potential mates. These results provide the first evidence that individual variation in the corticosterone stress response is associated with how selective females are in their choice of a mate, an important contributor to fitness in many species. Future research is needed to determine the functional basis of this association, and whether transient acute increases in circulating corticosterone directly mediate mate choice behaviors

  16. Dissociation predicts poor response to Dialectial Behavioral Therapy in female patients with Borderline Personality Disorder.

    Science.gov (United States)

    Kleindienst, Nikolaus; Limberger, Matthias F; Ebner-Priemer, Ulrich W; Keibel-Mauchnik, Jana; Dyer, Anne; Berger, Mathias; Schmahl, Christian; Bohus, Martin

    2011-08-01

    A substantial proportion of Borderline Personality Disorder (BPD) patients respond by a marked decrease of psychopathology when treated with Dialectical Behavioral Therapy (DBT). To further enhance the rate of DBT-response, it is useful to identify characteristics related to unsatisfactory response. As DBT relies on emotional learning, we explored whether dissociation-which is known to interfere with learning- predicts poor response to DBT. Fifty-seven Borderline Personality Disorder (BPD) patients (DSM-IV) were prospectively observed during a three-month inpatient DBT program. Pre-post improvements in general psychopathology (SCL-90-R) were predicted from baseline scores of the Dissociative Experiences Scale (DES) by regression models accounting for baseline psychopathology. High DES-scores were related to poor pre-post improvement (β = -0.017 ± 0.006, p = 0.008). The data yielded no evidence that some facets of dissociation are more important in predicting DBT-response than others. The results suggest that dissociation in borderline-patients should be closely monitored and targeted during DBT. At this stage, research on treatment of dissociation (e.g., specific skills training) is warranted.

  17. 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.

  18. Prediction of placebo responses: A systematic review of the literature

    Directory of Open Access Journals (Sweden)

    Bjoern eHoring

    2014-10-01

    Full Text Available Objective: Predicting who responds to placebo treatment – and under which circumstances – has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities and in PubMed (for pain only. Articles were selected when a they assessed putative predictors prior to placebo treatment and b an adequate control group was included when the association of predictors and PRs were analyzed. Results: Twenty-one studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism. Other predictors involved behavioural control (desire for control, eating restraint, personality variables (fun seeking, sensation seeking, neuroticism, biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism. Finally, suggestibility and beliefs in expectation biases, body consciousness and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or the expectation of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted.

  19. Improving response inhibition systems in frontotemporal dementia with citalopram.

    Science.gov (United States)

    Hughes, Laura E; Rittman, Timothy; Regenthal, Ralf; Robbins, Trevor W; Rowe, James B

    2015-07-01

    Disinhibition is a cardinal feature of the behavioural variant of frontotemporal dementia, presenting as impulsive and impetuous behaviours that are often difficult to manage. The options for symptomatic treatments are limited, but a potential target for therapy is the restoration of serotonergic function, which is both deficient in behavioural variant frontotemporal dementia and closely associated with inhibitory control. Based on preclinical studies and psychopharmacological interventions in other disorders, we predicted that inhibition would be associated with the right inferior frontal gyrus and dependent on serotonin. Using magnetoencephalography and electroencephalography of a Go-NoGo paradigm, we investigated the neural basis of behavioural disinhibition in behavioural variant frontotemporal dementia and the effect of selective serotonin reuptake inhibition on the neural systems for response inhibition. In a randomized double-blinded placebo-controlled crossover design study, 12 patients received either a single 30 mg dose of citalopram or placebo. Twenty age-matched healthy controls underwent the same magnetoencephalography/electroencephalography protocol on one session without citalopram, providing normative data for this task. In the control group, successful NoGo trials evoked two established indices of successful response inhibition: the NoGo-N2 and NoGo-P3. Both of these components were significantly attenuated by behavioural variant frontotemporal dementia. Cortical sources associated with successful inhibition in control subjects were identified in the right inferior frontal gyrus and anterior temporal lobe, which have been strongly associated with behavioural inhibition in imaging and lesion studies. These sources were impaired by behavioural variant frontotemporal dementia. Critically, citalopram enhanced the NoGo-P3 signal in patients, relative to placebo treatment, and increased the evoked response in the right inferior frontal gyrus. Voxel

  20. 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.

  1. 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.

  2. Nuclear medicine imaging to predict response to radiotherapy: a review

    International Nuclear Information System (INIS)

    Wiele, Christophe van de; Lahorte, Christophe; Oyen, Wim; Boerman, Otto; Goethals, Ingeborg; Slegers, Guido; Dierckx, Rudi Andre

    2003-01-01

    Purpose: To review available literature on positron emission tomography (PET) and single photon emission computerized tomography (SPECT) for the measurement of tumor metabolism, hypoxia, growth factor receptor expression, and apoptosis as predictors of response to radiotherapy. Methods and Materials: Medical literature databases (Pubmed, Medline) were screened for available literature and critically analyzed as to their scientific relevance. Results: Studies on 18 F-fluorodeoxyglucose PET as a predictor of response to radiotherapy in head-and-neck carcinoma are promising but need confirmation in larger series. 18 F-fluorothymine is stable in human plasma, and preliminary clinical data obtained with this marker of tumor cell proliferation are promising. For imaging tumor hypoxia, novel, more widely available radiopharmaceuticals with faster pharmacokinetics are mandatory. Imaging of ongoing apoptosis and growth factor expression is at a very early stage, but results obtained in other domains with radiolabeled peptides appear promising. Finally, for most of the tracers discussed, validation against a gold standard is needed. Conclusion: Optimization of the pharmacokinetics of relevant radiopharmaceuticals as well as validation against gold-standard tests in large patient series are mandatory if PET and SPECT are to be implemented in routine clinical practice for the purpose of predicting response to radiotherapy

  3. Birth weight predicted baseline muscular efficiency, but not response of energy expenditure to calorie restriction: An empirical test of the predictive adaptive response hypothesis.

    Science.gov (United States)

    Workman, Megan; Baker, Jack; Lancaster, Jane B; Mermier, Christine; Alcock, Joe

    2016-07-01

    Aiming to test the evolutionary significance of relationships linking prenatal growth conditions to adult phenotypes, this study examined whether birth size predicts energetic savings during fasting. We specifically tested a Predictive Adaptive Response (PAR) model that predicts greater energetic saving among adults who were born small. Data were collected from a convenience sample of young adults living in Albuquerque, NM (n = 34). Indirect calorimetry quantified changes in resting energy expenditure (REE) and active muscular efficiency that occurred in response to a 29-h fast. Multiple regression analyses linked birth weight to baseline and postfast metabolic values while controlling for appropriate confounders (e.g., sex, body mass). Birth weight did not moderate the relationship between body size and energy expenditure, nor did it predict the magnitude change in REE or muscular efficiency observed from baseline to after fasting. Alternative indicators of birth size were also examined (e.g., low v. normal birth weight, comparison of tertiles), with no effects found. However, baseline muscular efficiency improved by 1.1% per 725 g (S.D.) increase in birth weight (P = 0.037). Birth size did not influence the sensitivity of metabolic demands to fasting-neither at rest nor during activity. Moreover, small birth size predicted a reduction in the efficiency with which muscles convert energy expended into work accomplished. These results do not support the ascription of adaptive function to phenotypes associated with small birth size. © 2015 Wiley Periodicals, Inc. Am. J. Hum. Biol. 28:484-492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  4. Can biomass responses to warming at plant to ecosystem levels be predicted by leaf-level responses?

    Science.gov (United States)

    Xia, J.; Shao, J.; Zhou, X.; Yan, W.; Lu, M.

    2015-12-01

    Global warming has the profound impacts on terrestrial C processes from leaf to ecosystem scales, potentially feeding back to climate dynamics. Although numerous studies had investigated the effects of warming on C processes from leaf to plant and ecosystem levels, how leaf-level responses to warming scale up to biomass responses at plant, population, and community levels are largely unknown. In this study, we compiled a dataset from 468 papers at 300 experimental sites and synthesized the warming effects on leaf-level parameters, and plant, population and ecosystem biomass. Our results showed that responses of plant biomass to warming mainly resulted from the changed leaf area rather than the altered photosynthetic capacity. The response of ecosystem biomass to warming was weaker than those of leaf area and plant biomass. However, the scaling functions from responses of leaf area to plant biomass to warming were different in diverse forest types, but functions were similar in non-forested biomes. In addition, it is challenging to scale the biomass responses from plant up to ecosystem. These results indicated that leaf area might be the appropriate index for plant biomass response to warming, and the interspecific competition might hamper the scaling of the warming effects on plant and ecosystem levels, suggesting that the acclimation capacity of plant community should be incorporated into land surface models to improve the prediction of climate-C cycle feedback.

  5. 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.)

  6. Neural responses to exclusion predict susceptibility to social influence.

    Science.gov (United States)

    Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

    2014-05-01

    Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

  7. Prediction of spectral acceleration response ordinates based on PGA attenuation

    Science.gov (United States)

    Graizer, V.; Kalkan, E.

    2009-01-01

    Developed herein is a new peak ground acceleration (PGA)-based predictive model for 5% damped pseudospectral acceleration (SA) ordinates of free-field horizontal component of ground motion from shallow-crustal earthquakes. The predictive model of ground motion spectral shape (i.e., normalized spectrum) is generated as a continuous function of few parameters. The proposed model eliminates the classical exhausted matrix of estimator coefficients, and provides significant ease in its implementation. It is structured on the Next Generation Attenuation (NGA) database with a number of additions from recent Californian events including 2003 San Simeon and 2004 Parkfield earthquakes. A unique feature of the model is its new functional form explicitly integrating PGA as a scaling factor. The spectral shape model is parameterized within an approximation function using moment magnitude, closest distance to the fault (fault distance) and VS30 (average shear-wave velocity in the upper 30 m) as independent variables. Mean values of its estimator coefficients were computed by fitting an approximation function to spectral shape of each record using robust nonlinear optimization. Proposed spectral shape model is independent of the PGA attenuation, allowing utilization of various PGA attenuation relations to estimate the response spectrum of earthquake recordings.

  8. 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.

  9. Estimating confidence intervals in predicted responses for oscillatory biological models.

    Science.gov (United States)

    St John, Peter C; Doyle, Francis J

    2013-07-29

    The dynamics of gene regulation play a crucial role in a cellular control: allowing the cell to express the right proteins to meet changing needs. Some needs, such as correctly anticipating the day-night cycle, require complicated oscillatory features. In the analysis of gene regulatory networks, mathematical models are frequently used to understand how a network's structure enables it to respond appropriately to external inputs. These models typically consist of a set of ordinary differential equations, describing a network of biochemical reactions, and unknown kinetic parameters, chosen such that the model best captures experimental data. However, since a model's parameter values are uncertain, and since dynamic responses to inputs are highly parameter-dependent, it is difficult to assess the confidence associated with these in silico predictions. In particular, models with complex dynamics - such as oscillations - must be fit with computationally expensive global optimization routines, and cannot take advantage of existing measures of identifiability. Despite their difficulty to model mathematically, limit cycle oscillations play a key role in many biological processes, including cell cycling, metabolism, neuron firing, and circadian rhythms. In this study, we employ an efficient parameter estimation technique to enable a bootstrap uncertainty analysis for limit cycle models. Since the primary role of systems biology models is the insight they provide on responses to rate perturbations, we extend our uncertainty analysis to include first order sensitivity coefficients. Using a literature model of circadian rhythms, we show how predictive precision is degraded with decreasing sample points and increasing relative error. Additionally, we show how this method can be used for model discrimination by comparing the output identifiability of two candidate model structures to published literature data. Our method permits modellers of oscillatory systems to confidently

  10. 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.

  11. 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

  12. 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.

  13. Predicting photosynthesis and transpiration responses to ozone: decoupling modeled photosynthesis and stomatal conductance

    Directory of Open Access Journals (Sweden)

    D. Lombardozzi

    2012-08-01

    Full Text Available Plants exchange greenhouse gases carbon dioxide and water with the atmosphere through the processes of photosynthesis and transpiration, making them essential in climate regulation. Carbon dioxide and water exchange are typically coupled through the control of stomatal conductance, and the parameterization in many models often predict conductance based on photosynthesis values. Some environmental conditions, like exposure to high ozone (O3 concentrations, alter photosynthesis independent of stomatal conductance, so models that couple these processes cannot accurately predict both. The goals of this study were to test direct and indirect photosynthesis and stomatal conductance modifications based on O3 damage to tulip poplar (Liriodendron tulipifera in a coupled Farquhar/Ball-Berry model. The same modifications were then tested in the Community Land Model (CLM to determine the impacts on gross primary productivity (GPP and transpiration at a constant O3 concentration of 100 parts per billion (ppb. Modifying the Vcmax parameter and directly modifying stomatal conductance best predicts photosynthesis and stomatal conductance responses to chronic O3 over a range of environmental conditions. On a global scale, directly modifying conductance reduces the effect of O3 on both transpiration and GPP compared to indirectly modifying conductance, particularly in the tropics. The results of this study suggest that independently modifying stomatal conductance can improve the ability of models to predict hydrologic cycling, and therefore improve future climate predictions.

  14. Consumer factors predicting level of treatment response to illness management and recovery.

    Science.gov (United States)

    White, Dominique A; McGuire, Alan B; Luther, Lauren; Anderson, Adrienne I; Phalen, Peter; McGrew, John H

    2017-12-01

    This study aims to identify consumer-level predictors of level of treatment response to illness management and recovery (IMR) to target the appropriate consumers and aid psychiatric rehabilitation settings in developing intervention adaptations. Secondary analyses from a multisite study of IMR were conducted. Self-report data from consumer participants of the parent study (n = 236) were analyzed for the current study. Consumers completed prepost surveys assessing illness management, coping, goal-related hope, social support, medication adherence, and working alliance. Correlations and multiple regression analyses were run to identify self-report variables that predicted level of treatment response to IMR. Analyses revealed that goal-related hope significantly predicted level of improved illness self-management, F(1, 164) = 10.93, p consumer-level predictors of level of treatment response have not been explored for IMR. Although 2 significant predictors were identified, study findings suggest more work is needed. Future research is needed to identify additional consumer-level factors predictive of IMR treatment response in order to identify who would benefit most from this treatment program. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  15. Self-responsibility predicts the successful outcome of coronary artery bypass surgery

    Directory of Open Access Journals (Sweden)

    C. J. Eales

    2004-01-01

    and their spouses/care-givers had a greater knowledge about the disease and the risk factor modification (p=0.01; p<0.01, and twelve months after the operation the patients are satisfied with the outcome of the operation (p<0.01. Conclusions: A stepwise logistic regression established that the acceptance of self-responsibility was the strongest  factor predicting an improved quality of life after CABG surgery. Patients who did not accept responsibility did not have an improved quality of life irrespective of the impact of all other parameters. Patients' satisfaction with the outcome of the operative procedure is an important predictor of the acceptance of self-responsibility. Realistic expectations of the outcome of CABG surgery will improve patients' satisfaction with the outcome. The knowledge of the spouse is a significant factor in the patients' acceptance of self-responsibility. Knowledge of the chronic nature of their disease as well as risk factor modification and realistic expectations of the outcome of CABG surgery influences patientsacceptance of self-responsibility.

  16. 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%).

  17. 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.

  18. 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.

  19. 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.

  20. Stathmin protein level, a potential predictive marker for taxane treatment response in endometrial cancer.

    Directory of Open Access Journals (Sweden)

    Henrica M J Werner

    Full Text Available Stathmin is a prognostic marker in many cancers, including endometrial cancer. Preclinical studies, predominantly in breast cancer, have suggested that stathmin may additionally be a predictive marker for response to paclitaxel. We first evaluated the response to paclitaxel in endometrial cancer cell lines before and after stathmin knock-down. Subsequently we investigated the clinical response to paclitaxel containing chemotherapy in metastatic endometrial cancer in relation to stathmin protein level in tumors. Stathmin level was also determined in metastatic lesions, analyzing changes in biomarker status on disease progression. Knock-down of stathmin improved sensitivity to paclitaxel in endometrial carcinoma cell lines with both naturally higher and lower sensitivity to paclitaxel. In clinical samples, high stathmin level was demonstrated to be associated with poor response to paclitaxel containing chemotherapy and to reduced disease specific survival only in patients treated with such combination. Stathmin level increased significantly from primary to metastatic lesions. This study suggests, supported by both preclinical and clinical data, that stathmin could be a predictive biomarker for response to paclitaxel treatment in endometrial cancer. Re-assessment of stathmin level in metastatic lesions prior to treatment start may be relevant. Also, validation in a randomized clinical trial will be important.

  1. 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.

  2. Seismic Hazard Assessment in Site Evaluation for Nuclear Installations: Ground Motion Prediction Equations and Site Response

    International Nuclear Information System (INIS)

    2016-07-01

    The objective of this publication is to provide the state-of-the-art practice and detailed technical elements related to ground motion evaluation by ground motion prediction equations (GMPEs) and site response in the context of seismic hazard assessments as recommended in IAEA Safety Standards Series No. SSG-9, Seismic Hazards in Site Evaluation for Nuclear Installations. The publication includes the basics of GMPEs, ground motion simulation, selection and adjustment of GMPEs, site characterization, and modelling of site response in order to improve seismic hazard assessment. The text aims at delineating the most important aspects of these topics (including current practices, criticalities and open problems) within a coherent framework. In particular, attention has been devoted to filling conceptual gaps. It is written as a reference text for trained users who are responsible for planning preparatory seismic hazard analyses for siting of all nuclear installations and/or providing constraints for anti-seismic design and retrofitting of existing structures

  3. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Verification and improvement of a predictive model for radionuclide migration

    International Nuclear Information System (INIS)

    Miller, C.W.; Benson, L.V.; Carnahan, C.L.

    1982-01-01

    Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

  5. Improving decision making in crisis response through critical thinking support

    NARCIS (Netherlands)

    Schraagen, Johannes Martinus Cornelis; van de Ven, Josine G.M.

    2008-01-01

    In this study, we describe how to use innovative techniques to improve the decision-making process in crisis response organizations. The focus was on building situation awareness of a crisis and overcoming pitfalls such as tunnel vision and information bias through using critical thinking. We

  6. Improving Completion Rates in Adult Education through Social Responsibility

    Science.gov (United States)

    Wahlgren, Bjarne; Mariager-Anderson, Kristina

    2017-01-01

    Dropout is a serious problem within education. This article reports on an intervention project, titled "New Roles for the Teacher--Increased Completion Rates Through Social Responsibility," which sought to reduce nonattendance and drop-out rates in the Danish adult educational system by improving teachers' competences. This goal was…

  7. Improving Students' Vocabulary Mastery by Using Total Physical Response

    Science.gov (United States)

    Fahrurrozi

    2017-01-01

    This study aims to describe how Total Physical Response improves students' vocabulary learning outcomes at the third-grade elementary school Guntur 03 South Jakarta, Indonesia. This research was conducted in the first semester of the academic year 2015-2016 with the number of students as many as 40 students. The method used in this research is a…

  8. Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form

    Directory of Open Access Journals (Sweden)

    Shira Barzilay

    2018-04-01

    3-factor structure. It demonstrates construct validity for assessing distinct suicide-related countertransference to psychiatric outpatients. Mental health professionals’ emotional responses to their patients are concurrently indicative and prospectively predictive of suicidal thoughts and behaviors. Thus, the TRQ-SF is a useful tool for the study of countertransference in the treatment of suicidal patients and may help clinicians make diagnostic and therapeutic use of their own responses to improve assessment and intervention for individual suicidal patients.

  9. Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form.

    Science.gov (United States)

    Barzilay, Shira; Yaseen, Zimri S; Hawes, Mariah; Gorman, Bernard; Altman, Rachel; Foster, Adriana; Apter, Alan; Rosenfield, Paul; Galynker, Igor

    2018-01-01

    construct validity for assessing distinct suicide-related countertransference to psychiatric outpatients. Mental health professionals' emotional responses to their patients are concurrently indicative and prospectively predictive of suicidal thoughts and behaviors. Thus, the TRQ-SF is a useful tool for the study of countertransference in the treatment of suicidal patients and may help clinicians make diagnostic and therapeutic use of their own responses to improve assessment and intervention for individual suicidal patients.

  10. Improving Marital Prediction: A Model and a Pilot Study.

    Science.gov (United States)

    Dean, Dwight G.; Lucas, Wayne L.

    A model for the prediction of marital adjustment is proposed which presents selected social background factors (e.g., education) and interactive factors (e.g., Bienvenu's Communication scale, Hurvitz' Role Inventory, Dean's Emotional Maturity and Commitment scales, Rosenberg's Self-Esteem scale) in order to account for as much of the variance in…

  11. How Predictive Analytics and Choice Architecture Can Improve Student Success

    Science.gov (United States)

    Denley, Tristan

    2014-01-01

    This article explores the challenges that students face in navigating the curricular structure of post-secondary degree programs, and how predictive analytics and choice architecture can play a role. It examines Degree Compass, a course recommendation system that successfully pairs current students with the courses that best fit their talents and…

  12. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    user

    2010-10-04

    Oct 4, 2010 ... Available online at http://www.academicjournals.org/AJEST ... robust technique for predicting optimal image resolution for the mapping of savannah ecosystems was developed. .... whether to purchase multi-spectral imagery acquired by GeoEye-2 ..... Analysis of the spectral behaviour of the pasture class in.

  13. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    Past studies to predict optimal image resolution required for generating spatial information for savannah ecosystems have yielded different outcomes, hence providing a knowledge gap that was investigated in the present study. The postulation, for the present study, was that by graphically solving two simultaneous ...

  14. Combining disparate data sources for improved poverty prediction and mapping.

    Science.gov (United States)

    Pokhriyal, Neeti; Jacques, Damien Christophe

    2017-11-14

    More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of "Big Data" to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84-0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. Copyright © 2017 the Author(s). Published by PNAS.

  15. 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...

  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...

  17. Selection procedures in sports: Improving predictions of athletes’ future performance

    NARCIS (Netherlands)

    den Hartigh, Jan Rudolf; Niessen, Anna; Frencken, Wouter; Meijer, Rob R.

    The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the

  18. 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.

  19. 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.

  20. The Social Responsibility Performance Outcomes Model: Building Socially Responsible Companies through Performance Improvement Outcomes.

    Science.gov (United States)

    Hatcher, Tim

    2000-01-01

    Considers the role of performance improvement professionals and human resources development professionals in helping organizations realize the ethical and financial power of corporate social responsibility. Explains the social responsibility performance outcomes model, which incorporates the concepts of societal needs and outcomes. (LRW)

  1. 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

  2. Prediction of First-Order Vessel Responses with Applications to Decision Support Systems

    DEFF Research Database (Denmark)

    Nielsen, Ulrik D.; Iseki, Toshio

    2015-01-01

    The paper presents a practical and simple approach for making vessel response predictions. Features of the procedure include a) predictions which are scaled so to better agree with corresponding true, future values to be measured at the time the predictions apply at; and b) predictions that are a...

  3. Improved predictions of nuclear data: A continued challenge in astrophysics

    International Nuclear Information System (INIS)

    Goriely, S.

    2001-01-01

    Although important effort has been devoted in the last decades to measure reaction cross sections and decay half-lives of interest in astrophysics, most of the nuclear astrophysics applications still require the use of theoretical predictions to estimate experimentally unknown rates. The nuclear ingredients to the reaction or weak interaction models should preferentially be estimated from microscopic or semi-microscopic global predictions based on sound and reliable nuclear models which, in turn, can compete with more phenomenological highly-parametrized models in the reproduction of experimental data. The latest developments made in deriving the nuclear inputs of relevance in astrophysics applications are reviewed. It mainly concerns nuclear structure properties (atomic masses, deformations, radii, etc...), nuclear level densities, nucleon and α-optical potentials, γ-ray and Gamow-Teller strength functions

  4. Improved Methods for Pitch Synchronous Linear Prediction Analysis of Speech

    OpenAIRE

    劉, 麗清

    2015-01-01

    Linear prediction (LP) analysis has been applied to speech system over the last few decades. LP technique is well-suited for speech analysis due to its ability to model speech production process approximately. Hence LP analysis has been widely used for speech enhancement, low-bit-rate speech coding in cellular telephony, speech recognition, characteristic parameter extraction (vocal tract resonances frequencies, fundamental frequency called pitch) and so on. However, the performance of the co...

  5. Improving Transit Predictions of Known Exoplanets with TERMS

    Directory of Open Access Journals (Sweden)

    Mahadevan S.

    2011-02-01

    Full Text Available Transiting planet discoveries have largely been restricted to the short-period or low-periastron distance regimes due to the bias inherent in the geometric transit probability. Through the refinement of planetary orbital parameters, and hence reducing the size of transit windows, long-period planets become feasible targets for photometric follow-up. Here we describe the TERMS project that is monitoring these host stars at predicted transit times.

  6. Novel enzymatic assay predicts minoxidil response in the treatment of androgenetic alopecia.

    Science.gov (United States)

    Goren, Andy; Castano, Juan Antonio; McCoy, John; Bermudez, Fernando; Lotti, Torello

    2014-01-01

    Topical minoxidil is the most common drug used for the treatment of androgenetic alopecia (AGA) in men and women. Although topical minoxidil exhibits a good safety profile, the efficacy in the overall population remains relatively low at 30-40%. To observe significant improvement in hair growth, minoxidil is typically used daily for a period of at least 3-4 months. Due to the significant time commitment and low response rate, a biomarker for predicting patient response prior to therapy would be advantageous. Minoxidil is converted in the scalp to its active form, minoxidil sulfate, by the sulfotransferase enzyme SULT1A1. We hypothesized that SULT1A1 enzyme activity in the hair follicle correlates with minoxidil response for the treatment of AGA. Our preliminary retrospective study of a SULT1A1 activity assay demonstrates 95% sensitivity and 73% specificity in predicting minoxidil treatment response for AGA. A larger prospective study is now under way to further validate this novel assay. © 2013 Wiley Periodicals, Inc.

  7. Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis.

    Science.gov (United States)

    Haagen, Joris F G; Ter Heide, F Jackie June; Mooren, Trudy M; Knipscheer, Jeroen W; Kleber, Rolf J

    2017-03-01

    Given the recent peak in refugee numbers and refugees' high odds of developing post-traumatic stress disorder (PTSD), finding ways to alleviate PTSD in refugees is of vital importance. However, there are major differences in PTSD treatment response between refugees, the determinants of which are largely unknown. This study aimed at improving PTSD treatment for adult refugees by identifying PTSD treatment response predictors. A prospective longitudinal multilevel modelling design was used to predict PTSD severity scores over time. We analysed data from a randomized controlled trial with pre-, post-, and follow-up measurements of the safety and efficacy of eye movement desensitization and reprocessing and stabilization in asylum seekers and refugees suffering from PTSD. Lack of refugee status, comorbid depression, demographic, trauma-related and treatment-related variables were analysed as potential predictors of PTSD treatment outcome. Treatment outcome data from 72 participants were used. The presence (B = 6.5, p = .03) and severity (B = 6.3, p disorder predicted poor treatment response and explained 39% of the variance between individuals. Refugee patients who suffer from PTSD and severe comorbid depression benefit less from treatment aimed at alleviating PTSD. Results highlight the need for treatment adaptations for PTSD and comorbid severe depression in traumatized refugees, including testing whether initial targeting of severe depressive symptoms increases PTSD treatment effectiveness. There are differences in post-traumatic stress disorder (PTSD) treatment response between traumatized refugees. Comorbid depressive disorder and depression severity predict poor PTSD response. Refugees with PTSD and severe depression may not benefit from PTSD treatment. Targeting comorbid severe depression before PTSD treatment is warranted. This study did not correct for multiple hypothesis testing. Comorbid depression may differentially impact alternative PTSD treatments

  8. Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments

    Science.gov (United States)

    S. Vicca; M. Bahn; M. Estiarte; E. E. van Loon; R. Vargas; G. Alberti; P. Ambus; M. A. Arain; C. Beier; L. P. Bentley; W. Borken; N. Buchmann; S. L. Collins; G. de Dato; J. S. Dukes; C. Escolar; P. Fay; G. Guidolotti; P. J. Hanson; A. Kahmen; G. Kröel-Dulay; T. Ladreiter-Knauss; K. S. Larsen; E. Lellei-Kovacs; E. Lebrija-Trejos; F. T. Maestre; S. Marhan; M. Marshall; P. Meir; Y. Miao; J. Muhr; P. A. Niklaus; R. Ogaya; J. Peñuelas; C. Poll; L. E. Rustad; K. Savage; A. Schindlbacher; I. K. Schmidt; A. R. Smith; E. D. Sotta; V. Suseela; A. Tietema; N. van Gestel; O. van Straaten; S. Wan; U. Weber; I. A. Janssens

    2014-01-01

    As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends an extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the...

  9. Improving 3D structure prediction from chemical shift data

    Energy Technology Data Exchange (ETDEWEB)

    Schot, Gijs van der [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Zhang, Zaiyong [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany); Vernon, Robert [University of Washington, Department of Biochemistry (United States); Shen, Yang [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States); Vranken, Wim F. [VIB, Department of Structural Biology (Belgium); Baker, David [University of Washington, Department of Biochemistry (United States); Bonvin, Alexandre M. J. J., E-mail: a.m.j.j.bonvin@uu.nl [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Lange, Oliver F., E-mail: oliver.lange@tum.de [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany)

    2013-09-15

    We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50-100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 A RMSD from the reference)

  10. 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

  11. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  12. 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

  13. Predicting predatory impact of juvenile invasive lionfish (Pterois volitans) on a crustacean prey using functional response analysis: effects of temperature, habitat complexity and light regimes

    KAUST Repository

    South, Josie; Dick, Jaimie T. A.; McCard, Monica; Barrios-O’ Neill, Daniel; Anton, Andrea

    2017-01-01

    The ecological implications of biotic interactions, such as predator-prey relationships, are often context-dependent. Comparative functional responses analysis can be used under different abiotic contexts to improve understanding and prediction

  14. Early perception of medication benefit predicts subsequent antipsychotic response in schizophrenia: "the consumer has a point" revisited.

    Science.gov (United States)

    Ascher-Svanum, Haya; Weiden, Peter; Nyhuis, Allen W; Faries, Douglas E; Stauffer, Virginia; Kollack-Walker, Sara; Kinon, Bruce J

    2014-07-01

    An easy-to-administer tool for predicting response to antipsychotic treatment could improve the acute management of patients with schizophrenia. We assessed whether a patient's perception of medication benefit early in treatment could predict subsequent response or nonresponse to continued use of the same treatment. This post hoc analysis used data from a randomized, open-label trial of antipsychotics for treatment of schizophrenia in which attitudes about medication adherence were assessed after two weeks of antipsychotic treatment using the Rating of Medication Influences (ROMI) scale. The analysis included 439 patients who had Positive and Negative Syndrome Scale (PANSS) and ROMI scale data at Weeks 2 and 8. Scores on the ROMI subscale Perceived Medication Benefit factor were used to predict subsequent antipsychotic response at Week 8, defined as a .20% reduction from baseline on the PANSS. Logistic regression was used to identify a cut-off score for the Perceived Medication Benefit factor that could accurately identify antipsychotic responders vs. nonresponders at Week 8. A score of .2.75 (equal to a mean subscale score of .11.00) on the ROMI scale Perceived Medication Benefit factor at Week 2 predicted response at Week 8 with high specificity (72%) and negative predictive value (70%), moderate sensitivity (44%) and positive predictive value (47%), and with a 38% misclassification rate. A brief assessment of the patient's perception of medication benefit at two weeks into treatment appears to be a good predictor of subsequent response and nonresponse after eight weeks of treatment with the same antipsychotic.

  15. PREDICTS: Projecting Responses of Ecological Diversity in Changing Terrestrial Systems

    Directory of Open Access Journals (Sweden)

    Georgina Mace

    2012-12-01

    Full Text Available The PREDICTS project (www.predicts.org.uk is a three-year NERC-funded project to model and predict at a global scale how local terrestrial diversity responds to human pressures such as land use, land cover, pollution, invasive species and infrastructure. PREDICTS is a collaboration between Imperial College London, the UNEP World Conservation Monitoring Centre, Microsoft Research Cambridge, UCL and the University of Sussex. In order to meet its aims, the project relies on extensive data describing the diversity and composition of biological communities at a local scale. Such data are collected on a vast scale through the committed efforts of field ecologists. If you have appropriate data that you would be willing to share with us, please get in touch (enquiries@predicts.org.uk. All contributions will be acknowledged appropriately and all data contributors will be included as co-authors on an open-access paper describing the database.

  16. Improving Student Success Using Predictive Models and Data Visualisations

    Science.gov (United States)

    Essa, Alfred; Ayad, Hanan

    2012-01-01

    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…

  17. Improving the TRIGA facility maintenance by predictive maintenance techniques

    International Nuclear Information System (INIS)

    Preda, M.; Sabau, C.; Barbalata, E.

    1997-01-01

    This work deals with the specific operation of equipment in radioactive environment or in conditions allowing radioactive contamination. The requirements of remote operation ensuring the operators' protection are presented. Also, the requirements of international standards issued by IAEA-Vienna are reviewed. The organizational withdraws of the maintenance activities, based on the standards and maintenance and repair directives still in force, are shown. It is emphasized the fact that this type of maintenance was adequate to a given level of technical development, characteristic for pre-computerized industry, but, at present, it is obsolete and uneconomic both in utilization and maintenance. Such a system constitutes already a burden hindering the efforts of maximizing the availability, maintenance, prolongation the service life of equipment and utilities, finally, of increasing the efficiency of complex installations. Moreover, the predictive maintenance techniques are strongly requested by the character of radioactive installations precluding the direct access in given zones (a potential risk of irradiation or radioactive contamination) of installations during operation. The results obtained by applying the predictive maintenance techniques in the operation of the double circuit irradiation loop, used in the TRIGA reactors, are presented

  18. 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).

  19. 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.

  20. Improvements in the nuclear accident response system in Brazil

    International Nuclear Information System (INIS)

    Estrada, J.J.S.; Azevedo, E.M.; Knofel, T.M.J.; Recio, J.C.A.; Alves, R.N.

    1998-01-01

    The National Commission on Nuclear Energy has been making outstanding effort to improve its nuclear and radiological accident response systems since the tragic accident in Goiania. Most of this effort is related to nuclear area although the radiological accident has been also considered. This paper describes the improvements in the CNEN response system structure, discusses several topics involving those related to emergency planning and preparedness, and points out some deficiencies that need to be corrected also. The situation during the Goiania accident was more disadvantageous than nowadays, so it is believed that none of the actual deficiencies are sufficient to guess that the population and the environment will not be protected in case of a nuclear or radiological accident

  1. Prediction of treatment response and metastatic disease in soft tissue sarcoma

    Science.gov (United States)

    Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

    2014-03-01

    Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis treatment and metastasis.

  2. Improving completion rates in adult education through social responsibility

    DEFF Research Database (Denmark)

    Wahlgren, Bjarne; Mariager-Anderson, Kristina

    2017-01-01

    Dropout is a serious problem within education. This article reports on an intervention project, titled “New Roles for the Teacher—Increased Completion Rates Through Social Responsibility,” which sought to reduce nonattendance and dropout rates in the Danish adult educational system by improving...... of reducing drop-out rates. As a consequence, the teachers acted more consistently and purposefully to prevent dropout, and a positive effect of the intervention on drop-out rates was documented....

  3. 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.

  4. XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

    Science.gov (United States)

    Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

    2014-05-01

    Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be

  5. Improving, characterizing and predicting the lifetime of organic photovoltaics

    DEFF Research Database (Denmark)

    Gevorgyan, Suren A.; Heckler, Ilona Maria; Bundgaard, Eva

    2017-01-01

    This review summarizes the recent progress in the stability and lifetime of organic photovoltaics (OPVs). In particular, recently proposed solutions to failure mechanisms in different layers of the device stack are discussed comprising both structural and chemical modifications. Upscaling...... characterization reported recently. Lifetime testing and determination is another challenge in the field of organic solar cells and the final sections of this review discuss the testing protocols as well as the generic marker for device lifetime and the methodology for comparing all the lifetime landmarks in one...... common diagram. These tools were used to determine the baselines for OPV lifetime tested under different ageing conditions. Finally, the current status of lifetime for organic solar cells is presented and predictions are made for progress in the near future....

  6. Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

    Science.gov (United States)

    Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

    2012-01-01

    In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

  7. Predicting placebo response in adolescents with major depressive disorder: The Adolescent Placebo Impact Composite Score (APICS).

    Science.gov (United States)

    Nakonezny, Paul A; Mayes, Taryn L; Byerly, Matthew J; Emslie, Graham J

    2015-09-01

    The aim of this study was to construct a composite scoring system to predict the probability of placebo response in adolescents with Major Depressive Disorder (MDD). Participants of the current study were 151 adolescents (aged 12-17 years) who were randomized to the placebo arm (placebo transdermal patches) of a randomized controlled trial (RCT) comparing the selegiline transdermal patch with placebo (DelBello et al., 2014). The primary outcome of response was defined as a CGI-I score of 1 or 2 (very much or much improved) at week 12 (study-end) or exit. As a first step, a multiple logistic mixed model was used to estimate the odds of placebo response from each predictor in the model, including age, CDRS-R total at baseline (depressive symptom severity), history of recurrent depression (yes vs. no), sex (female vs. male), and race (non-Caucasian vs. Caucasian). On the basis of the initial logistic mixed model analysis, we then constructed an Adolescent Placebo Impact Composite Score (APICS) that became the sole predictor in a re-specified Bayesian logistic regression model to estimate the probability of placebo response. Finally, the AUC for the APICS was tested against a nominal area of 0.50 to evaluate how well the APICS discriminated placebo response status. Among the 151 adolescents, with a mean age of 14.6 years (SD = 1.6) and a mean baseline CDRS-R total of 60.6 (SD = 12.1), 68.2% were females, 50.3% was Caucasian, and 39.7% had a history of recurrent depression. Placebo response rate was 58.3%. Based on the logistic mixed model, the re-specified equation with the highest discriminatory ability to estimate the probability of placebo response was APICS = age + (0.32 × CDRS-R Total at baseline) + (-2.85 × if female) + (-5.50 × if history of recurrent depression) + (-5.85 × if non-Caucasian). The AUC for this model was 0.59 (p = .049). Within a Bayesian decision-theoretic framework, in 95.5% of the time, the 10,000 posterior Monte Carlo samples suggested

  8. Predicting occurrence of juvenile shark habitat to improve conservation planning.

    Science.gov (United States)

    Oh, Beverly Z L; Sequeira, Ana M M; Meekan, Mark G; Ruppert, Jonathan L W; Meeuwig, Jessica J

    2017-06-01

    Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark-monitoring data on large scales (100s-1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of habitat suitability for juvenile sharks. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490,515 km 2 of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species-level models had higher accuracy (ĸ ≥ 0.69) and deviance explained (≥48%) than our order-level model (ĸ = 0.36 and deviance explained of 10%). Maps of predicted occurrence revealed different species-specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species-focused approach. Highly suitable habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non-extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across

  9. Responsiveness of performance and morphological traits to experimental submergence predicts field distribution pattern of wetland plants

    NARCIS (Netherlands)

    Luo, Fang-Li; Huang, Lin; Lei, Ting; Xue, Wei; Li, Hong-Li; Yu, Fei-Hai; Cornelissen, J.H.C.

    2016-01-01

    Question: Plant trait mean values and trait responsiveness to different environmental regimes are both important determinants of plant field distribution, but the degree to which plant trait means vs trait responsiveness predict plant distribution has rarely been compared quantitatively. Because

  10. Prepotent response inhibition predicts treatment outcome in attention deficit/hyperactivity disorder

    NARCIS (Netherlands)

    van der Oord, S.; Geurts, H.M.; Prins, P.J.M.; Emmelkamp, P.M.G.; Oosterlaan, J.

    2012-01-01

    Objective: Inhibition deficits, including deficits in prepotent response inhibition and interference control, are core deficits in ADHD. The predictive value of prepotent response inhibition and interference control was assessed for outcome in a 10-week treatment trial with methylphenidate. Methods:

  11. Neural response to pictorial health warning labels can predict smoking behavioral change.

    Science.gov (United States)

    Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

    2016-11-01

    In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

  12. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

    Science.gov (United States)

    Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

    2016-08-23

    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

  13. Pharmacogenetics predictive of response and toxicity in acute lymphoblastic leukemia therapy.

    Science.gov (United States)

    Mei, Lin; Ontiveros, Evelena P; Griffiths, Elizabeth A; Thompson, James E; Wang, Eunice S; Wetzler, Meir

    2015-07-01

    Acute lymphoblastic leukemia (ALL) is a relatively rare disease in adults accounting for no more than 20% of all cases of acute leukemia. By contrast with the pediatric population, in whom significant improvements in long term survival and even cure have been achieved over the last 30years, adult ALL remains a significant challenge. Overall survival in this group remains a relatively poor 20-40%. Modern research has focused on improved pharmacokinetics, novel pharmacogenetics and personalized principles to optimize the efficacy of the treatment while reducing toxicity. Here we review the pharmacogenetics of medications used in the management of patients with ALL, including l-asparaginase, glucocorticoids, 6-mercaptopurine, methotrexate, vincristine and tyrosine kinase inhibitors. Incorporating recent pharmacogenetic data, mainly from pediatric ALL, will provide novel perspective of predicting response and toxicity in both pediatric and adult ALL therapies. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

    Science.gov (United States)

    Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

    2015-07-01

    Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

  15. Can brain responses to movie trailers predict success?

    OpenAIRE

    Boksem, Maarten

    2015-01-01

    textabstractDecades of research have shown that much of our mental processing occurs at the subconscious level, including the decisions we make as consumers. These subconscious processes explain why we so often fail to accurately predict our own future choices. Often what we think we want has little or no bearing on the choices we actually make. Now a new study provides the first evidence that brain measures can provide significant added value to models for predicting consumer choice.

  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. A predictive framework to understand forest responses to global change.

    Science.gov (United States)

    McMahon, Sean M; Dietze, Michael C; Hersh, Michelle H; Moran, Emily V; Clark, James S

    2009-04-01

    Forests are one of Earth's critical biomes. They have been shown to respond strongly to many of the drivers that are predicted to change natural systems over this century, including climate, introduced species, and other anthropogenic influences. Predicting how different tree species might respond to this complex of forces remains a daunting challenge for forest ecologists. Yet shifts in species composition and abundance can radically influence hydrological and atmospheric systems, plant and animal ranges, and human populations, making this challenge an important one to address. Forest ecologists have gathered a great deal of data over the past decades and are now using novel quantitative and computational tools to translate those data into predictions about the fate of forests. Here, after a brief review of the threats to forests over the next century, one of the more promising approaches to making ecological predictions is described: using hierarchical Bayesian methods to model forest demography and simulating future forests from those models. This approach captures complex processes, such as seed dispersal and mortality, and incorporates uncertainty due to unknown mechanisms, data problems, and parameter uncertainty. After describing the approach, an example by simulating drought for a southeastern forest is offered. Finally, there is a discussion of how this approach and others need to be cast within a framework of prediction that strives to answer the important questions posed to environmental scientists, but does so with a respect for the challenges inherent in predicting the future of a complex biological system.

  18. Life history theory predicts fish assemblage response to hydrologic regimes.

    Science.gov (United States)

    Mims, Meryl C; Olden, Julian D

    2012-01-01

    The hydrologic regime is regarded as the primary driver of freshwater ecosystems, structuring the physical habitat template, providing connectivity, framing biotic interactions, and ultimately selecting for specific life histories of aquatic organisms. In the present study, we tested ecological theory predicting directional relationships between major dimensions of the flow regime and life history composition of fish assemblages in perennial free-flowing rivers throughout the continental United States. Using long-term discharge records and fish trait and survey data for 109 stream locations, we found that 11 out of 18 relationships (61%) tested between the three life history strategies (opportunistic, periodic, and equilibrium) and six hydrologic metrics (two each describing flow variability, predictability, and seasonality) were statistically significant (P history strategies, with 82% of all significant relationships observed supporting predictions from life history theory. Specifically, we found that (1) opportunistic strategists were positively related to measures of flow variability and negatively related to predictability and seasonality, (2) periodic strategists were positively related to high flow seasonality and negatively related to variability, and (3) the equilibrium strategists were negatively related to flow variability and positively related to predictability. Our study provides important empirical evidence illustrating the value of using life history theory to understand both the patterns and processes by which fish assemblage structure is shaped by adaptation to natural regimes of variability, predictability, and seasonality of critical flow events over broad biogeographic scales.

  19. Pressure integration technique for predicting wind-induced response in high-rise buildings

    Directory of Open Access Journals (Sweden)

    Aly Mousaad Aly

    2013-12-01

    Full Text Available This paper presents a procedure for response prediction in high-rise buildings under wind loads. The procedure is illustrated in an application example of a tall building exposed to both cross-wind and along-wind loads. The responses of the building in the lateral directions combined with torsion are estimated simultaneously. Results show good agreement with recent design standards; however, the proposed procedure has the advantages of accounting for complex mode shapes, non-uniform mass distribution, and interference effects from the surrounding. In addition, the technique allows for the contribution of higher modes. For accurate estimation of the acceleration response, it is important to consider not only the first two lateral vibrational modes, but also higher modes. Ignoring the contribution of higher modes may lead to underestimation of the acceleration response; on the other hand, it could result in overestimation of the displacement response. Furthermore, the procedure presented in this study can help decision makers, involved in a tall building design/retrofit to choose among innovative solutions like aerodynamic mitigation, structural member size adjustment, damping enhancement, and/or materials change, with an objective to improve the resiliency and the serviceability under extreme wind actions.

  20. High-resolution MRI predicts steroid injection response in carpal tunnel syndrome patients

    Energy Technology Data Exchange (ETDEWEB)

    Aoki, Takatoshi; Oki, Hodaka; Kinoshita, Shunsuke; Yamashita, Yoshiko; Takahashi, Hiroyuki; Hayashida, Yoshiko; Korogi, Yukunori [University of Occupational and Environmental Health School of Medicine, Department of Radiology, Kitakyushu (Japan); Oshige, Takahisa; Sakai, Akinori [University of Occupational and Environmental Health School of Medicine, Department of Orthopaedic Surgery, Kitakyushu (Japan); Matsuyama, Atsushi; Hisaoka, Masanori [University of Occupational and Environmental Health School of Medicine, Department of Pathology and Oncology, Kitakyushu (Japan)

    2014-03-15

    To correlate median nerve T2 signal and shape at the carpal tunnel with steroid injection (SI) response in carpal tunnel syndrome (CTS) patients. One hundred and sixty-three CTS wrists of 92 consecutive patients who were scheduled to undergo SI were prospectively evaluated with 3-T magnetic resonance imaging (MRI) and a nerve conduction study. All patients underwent axial high-resolution T2-weighted MRI (in-plane resolution of 0.25 x 0.25 mm). The CTS wrists were classified into three groups according to the nerve T2 signal and the flattening ratio at the hook of hamate level: group 1, high and oval; group 2, high and flat; group 3, low and flat. Clinical response to SI was evaluated at 6 months after injection. One hundred and thirteen of the 163 wrists (69.3 %) responded well to SI. The percentage of improvement was 81.7 % (49/60) in group 1, 69.9 % (51/73) in group 2, and 43.3 % (13/30) in group 3 (P < 0.01). On stepwise logistic regression analysis high-resolution MRI was the only significant independent factor for SI response in CTS patients (P < 0.01). High-resolution MRI correlates well with SI response in CTS patients and seems useful for predicting SI response. (orig.)

  1. 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

  2. Chemoreceptor Responsiveness at Sea Level Does Not Predict the Pulmonary Pressure Response to High Altitude.

    Science.gov (United States)

    Hoiland, Ryan L; Foster, Glen E; Donnelly, Joseph; Stembridge, Mike; Willie, Chris K; Smith, Kurt J; Lewis, Nia C; Lucas, Samuel J E; Cotter, Jim D; Yeoman, David J; Thomas, Kate N; Day, Trevor A; Tymko, Mike M; Burgess, Keith R; Ainslie, Philip N

    2015-07-01

    The hypoxic ventilatory response (HVR) at sea level (SL) is moderately predictive of the change in pulmonary artery systolic pressure (PASP) to acute normobaric hypoxia. However, because of progressive changes in the chemoreflex control of breathing and acid-base balance at high altitude (HA), HVR at SL may not predict PASP at HA. We hypothesized that resting oxygen saturation as measured by pulse oximetry (Spo₂) at HA would correlate better than HVR at SL with PASP at HA. In 20 participants at SL, we measured normobaric, isocapnic HVR (L/min · -%Spo₂⁻¹) and resting PASP using echocardiography. Both resting Spo₂ and PASP measures were repeated on day 2 (n = 10), days 4 to 8 (n = 12), and 2 to 3 weeks (n = 8) after arrival at 5,050 m. These data were also collected at 5,050 m in life-long HA residents (ie, Sherpa [n = 21]). Compared with SL, Spo₂ decreased from 98.6% to 80.5% (P HVR at SL was not related to Spo₂ or PASP at any time point at 5,050 m (all P > .05). Sherpa had lower PASP (P .50), there was a weak relationship in the Sherpa (R² = 0.16, P = .07). We conclude that neither HVR at SL nor resting Spo₂ at HA correlates with elevations in PASP at HA.

  3. Improving Response Inhibition in Parkinson’s Disease with Atomoxetine

    Science.gov (United States)

    Ye, Zheng; Altena, Ellemarije; Nombela, Cristina; Housden, Charlotte R.; Maxwell, Helen; Rittman, Timothy; Huddleston, Chelan; Rae, Charlotte L.; Regenthal, Ralf; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.; Rowe, James B.

    2015-01-01

    Background Dopaminergic drugs remain the mainstay of Parkinson’s disease therapy but often fail to improve cognitive problems such as impulsivity. This may be due to the loss of other neurotransmitters, including noradrenaline, which is linked to impulsivity and response inhibition. We therefore examined the effect of the selective noradrenaline reuptake inhibitor atomoxetine on response inhibition in a stop-signal paradigm. Methods This pharmacological functional magnetic resonance imaging study used a double-blinded randomized crossover design with low-frequency inhibition trials distributed among frequent Go trials. Twenty-one patients received 40 mg atomoxetine or placebo. Control subjects were tested on no-drug. The effects of disease and drug on behavioral performance, regional brain activity, and functional connectivity were analyzed using general linear models. Anatomical connectivity was examined using diffusion-weighted imaging. Results Patients with Parkinson’s disease had longer stop-signal reaction times, less stop-related activation in the right inferior frontal gyrus (RIFG), and weaker functional connectivity between the RIFG and striatum compared with control subjects. Atomoxetine enhanced stop-related RIFG activation in proportion to disease severity. Although there was no overall behavioral benefit from atomoxetine, analyses of individual differences revealed that enhanced response inhibition by atomoxetine was associated with increased RIFG activation and functional frontostriatal connectivity. Improved performance was more likely in patients with higher structural frontostriatal connectivity. Conclusions This study suggests that enhanced prefrontal cortical activation and frontostriatal connectivity by atomoxetine may improve response inhibition in Parkinson’s disease. These results point the way to new stratified clinical trials of atomoxetine to treat impulsivity in selected patients with Parkinson’s disease. PMID:24655598

  4. Improving response inhibition in Parkinson's disease with atomoxetine.

    Science.gov (United States)

    Ye, Zheng; Altena, Ellemarije; Nombela, Cristina; Housden, Charlotte R; Maxwell, Helen; Rittman, Timothy; Huddleston, Chelan; Rae, Charlotte L; Regenthal, Ralf; Sahakian, Barbara J; Barker, Roger A; Robbins, Trevor W; Rowe, James B

    2015-04-15

    Dopaminergic drugs remain the mainstay of Parkinson's disease therapy but often fail to improve cognitive problems such as impulsivity. This may be due to the loss of other neurotransmitters, including noradrenaline, which is linked to impulsivity and response inhibition. We therefore examined the effect of the selective noradrenaline reuptake inhibitor atomoxetine on response inhibition in a stop-signal paradigm. This pharmacological functional magnetic resonance imaging study used a double-blinded randomized crossover design with low-frequency inhibition trials distributed among frequent Go trials. Twenty-one patients received 40 mg atomoxetine or placebo. Control subjects were tested on no-drug. The effects of disease and drug on behavioral performance, regional brain activity, and functional connectivity were analyzed using general linear models. Anatomical connectivity was examined using diffusion-weighted imaging. Patients with Parkinson's disease had longer stop-signal reaction times, less stop-related activation in the right inferior frontal gyrus (RIFG), and weaker functional connectivity between the RIFG and striatum compared with control subjects. Atomoxetine enhanced stop-related RIFG activation in proportion to disease severity. Although there was no overall behavioral benefit from atomoxetine, analyses of individual differences revealed that enhanced response inhibition by atomoxetine was associated with increased RIFG activation and functional frontostriatal connectivity. Improved performance was more likely in patients with higher structural frontostriatal connectivity. This study suggests that enhanced prefrontal cortical activation and frontostriatal connectivity by atomoxetine may improve response inhibition in Parkinson's disease. These results point the way to new stratified clinical trials of atomoxetine to treat impulsivity in selected patients with Parkinson's disease. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  5. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  6. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  7. 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.

  8. Improving the reliability of fishery predictions under climate change

    DEFF Research Database (Denmark)

    Brander, Keith

    2015-01-01

    The increasing number of publications assessing impacts of climate change on marine ecosystems and fisheries attests to rising scientific and public interest. A selection of recent papers, dealing more with biological than social and economic aspects, is reviewed here, with particular attention...... to the reliability of projections of climate impacts on future fishery yields. The 2014 Intergovernmental Panel on Climate Change (IPCC) report expresses high confidence in projections that mid- and high-latitude fish catch potential will increase by 2050 and medium confidence that low-latitude catch potential...... understanding of climate impacts, such as how to improve coupled models from physics to fish and how to strengthen confidence in analysis of time series...

  9. Verification and improvement of predictive algorithms for radionuclide migration

    International Nuclear Information System (INIS)

    Carnahan, C.L.; Miller, C.W.; Remer, J.S.

    1984-01-01

    This research addresses issues relevant to numerical simulation and prediction of migration of radionuclides in the environment of nuclear waste repositories. Specific issues investigated are the adequacy of current numerical codes in simulating geochemical interactions affecting radionuclide migration, the level of complexity required in chemical algorithms of transport models, and the validity of the constant-k/sub D/ concept in chemical transport modeling. An initial survey of the literature led to the conclusion that existing numerical codes did not encompass the full range of chemical and physical phenomena influential in radionuclide migration. Studies of chemical algorithms have been conducted within the framework of a one-dimensional numerical code that simulates the transport of chemically reacting solutes in a saturated porous medium. The code treats transport by dispersion/diffusion and advection, and equilibrium-controlled proceses of interphase mass transfer, complexation in the aqueous phase, pH variation, and precipitation/dissolution of secondary solids. Irreversible, time-dependent dissolution of solid phases during transport can be treated. Mass action, transport, and sorptive site constraint equations are expressed in differential/algebraic form and are solved simultaneously. Simulations using the code show that use of the constant-k/sub D/ concept can produce unreliable results in geochemical transport modeling. Applications to a field test and laboratory analogs of a nuclear waste repository indicate that a thermodynamically based simulator of chemical transport can successfully mimic real processes provided that operative chemical mechanisms and associated data have been correctly identified and measured, and have been incorporated in the simulator. 17 references, 10 figures

  10. Improving Radar QPE's in Complex Terrain for Improved Flash Flood Monitoring and Prediction

    Science.gov (United States)

    Cifelli, R.; Streubel, D. P.; Reynolds, D.

    2010-12-01

    Quantitative Precipitation Estimation (QPE) is extremely challenging in regions of complex terrain due to a combination of issues related to sampling. In particular, radar beams are often blocked or scan above the liquid precipitation zone while rain gauge density is often too low to properly characterize the spatial distribution of precipitation. Due to poor radar coverage, rain gauge networks are used by the National Weather Service (NWS) River Forecast Centers as the principal source for QPE across the western U.S. The California Nevada River Forecast Center (CNRFC) uses point rainfall measurements and historical rainfall runoff relationships to derive river stage forecasts. The point measurements are interpolated to a 4 km grid using Parameter-elevation Regressions on Independent Slopes Model (PRISM) data to develop a gridded 6-hour QPE product (hereafter referred to as RFC QPE). Local forecast offices can utilize the Multi-sensor Precipitation Estimator (MPE) software to improve local QPE’s and thus local flash flood monitoring and prediction. MPE uses radar and rain gauge data to develop a combined QPE product at 1-hour intervals. The rain gauge information is used to bias correct the radar precipitation estimates so that, in situations where the rain gauge density and radar coverage are adequate, MPE can take advantage of the spatial coverage of the radar and the “ground truth” of the rain gauges to provide an accurate QPE. The MPE 1-hour QPE analysis should provide better spatial and temporal resolution for short duration hydrologic events as compared to 6-hour analyses. These hourly QPEs are then used to correct radar derived rain rates used by the Flash Flood Monitoring and Prediction (FFMP) software in forecast offices for issuance of flash flood warnings. Although widely used by forecasters across the eastern U.S., MPE is not used extensively by the NWS in the west. Part of the reason for the lack of use of MPE across the west is that there has

  11. A predictive control scheme for automated demand response mechanisms

    NARCIS (Netherlands)

    Lampropoulos, I.; Bosch, van den P.P.J.; Kling, W.L.

    2012-01-01

    The development of demand response mechanisms can provide a considerable option for the integration of renewable energy sources and the establishment of efficient generation and delivery of electrical power. The full potential of demand response can be significant, but its exploration still remains

  12. Predicting emergency department volume using forecasting methods to create a "surge response" for noncrisis events.

    Science.gov (United States)

    Chase, Valerie J; Cohn, Amy E M; Peterson, Timothy A; Lavieri, Mariel S

    2012-05-01

    true negatives ranged from 70% to 90% depending on the threshold used to determine the state of the ED with the 30-minute prediction model. The CUR is a new and robust indicator of an ED system's performance. The study was able to model the tradeoff of longer time to response versus shorter but more accurate predictions, by investigating different prediction intervals. Current practice would have been improved by using the proposed models and would have identified the surge in patient volume earlier on noncrisis days. © 2012 by the Society for Academic Emergency Medicine.

  13. 5-HTTLPR differentially predicts brain network responses to emotional faces

    DEFF Research Database (Denmark)

    Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K

    2015-01-01

    The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions...... to fearful faces was significantly greater in S' carriers compared to LA LA individuals. These findings provide novel evidence for emotion-specific 5-HTTLPR effects on the response of a distributed set of brain regions including areas responsive to emotionally salient stimuli and critical components...... involved in emotion, cognitive and visual processing, less is known about 5-HTTLPR effects on broader network responses. To address this, we evaluated 5-HTTLPR differences in the whole-brain response to an emotional faces paradigm including neutral, angry and fearful faces using functional magnetic...

  14. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  15. Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

    Science.gov (United States)

    Cho, Seung Hyun; Kim, Gab Chul; Jang, Yun-Jin; Ryeom, Hunkyu; Kim, Hye Jung; Shin, Kyung-Min; Park, Jun Seok; Choi, Gyu-Seog; Kim, See Hyung

    2015-09-01

    The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC. © The Foundation Acta Radiologica 2014.

  16. 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.

  17. Using synchronization in multi-model ensembles to improve prediction

    Science.gov (United States)

    Hiemstra, P.; Selten, F.

    2012-04-01

    In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of

  18. Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

    Science.gov (United States)

    Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

    2012-01-01

    Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

  19. Simplified inelastic seismic response analysis of piping system using improved capacity spectrum method

    International Nuclear Information System (INIS)

    Iijima, Tadashi

    2005-01-01

    We applied improved capacity spectrum method (ICSM) to a piping system with an asymmetric load-deformation relationship in a piping elbow. The capacity spectrum method can predict an inelastic response by balancing the structural capacity obtained from the load-deformation relationship with the seismic demand defined by an acceleration-displacement response spectrum. The ICSM employs (1) effective damping ratio and period that are based on a statistical methodology, (2) practical procedures necessary to obtain a balance between the structural capacity and the seismic demand. The effective damping ratio and period are defined so as to maximize the probability that predicted response errors lie inside the -10 to 20% range. However, without taking asymmetry into consideration the displacement calculated by using the load-deformation relationship on the stiffer side was 39% larger than that of a time history analysis by a direct integral method. On the other hand, when asymmetry was taken into account, the calculated displacement was only 14% larger than that of a time history analysis. Thus, we verified that the ICSM could predict the inelastic response with errors lying within the -10 to 20% range, by taking into account the asymmetric load-deformation relationship of the piping system. (author)

  20. Oxytocin receptor gene variation predicts subjective responses to MDMA.

    Science.gov (United States)

    Bershad, Anya K; Weafer, Jessica J; Kirkpatrick, Matthew G; Wardle, Margaret C; Miller, Melissa A; de Wit, Harriet

    2016-12-01

    3,4-Methylenedioxymethamphetamine (MDMA, "ecstasy") enhances desire to socialize and feelings of empathy, which are thought to be related to increased oxytocin levels. Thus, variation in the oxytocin receptor gene (OXTR) may influence responses to the drug. Here, we examined the influence of a single OXTR nucleotide polymorphism (SNP) on responses to MDMA in humans. Based on findings that carriers of the A allele at rs53576 exhibit reduced sensitivity to oxytocin-induced social behavior, we hypothesized that these individuals would show reduced subjective responses to MDMA, including sociability. In this three-session, double blind, within-subjects study, healthy volunteers with past MDMA experience (N = 68) received a MDMA (0, 0.75 mg/kg, and 1.5 mg/kg) and provided self-report ratings of sociability, anxiety, and drug effects. These responses were examined in relation to rs53576. MDMA (1.5 mg/kg) did not increase sociability in individuals with the A/A genotype as it did in G allele carriers. The genotypic groups did not differ in responses at the lower MDMA dose, or in cardiovascular or other subjective responses. These findings are consistent with the idea that MDMA-induced sociability is mediated by oxytocin, and that variation in the oxytocin receptor gene may influence responses to the drug.

  1. Improving mental health service responses to domestic violence and abuse.

    Science.gov (United States)

    Trevillion, Kylee; Corker, Elizabeth; Capron, Lauren E; Oram, Siân

    2016-10-01

    Domestic violence and abuse is a considerable international public health problem, which is associated with mental disorders in both women and men. Nevertheless, victimization and perpetration remain undetected by mental health services. This paper reviews the evidence on mental health service responses to domestic violence, including identifying, referring, and providing care for people experiencing or perpetrating violence. The review highlights the need for mental health services to improve rates of identification and responses to domestic violence and abuse, through the provision of specific training on domestic violence and abuse, the implementation of clear information sharing protocols and evidence-based interventions, and the establishment of care referral pathways. This review also highlights the need for further research into mental health service users who perpetrate domestic violence and abuse.

  2. Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

    Science.gov (United States)

    Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

    2016-01-01

    Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize

  3. GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

    Science.gov (United States)

    Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

    2018-04-01

    Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.

  4. 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.

  5. Can brain responses to movie trailers predict success?

    NARCIS (Netherlands)

    M.A.S. Boksem (Maarten)

    2015-01-01

    textabstractDecades of research have shown that much of our mental processing occurs at the subconscious level, including the decisions we make as consumers. These subconscious processes explain why we so often fail to accurately predict our own future choices. Often what we think we want has

  6. Mechanistic Modeling Framework for Predicting Extreme Battery Response

    Energy Technology Data Exchange (ETDEWEB)

    Moffat, Harry K.; Geller, Anthony S.; R. Kee (CSM); S. Allu (ORNL)

    2017-03-01

    The objective of this project was to Address root cause and implications of thermal runaway of Li-ion batteries by delivering a software architecture solution that can lead to the development of predictive mechanisms that are based on identification of species.

  7. Mechanistic Modeling Framework for Predicting Extreme Battery Response

    Energy Technology Data Exchange (ETDEWEB)

    Geller, Anthony S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-11-01

    The objectives of this project are to address the root cause implications of thermal runaway of Li-ion batteries by delivering a software architecture solution that can lead to the development of predictive mechanisms that are based on identification of species.

  8. Novel transformation-based response prediction of shear building ...

    Indian Academy of Sciences (India)

    c Indian Academy of Sciences ... structural response of multi-storey shear buildings subject to earthquake motion. The INN is first ... China has been presented by Xie et al. (2011). ... research works have been done using INN in other fields.

  9. 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

  10. 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.

  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. In situ immune response after neoadjuvant chemotherapy for breast cancer predicts survival.

    Science.gov (United States)

    Ladoire, Sylvain; Mignot, Grégoire; Dabakuyo, Sandrine; Arnould, Laurent; Apetoh, Lionel; Rébé, Cedric; Coudert, Bruno; Martin, Francois; Bizollon, Marie Hélène; Vanoli, André; Coutant, Charles; Fumoleau, Pierre; Bonnetain, Franck; Ghiringhelli, François

    2011-07-01

    Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. However, the predictive value of tumour-infiltrating lymphocytes after neoadjuvant chemotherapy for breast cancer remains unknown. We hypothesized that the nature of the immune infiltrate following neoadjuvant chemotherapy would predict patient survival. In a series of 111 consecutive HER2- and a series of 51 non-HER2-overexpressing breast cancer patients treated by neoadjuvant chemotherapy, we studied by immunohistochemistry tumour infiltration by FOXP3 and CD8 T lymphocytes before and after chemotherapy. Kaplan-Meier analysis and Cox modelling were used to assess relapse-free survival (RFS) and overall survival (OS). A predictive scoring system using American Joint Committee on Cancer (AJCC) pathological staging and immunological markers was created. Association of high CD8 and low FOXP3 cell infiltrates after chemotherapy was significantly associated with improved RFS (p = 0.02) and OS (p = 0.002), and outperformed classical predictive factors in multivariate analysis. A combined score associating CD8/FOXP3 ratio and pathological AJCC staging isolated a subgroup of patients with a long-term overall survival of 100%. Importantly, this score also identified patients with a favourable prognosis in an independent cohort of HER2-negative breast cancer patients. These results suggest that immunological CD8 and FOXP3 cell infiltrate after treatment is an independent predictive factor of survival in breast cancer patients treated with neoadjuvant chemotherapy and provides new insights into the role of the immune milieu and cancer. Copyright © 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

  13. Predicting fluid responsiveness with transthoracic echocardiography is not yet evidence based

    DEFF Research Database (Denmark)

    Wetterslev, M; Haase, N; Johansen, R R

    2013-01-01

    an integrated tool in the intensive care unit, this systematic review examined studies evaluating the predictive value of TTE for fluid responsiveness. In October 2012, we searched Pubmed, EMBASE and Web of Science for studies evaluating the predictive value of TTE-derived variables for fluid responsiveness...... responsiveness. Of the 4294 evaluated citations, only one study fully met our inclusion criteria. In this study, the predictive value of variations in inferior vena cava diameter (> 16%) for fluid responsiveness was moderate with sensitivity of 71% [95% confidence interval (CI) 44-90], specificity of 100% (95......% CI 73-100) and an area under the receiver operating curve of 0.90 (95% CI 0.73-0.98). Only one study of TTE-based methods fulfilled the criteria for valid assessment of fluid responsiveness. Before recommending the use of TTE in predicting fluid responsiveness, proper evaluation including...

  14. 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.

  15. Response prediction of long flexible risers subject to forced harmonic vibration

    OpenAIRE

    Riveros, Carlos Alberto; Utsunomiya, Tomoaki; Maeda, Katsuya; Itoh, Kazuaki

    2010-01-01

    Several research efforts have been directed toward the development of models for response prediction of flexible risers. The main difficulties arise from the fact that the dynamic response of flexible risers involves highly nonlinear behavior and a self-regulated process. This article presents a quasi-steady approach for response prediction of oscillating flexible risers. Amplitude-dependent lift coefficients are considered, as is an increased mean drag coefficient model during synchronizatio...

  16. 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...

  17. Innovative improvements of thermal response tests - Final report

    Energy Technology Data Exchange (ETDEWEB)

    Poppei, J.; Schwarz, R. [AF-Colenco Ltd, Baden (Switzerland); Peron, H.; Silvani, C; Steinmann, G.; Laloui, L. [Swiss Federal Institute of Technology, Laboratoire de Mecanique des Sols, Lausanne (Switzerland); Wagner, R.; Lochbuehler, T.; Rohner, E. [Geowatt AG, Zuerich (Switzerland)

    2008-12-15

    This illustrated final report for Swiss Federal Office of Energy (SFOE) takes a look at innovative improvements to thermal response tests that are used to investigate the thermo-physical properties of the ground for the purpose of dimensioning borehole heat exchangers. Recent technical developments in the borehole investigation tools area provide a promising prerequisite for improved estimates of thermal conductivity. A mini-module developed at the Swiss Federal Institute of Technology EPFL which is suitable for fast and flexible thermal response testing is discussed as is a wireless miniature data logger for continuous temperature recordings in borehole heat exchangers up to a depth of 350 m. This allows high-resolution vertical temperature profiling in boreholes. International state-of-the-art methods are reviewed. The adaptations to the analytical methods necessary for the effective application of these tools are discussed and numerical methods available are looked at. The testing of the methods developed and their results are discussed, as is the influence of ground-water flow.

  18. Using Technology to Improve the Objectivity of Criminal Responsibility Evaluations.

    Science.gov (United States)

    Vitacco, Michael J; Gottfried, Emily D; Batastini, Ashley B

    2018-03-01

    Criminal responsibility (or insanity) evaluations require forensic clinicians to reconstruct a defendant's decision-making abilities, behavioral control, and emotional state at the time of the criminal act. Forensic evaluators are ultimately tasked to evaluate whether an individual had the capacity to understand right from wrong, and in some jurisdictions, determine whether the defendant lacked substantial capacity to conform his behavior to the requirements of the law as a result of a threshold condition (e.g., mental illness). Insanity evaluations are inherently complex, because they require the clinician to determine someone's mental state at some point in the past (weeks, months, or even years). Recent research on insanity evaluations underscores significant problems with the reliability and validity of these evaluations. However, technological advances including social media (e.g., Facebook and Twitter), mandating that law enforcement videotape interrogations, and the use of body and dashboard cameras can aid clinicians in improving the precision and quality of insanity evaluations. This article discusses practical guidelines and ethics-related concerns regarding the use of technology to improve the objectivity of criminal responsibility evaluations. © 2018 American Academy of Psychiatry and the Law.

  19. 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

  20. 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.

  1. 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

  2. Strategies for Improved Hospital Response to Mass Casualty Incidents.

    Science.gov (United States)

    TariVerdi, Mersedeh; Miller-Hooks, Elise; Kirsch, Thomas

    2018-03-19

    Mass casualty incidents are a concern in many urban areas. A community's ability to cope with such events depends on the capacities and capabilities of its hospitals for handling a sudden surge in demand of patients with resource-intensive and specialized medical needs. This paper uses a whole-hospital simulation model to replicate medical staff, resources, and space for the purpose of investigating hospital responsiveness to mass casualty incidents. It provides details of probable demand patterns of different mass casualty incident types in terms of patient categories and arrival patterns, and accounts for related transient system behavior over the response period. Using the layout of a typical urban hospital, it investigates a hospital's capacity and capability to handle mass casualty incidents of various sizes with various characteristics, and assesses the effectiveness of designed demand management and capacity-expansion strategies. Average performance improvements gained through capacity-expansion strategies are quantified and best response actions are identified. Capacity-expansion strategies were found to have superadditive benefits when combined. In fact, an acceptable service level could be achieved by implementing only 2 to 3 of the 9 studied enhancement strategies. (Disaster Med Public Health Preparedness. 2018;page 1 of 13).

  3. 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.

  4. Accuracy of eosinophils and eosinophil cationic protein to predict steroid improvement in asthma

    NARCIS (Netherlands)

    Meijer, RJ; Postma, DS; Kauffman, HF; Arends, LR; Koeter, GH; Kerstjens, HAM

    Background There is a large variability in clinical response to corticosteroid treatment in patients with asthma. Several markers of inflammation like eosinophils and eosinophil cationic protein (ECP), as well as exhaled nitric oxide (NO), are good candidates to predict clinical response. Aim We

  5. 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.

  6. Individual differences in regulatory focus predict neural response to reward.

    Science.gov (United States)

    Scult, Matthew A; Knodt, Annchen R; Hanson, Jamie L; Ryoo, Minyoung; Adcock, R Alison; Hariri, Ahmad R; Strauman, Timothy J

    2017-08-01

    Although goal pursuit is related to both functioning of the brain's reward circuits and psychological factors, the literatures surrounding these concepts have often been separate. Here, we use the psychological construct of regulatory focus to investigate individual differences in neural response to reward. Regulatory focus theory proposes two motivational orientations for personal goal pursuit: (1) promotion, associated with sensitivity to potential gain, and (2) prevention, associated with sensitivity to potential loss. The monetary incentive delay task was used to manipulate reward circuit function, along with instructional framing corresponding to promotion and prevention in a within-subject design. We observed that the more promotion oriented an individual was, the lower their ventral striatum response to gain cues. Follow-up analyses revealed that greater promotion orientation was associated with decreased ventral striatum response even to no-value cues, suggesting that promotion orientation may be associated with relatively hypoactive reward system function. The findings are also likely to represent an interaction between the cognitive and motivational characteristics of the promotion system with the task demands. Prevention orientation did not correlate with ventral striatum response to gain cues, supporting the discriminant validity of regulatory focus theory. The results highlight a dynamic association between individual differences in self-regulation and reward system function.

  7. Relationship-Based Infant Care: Responsive, on Demand, and Predictable

    Science.gov (United States)

    Petersen, Sandra; Wittmer, Donna

    2008-01-01

    Young babies are easily overwhelmed by the pain of hunger or gas. However, when an infant's day is filled with caregiving experiences characterized by quick responses to his cries and accurate interpretations of the meaning of his communication, the baby learns that he can count on being fed and comforted. He begins to develop trust in his teacher…

  8. Linking precipitation, evapotranspiration and soil moisture content for the improvement of predictability over land

    Science.gov (United States)

    Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

    2013-04-01

    Climate change scenarios are expected to show an intensification of the hydrological cycle together with modifications of evapotranspiration and soil moisture content. Evapotranspiration changes have been already evidenced for the end of the 20th century. The variance of evapotranspiration has been shown to be strongly related to the variance of precipitation over land. Nevertheless, the feedbacks between evapotranspiration, soil moisture and precipitation have not yet been completely understood at present-day. Furthermore, soil moisture reservoirs are associated to a memory and thus their proper initialization may have a strong influence on predictability. In particular, the linkage between precipitation and soil moisture is modulated by the effects on evapotranspiration. Therefore, the investigation of the coupling between these variables appear to be of primary importance for the improvement of predictability over the continents. The coupled manifold (CM) technique (Navarra and Tribbia 2005) is a method designed to separate the effects of the variability of two variables which are connected. This method has proved to be successful for the analysis of different climate fields, like precipitation, vegetation and sea surface temperature. In particular, the coupled variables reveal patterns that may be connected with specific phenomena, thus providing hints regarding potential predictability. In this study we applied the CM to recent observational datasets of precipitation (from CRU), evapotranspiration (from GIMMS and MODIS satellite-based estimates) and soil moisture content (from ESA) spanning a time period of 23 years (1984-2006) with a monthly frequency. Different data stratification (monthly, seasonal, summer JJA) have been employed to analyze the persistence of the patterns and their characteristical time scales and seasonality. The three variables considered show a significant coupling among each other. Interestingly, most of the signal of the

  9. 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)

  10. Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form

    OpenAIRE

    Shira Barzilay; Zimri S. Yaseen; Zimri S. Yaseen; Mariah Hawes; Bernard Gorman; Rachel Altman; Adriana Foster; Alan Apter; Paul Rosenfield; Igor Galynker; Igor Galynker

    2018-01-01

    BackgroundMental health professionals have a pivotal role in suicide prevention. However, they also often have intense emotional responses, or countertransference, during encounters with suicidal patients. Previous studies of the Therapist Response Questionnaire-Suicide Form (TRQ-SF), a brief novel measure aimed at probing a distinct set of suicide-related emotional responses to patients found it to be predictive of near-term suicidal behavior among high suicide-risk inpatients. The purpose o...

  11. Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

    Science.gov (United States)

    Qi, D.; Majda, A.

    2017-12-01

    A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

  12. 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....

  13. Brain anatomy and chemistry may predict treatment response in paediatric obsessive--compulsive disorder.

    Science.gov (United States)

    Rosenberg, D R; MacMillan, S N; Moore, G J

    2001-06-01

    Obsessive--compulsive disorder (OCD) is a severe, highly prevalent and often chronically disabling illness with frequent onset in childhood and adolescence. This underscores the importance of studying the illness during childhood near the onset of illness to minimize potential confounds of long-term illness duration and treatment intervention as well as to examine the developmental underpinnings of the illness. In this review, the authors focus on an integrated series of brain-imaging studies in paediatric OCD suggesting a reversible glutamatergically mediated thalamo-cortical--striatal dysfunction in OCD and their relevance for improved diagnosis and treatment of the condition. Developmental neurobiological models for OCD are presented and particular attention is devoted to evaluating neuroimaging studies designed to test these models and how they may help predict treatment response in paediatric OCD.

  14. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

    Science.gov (United States)

    Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

    2016-01-01

    Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037

  15. Conceptualizing, Understanding, and Predicting Responsible Decisions and Quality Input

    Science.gov (United States)

    Wall, N.; PytlikZillig, L. M.

    2012-12-01

    In areas such as climate change, where uncertainty is high, it is arguably less difficult to tell when efforts have resulted in changes in knowledge, than when those efforts have resulted in responsible decisions. What is a responsible decision? More broadly, when it comes to citizen input, what is "high quality" input? And most importantly, how are responsible decisions and quality input enhanced? The aim of this paper is to contribute to the understanding of the different dimensions of "responsible" or "quality" public input and citizen decisions by comparing and contrasting the different predictors of those different dimensions. We first present different possibilities for defining, operationalizing and assessing responsible or high quality decisions. For example, responsible decisions or quality input might be defined as using specific content (e.g., using climate change information in decisions appropriately), as using specific processes (e.g., investing time and effort in learning about and discussing the issues prior to making decisions), or on the basis of some judgment of the decision or input itself (e.g., judgments of the rationale provided for the decisions, or number of issues considered when giving input). Second, we present results from our work engaging people with science policy topics, and the different ways that we have tried to define these two constructs. In the area of climate change specifically, we describe the development of a short survey that assesses exposure to climate information, knowledge of and attitudes toward climate change, and use of climate information in one's decisions. Specifically, the short survey was developed based on a review of common surveys of climate change related knowledge, attitudes, and behaviors, and extensive piloting and cognitive interviews. Next, we analyze more than 200 responses to that survey (data collection is currently ongoing and will be complete after the AGU deadline), and report the predictors of

  16. DNA methylation–based immune response signature improves patient diagnosis in multiple cancers

    Science.gov (United States)

    Jeschke, Jana; Bizet, Martin; Calonne, Emilie; Dedeurwaerder, Sarah; Garaud, Soizic; Koch, Alexander; Larsimont, Denis; Salgado, Roberto; Van den Eynden, Gert; Willard Gallo, Karen; Defrance, Matthieu; Sotiriou, Christos

    2017-01-01

    BACKGROUND. The tumor immune response is increasingly associated with better clinical outcomes in breast and other cancers. However, the evaluation of tumor-infiltrating lymphocytes (TILs) relies on histopathological measurements with limited accuracy and reproducibility. Here, we profiled DNA methylation markers to identify a methylation of TIL (MeTIL) signature that recapitulates TIL evaluations and their prognostic value for long-term outcomes in breast cancer (BC). METHODS. MeTIL signature scores were correlated with clinical endpoints reflecting overall or disease-free survival and a pathologic complete response to preoperative anthracycline therapy in 3 BC cohorts from the Jules Bordet Institute in Brussels and in other cancer types from The Cancer Genome Atlas. RESULTS. The MeTIL signature measured TIL distributions in a sensitive manner and predicted survival and response to chemotherapy in BC better than did histopathological assessment of TILs or gene expression–based immune markers, respectively. The MeTIL signature also improved the prediction of survival in other malignancies, including melanoma and lung cancer. Furthermore, the MeTIL signature predicted differences in survival for malignancies in which TILs were not known to have a prognostic value. Finally, we showed that MeTIL markers can be determined by bisulfite pyrosequencing of small amounts of DNA from formalin-fixed, paraffin-embedded tumor tissue, supporting clinical applications for this methodology. CONCLUSIONS. This study highlights the power of DNA methylation to evaluate tumor immune responses and the potential of this approach to improve the diagnosis and treatment of breast and other cancers. FUNDING. This work was funded by the Fonds National de la Recherche Scientifique (FNRS) and Télévie, the INNOVIRIS Brussels Region BRUBREAST Project, the IUAP P7/03 program, the Belgian “Foundation against Cancer,” the Breast Cancer Research Foundation (BCRF), and the Fonds Gaston Ithier

  17. Optimization of a novel improver gel formulation for Barbari flat bread using response surface methodology.

    Science.gov (United States)

    Pourfarzad, Amir; Haddad Khodaparast, Mohammad Hossein; Karimi, Mehdi; Mortazavi, Seyed Ali

    2014-10-01

    Nowadays, the use of bread improvers has become an essential part of improving the production methods and quality of bakery products. In the present study, the Response Surface Methodology (RSM) was used to determine the optimum improver gel formulation which gave the best quality, shelf life, sensory and image properties for Barbari flat bread. Sodium stearoyl-2-lactylate (SSL), diacetyl tartaric acid esters of monoglyceride (DATEM) and propylene glycol (PG) were constituents of the gel and considered in this study. A second-order polynomial model was fitted to each response and the regression coefficients were determined using least square method. The optimum gel formulation was found to be 0.49 % of SSL, 0.36 % of DATEM and 0.5 % of PG when desirability function method was applied. There was a good agreement between the experimental data and their predicted counterparts. Results showed that the RSM, image processing and texture analysis are useful tools to investigate, approximate and predict a large number of bread properties.

  18. 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)

  19. 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.

  20. 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.

  1. 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.

  2. A Coupled Probabilistic Wake Vortex and Aircraft Response Prediction Model

    Science.gov (United States)

    Gloudemans, Thijs; Van Lochem, Sander; Ras, Eelco; Malissa, Joel; Ahmad, Nashat N.; Lewis, Timothy A.

    2016-01-01

    Wake vortex spacing standards along with weather and runway occupancy time, restrict terminal area throughput and impose major constraints on the overall capacity and efficiency of the National Airspace System (NAS). For more than two decades, the National Aeronautics and Space Administration (NASA) has been conducting research on characterizing wake vortex behavior in order to develop fast-time wake transport and decay prediction models. It is expected that the models can be used in the systems level design of advanced air traffic management (ATM) concepts that safely increase the capacity of the NAS. It is also envisioned that at a later stage of maturity, these models could potentially be used operationally, in groundbased spacing and scheduling systems as well as on the flight deck.

  3. Prediction of the human response time with the similarity and quantity of information

    International Nuclear Information System (INIS)

    Lee, Sungjin; Heo, Gyunyoung; Chang, Soon Heung

    2006-01-01

    Memory is one of brain processes that are important when trying to understand how people process information. Although a large number of studies have been made on the human performance, little is known about the similarity effect in human performance. The purpose of this paper is to propose and validate the quantitative and predictive model on the human response time in the user interface with the concept of similarity. However, it is not easy to explain the human performance with only similarity or information amount. We are confronted by two difficulties: making the quantitative model on the human response time with the similarity and validating the proposed model by experimental work. We made the quantitative model based on the Hick's law and the law of practice. In addition, we validated the model with various experimental conditions by measuring participants' response time in the environment of computer-based display. Experimental results reveal that the human performance is improved by the user interface's similarity. We think that the proposed model is useful for the user interface design and evaluation phases

  4. Diminished neural responses predict enhanced intrinsic motivation and sensitivity to external incentive.

    Science.gov (United States)

    Marsden, Karen E; Ma, Wei Ji; Deci, Edward L; Ryan, Richard M; Chiu, Pearl H

    2015-06-01

    The duration and quality of human performance depend on both intrinsic motivation and external incentives. However, little is known about the neuroscientific basis of this interplay between internal and external motivators. Here, we used functional magnetic resonance imaging to examine the neural substrates of intrinsic motivation, operationalized as the free-choice time spent on a task when this was not required, and tested the neural and behavioral effects of external reward on intrinsic motivation. We found that increased duration of free-choice time was predicted by generally diminished neural responses in regions associated with cognitive and affective regulation. By comparison, the possibility of additional reward improved task accuracy, and specifically increased neural and behavioral responses following errors. Those individuals with the smallest neural responses associated with intrinsic motivation exhibited the greatest error-related neural enhancement under the external contingency of possible reward. Together, these data suggest that human performance is guided by a "tonic" and "phasic" relationship between the neural substrates of intrinsic motivation (tonic) and the impact of external incentives (phasic).

  5. Renal Nerve Stimulation-Induced Blood Pressure Changes Predict Ambulatory Blood Pressure Response After Renal Denervation.

    Science.gov (United States)

    de Jong, Mark R; Adiyaman, Ahmet; Gal, Pim; Smit, Jaap Jan J; Delnoy, Peter Paul H M; Heeg, Jan-Evert; van Hasselt, Boudewijn A A M; Lau, Elizabeth O Y; Persu, Alexandre; Staessen, Jan A; Ramdat Misier, Anand R; Steinberg, Jonathan S; Elvan, Arif

    2016-09-01

    Blood pressure (BP) response to renal denervation (RDN) is highly variable and its effectiveness debated. A procedural end point for RDN may improve consistency of response. The objective of the current analysis was to look for the association between renal nerve stimulation (RNS)-induced BP increase before and after RDN and changes in ambulatory BP monitoring (ABPM) after RDN. Fourteen patients with drug-resistant hypertension referred for RDN were included. RNS was performed under general anesthesia at 4 sites in the right and left renal arteries, both before and immediately after RDN. RNS-induced BP changes were monitored and correlated to changes in ambulatory BP at a follow-up of 3 to 6 months after RDN. RNS resulted in a systolic BP increase of 50±27 mm Hg before RDN and systolic BP increase of 13±16 mm Hg after RDN (Pefficacy of RDN and predict BP response to RDN. © 2016 American Heart Association, Inc.

  6. Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

    Directory of Open Access Journals (Sweden)

    Bart V J Cuppen

    Full Text Available In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA respond insufficiently to TNF-α inhibitors (TNFis. The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124. The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC, sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI. The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28, erythrocyte sedimentation rate (ESR or C-reactive protein (CRP. Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6, sn1-LPC(15:0, ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01. The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23 and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05. Our study established an accurate

  7. Implicit motives predict affective responses to emotional expressions

    Directory of Open Access Journals (Sweden)

    Andreas G. Rösch

    2013-12-01

    Full Text Available We explored the influence of implicit motives and activity inhibition on subjectively experienced affect in response to the presentation of six different facial expressions of emotion (FEEs; anger, disgust, fear, happiness, sadness, and surprise and neutral faces from the NimStim set of facial expressions (Tottenham et al., 2009. Implicit motives and activity inhibition were assessed using a Picture Story Exercise (Schultheiss et al., 2009b. Ratings of subjectively experienced affect (arousal and valence were assessed using Self-Assessment Manikins (Bradley and Lang, 1994 in a sample of 84 participants. We found that people with either a strong implicit power or achievement motive experienced stronger arousal, while people with a strong affiliation motive experienced less aroused and felt more unpleasant across emotions. Additionally, we obtained significant power motive × activity inhibition interactions for arousal ratings in response to FEEs and neutral faces. Participants with a strong power motive and weak activity inhibition experienced stronger arousal after the presentation of neutral faces but no additional increase in arousal after the presentation of FEEs. Participants with a strong power motive and strong activity inhibition (inhibited power motive did not feel aroused by neutral faces. However, their arousal increased in response to all FEEs with the exception of happy faces, for which their subjective arousal decreased. These more differentiated reaction pattern of individuals with an inhibited power motive suggest that they engage in a more socially adaptive manner of responding to different FEEs. Our findings extend established links between implicit motives and affective processes found at the procedural level to declarative reactions to FEEs. Implications are discussed with respect to dual-process models of motivation and research in motive congruence.

  8. 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.

  9. An expert system for improving nuclear emergency response

    International Nuclear Information System (INIS)

    Salame-Alfie, A.; Goldbogen, G.C.; Ryan, R.M.; Wallace, W.A.; Yeater, M.L.

    1987-01-01

    The accidents at TMI-2 and Chernobyl have produced initiatives aimed at improving nuclear plant emergency response capabilities. Among them are the development of emergency response facilities with capabilities for the acquisition, processing, and diagnosis of data which are needed to help coordinate plant operations, engineering support and management under emergency conditions. An effort in this direction prompted the development of an expert system. EP (EMERGENCY PLANNER) is a prototype expert system that is intended to help coordinate the overall management during emergency conditions. The EP system was built using the GEN-X expert system shell. GEN-X has a variety of knowledge representation mechanisms including AND/OR trees, Decision trees, and IF/THEN tables, and runs on an IBM PC-XT or AT computer or compatible. Among the main features, EP is portable, modular, user friendly, can interact with external programs and interrogate data bases. The knowledge base is made of New York State (NYS) Procedures for Emergency Classification, NYS Radiological Emergency Preparedness Plan (REPP) and knowledge from experts of the NYS Radiological Emergency Preparedness Group and the Office of Radiological Health and Chemistry of the New York Power Authority (NYPA)

  10. 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.

  11. 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).

  12. 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).

  13. Structural Plasticity Denoises Responses and Improves Learning Speed

    Directory of Open Access Journals (Sweden)

    Robin Spiess

    2016-09-01

    Full Text Available Despite an abundance of computational models for learning of synaptic weights, there has been relatively little research on structural plasticity, i.e. the creation and elimination of synapses. Especially, it is not clear how structural plasticity works in concert with spike-timing-dependent plasticity (STDP and what advantages their combination offers.Here we present a fairly large-scale functional model that uses leaky integrate-and-fire neurons, STDP, homeostasis, recurrent connections, and structural plasticity to learn the input encoding, the relation between inputs, and to infer missing inputs. Using this model, we compare the error and the amount of noise in the network's responses with and without structural plasticity and the influence of structural plasticity on the learning speed of the network.Using structural plasticity during learning shows good results for learning the representation of input values, i.e. structural plasticity strongly reduces the noise of the response by preventing spikes with a high error.For inferring missing inputs we see similar results, with responses having less noise if the network was trained using structural plasticity.Additionally, using structural plasticity with pruning significantly decreased the time to learn weights suitable for inference.Presumably, this is due to the clearer signal containing less spikes that misrepresent the desired value. Therefore, this work shows that structural plasticity is not only able to improve upon the performance using STDP without structural plasticity but also speeds up learning.Additionally, it addresses the practical problem of limited resources for connectivity that is not only apparent in the mammalian neocortex but also in computer hardware or neuromorphic (brain-inspired hardware by efficiently pruning synapses without losing performance.

  14. Individual Differences in Gelotophobia Predict Responses to Joy and Contempt

    Directory of Open Access Journals (Sweden)

    Jennifer Hofmann

    2015-04-01

    Full Text Available In a paradigm facilitating smile misattribution, facial responses and ratings to contempt and joy were investigated in individuals with or without gelotophobia (fear of being laughed at. Participants from two independent samples (N1 = 83, N2 = 50 rated the intensity of eight emotions in 16 photos depicting joy, contempt, and different smiles. Facial responses were coded by the Facial Action Coding System in the second study. Compared with non-fearful individuals, gelotophobes rated joy smiles as less joyful and more contemptuous. Moreover, gelotophobes showed less facial joy and more contempt markers. The contempt ratings were comparable between the two groups. Looking at the photos of smiles lifted the positive mood of non-gelotophobes, whereas gelotophobes did not experience an increase. We hypothesize that the interpretation bias of “joyful faces hiding evil minds” (i.e., being also contemptuous and exhibiting less joy facially may complicate social interactions for gelotophobes and serve as a maintaining factor of gelotophobia.

  15. 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

  16. 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

  17. Mathematical modeling improves EC50 estimations from classical dose-response curves.

    Science.gov (United States)

    Nyman, Elin; Lindgren, Isa; Lövfors, William; Lundengård, Karin; Cervin, Ida; Sjöström, Theresia Arbring; Altimiras, Jordi; Cedersund, Gunnar

    2015-03-01

    The β-adrenergic response is impaired in failing hearts. When studying β-adrenergic function in vitro, the half-maximal effective concentration (EC50 ) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50 . Instead, we have applied a mathematical modeling approach to interpret the full dose-response curve in a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose-response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time-resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180-525 nm) is higher than the classically interpreted EC50 (46-191 nm). Mathematical modeling thus makes it possible to re-interpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible. The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database, and may be accessed at http://jjj.bio.vu.nl/database/nyman. © 2015 FEBS.

  18. 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

  19. Improving Flood Prediction By the Assimilation of Satellite Soil Moisture in Poorly Monitored Catchments.

    Science.gov (United States)

    Alvarez-Garreton, C. D.; Ryu, D.; Western, A. W.; Crow, W. T.; Su, C. H.; Robertson, D. E.

    2014-12-01

    Flood prediction in poorly monitored catchments is among the greatest challenges faced by hydrologists. To address this challenge, an increasing number of studies in the last decade have explored methods to integrate various existing observations from ground and satellites. One approach in particular, is the assimilation of satellite soil moisture (SM-DA) into rainfall-runoff models. The rationale is that satellite soil moisture (SSM) can be used to correct model soil water states, enabling more accurate prediction of catchment response to precipitation and thus better streamflow. However, there is still no consensus on the most effective SM-DA scheme and how this might depend on catchment scale, climate characteristics, runoff mechanisms, model and SSM products used, etc. In this work, an operational SM-DA scheme was set up in the poorly monitored, large (>40,000 km2), semi-arid Warrego catchment situated in eastern Australia. We assimilated passive and active SSM products into the probability distributed model (PDM) using an ensemble Kalman filter. We explored factors influencing the SM-DA framework, including relatively new techniques to remove model-observation bias, estimate observation errors and represent model errors. Furthermore, we explored the advantages of accounting for the spatial distribution of forcing and channel routing processes within the catchment by implementing and comparing lumped and semi-distributed model setups. Flood prediction is improved by SM-DA (Figure), with a 30% reduction of the average root-mean-squared difference of the ensemble prediction, a 20% reduction of the false alarm ratio and a 40% increase of the ensemble mean Nash-Sutcliffe efficiency. SM-DA skill does not significantly change with different observation error assumptions, but the skill strongly depends on the observational bias correction technique used, and more importantly, on the performance of the open-loop model before assimilation. Our findings imply that proper

  20. 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

  1. Contact parameter identification for vibrational response variability prediction

    DEFF Research Database (Denmark)

    Creixell Mediante, Ester; Brunskog, Jonas; Jensen, Jakob Søndergaard

    2018-01-01

    industry, where the vibrational behavior of the structures within the hearing frequency range is critical for the performance of the devices. A procedure to localize the most probable contact areas and determine the most sensitive contact points with respect to variations in the modes of vibration......Variability in the dynamic response of assembled structures can arise due to variations in the contact conditions between the parts that conform them. Contact conditions are difficult to model accurately due to randomness in physical properties such as contact surface, load distribution...... or geometric details. Those properties can vary for a given structure due to the assembly and disassembly process, and also across nominally equal items that are produced in series. This work focuses on modeling the contact between small light-weight plastic pieces such as those used in the hearing aid...

  2. Prediction of response to interferon therapy in multiple sclerosis

    DEFF Research Database (Denmark)

    Sellebjerg, F; Søndergaard, Helle Bach; Koch-Henriksen, N

    2014-01-01

    OBJECTIVE: Single nucleotide polymorphisms (SNPs) in the genes encoding interferon response factor (IRF)-5, IRF-8 and glypican-5 (GPC5) have been associated with disease activity in multiple sclerosis (MS) patients treated with interferon (IFN)-β. We analysed whether SNPs in the IRF5, IRF8 and GPC5...... genes are associated with clinical disease activity in MS patients beginning de novo treatment with IFN-β. METHODS: The SNPs rs2004640, rs3807306 and rs4728142 in IRF5, rs13333054 and rs17445836 in IRF8 and rs10492503 in GPC5 were genotyped in 575 patients with relapsing-remitting MS followed...... prospectively after the initiation of their first treatment with IFN-β. RESULTS: 62% of patients experienced relapses during the first 2 years of treatment, and 32% had disability progression during the first 5 years of treatment. Patients with a pretreatment annualized relapse rate >1 had an increased risk...

  3. Predicting the Response of Electricity Load to Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Sullivan, Patrick [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Colman, Jesse [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kalendra, Eric [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2015-07-28

    Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

  4. Dexamethasone-suppressed cortisol awakening response predicts treatment outcome in posttraumatic stress disorder

    NARCIS (Netherlands)

    Nijdam, M. J.; van Amsterdam, J. G. C.; Gersons, B. P. R.; Olff, M.

    2015-01-01

    Posttraumatic stress disorder (PTSD) has been associated with several alterations in the neuroendocrine system, including enhanced cortisol suppression in response to the dexamethasone suppression test. The aim of this study was to examine whether specific biomarkers of PTSD predict treatment

  5. A noise level prediction method based on electro-mechanical frequency response function for capacitors.

    Science.gov (United States)

    Zhu, Lingyu; Ji, Shengchang; Shen, Qi; Liu, Yuan; Li, Jinyu; Liu, Hao

    2013-01-01

    The capacitors in high-voltage direct-current (HVDC) converter stations radiate a lot of audible noise which can reach higher than 100 dB. The existing noise level prediction methods are not satisfying enough. In this paper, a new noise level prediction method is proposed based on a frequency response function considering both electrical and mechanical characteristics of capacitors. The electro-mechanical frequency response function (EMFRF) is defined as the frequency domain quotient of the vibration response and the squared capacitor voltage, and it is obtained from impulse current experiment. Under given excitations, the vibration response of the capacitor tank is the product of EMFRF and the square of the given capacitor voltage in frequency domain, and the radiated audible noise is calculated by structure acoustic coupling formulas. The noise level under the same excitations is also measured in laboratory, and the results are compared with the prediction. The comparison proves that the noise prediction method is effective.

  6. Predicting Emotional Responses to Horror Films from Cue-Specific Affect.

    Science.gov (United States)

    Neuendorf, Kimberly A.; Sparks, Glenn G.

    1988-01-01

    Assesses individuals' fear and enjoyment reactions to horror films, applying theories of cognition and affect that predict emotional responses to a stimulus on the basis of prior affect toward specific cues included in that stimulus. (MM)

  7. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    Science.gov (United States)

    Holmlund, A; Lampa, E; Lind, L

    2017-07-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P 4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  8. When bad stress goes good: increased threat reactivity predicts improved category learning performance.

    Science.gov (United States)

    Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

    2011-02-01

    The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

  9. Assessment of Predictive Markers of Response to Neoadjuvant Chemotherapy in Breast Cancer

    Directory of Open Access Journals (Sweden)

    Mallika Tewari

    2010-10-01

    Conclusion: Of all parameters examined, only the apoptosis-related genes (Bcl-2 and BAX seemed to exert some influence on the response to NACT, and neither by itself was sufficient to predict pCR; however, 50 patients is not sufficient to simultaneously analyse several predictive markers.

  10. Auditory Brainstem Response to Complex Sounds Predicts Self-Reported Speech-in-Noise Performance

    Science.gov (United States)

    Anderson, Samira; Parbery-Clark, Alexandra; White-Schwoch, Travis; Kraus, Nina

    2013-01-01

    Purpose: To compare the ability of the auditory brainstem response to complex sounds (cABR) to predict subjective ratings of speech understanding in noise on the Speech, Spatial, and Qualities of Hearing Scale (SSQ; Gatehouse & Noble, 2004) relative to the predictive ability of the Quick Speech-in-Noise test (QuickSIN; Killion, Niquette,…

  11. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    Science.gov (United States)

    Lawrence N. Hudson; Joseph Wunderle M.; And Others

    2016-01-01

    The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to...

  12. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    NARCIS (Netherlands)

    Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Alhusseini, Tamera I; Bedford, Felicity E; Bennett, Dominic J; Booth, Hollie; Burton, Victoria J; Chng, Charlotte W T; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Emerson, Susan R; Gao, Di; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; Pask-Hale, Gwilym D; Pynegar, Edwin L; Robinson, Alexandra N; Sanchez-Ortiz, Katia; Senior, Rebecca A; Simmons, Benno I; White, Hannah J; Zhang, Hanbin; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Albertos, Belén; Alcala, E L; Del Mar Alguacil, Maria; Alignier, Audrey; Ancrenaz, Marc; Andersen, Alan N; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Arroyo-Rodríguez, Víctor; Aumann, Tom; Axmacher, Jan C; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Bakayoko, Adama; Báldi, András; Banks, John E; Baral, Sharad K; Barlow, Jos; Barratt, Barbara I P; Barrico, Lurdes; Bartolommei, Paola; Barton, Diane M; Basset, Yves; Batáry, Péter; Bates, Adam J; Baur, Bruno; Bayne, Erin M; Beja, Pedro; Benedick, Suzan; Berg, Åke; Bernard, Henry; Berry, Nicholas J; Bhatt, Dinesh; Bicknell, Jake E; Bihn, Jochen H; Blake, Robin J; Bobo, Kadiri S; Bóçon, Roberto; Boekhout, Teun; Böhning-Gaese, Katrin; Bonham, Kevin J; Borges, Paulo A V; Borges, Sérgio H; Boutin, Céline; Bouyer, Jérémy; Bragagnolo, Cibele; Brandt, Jodi S; Brearley, Francis Q; Brito, Isabel; Bros, Vicenç; Brunet, Jörg; Buczkowski, Grzegorz; Buddle, Christopher M; Bugter, Rob; Buscardo, Erika; Buse, Jörn; Cabra-García, Jimmy; Cáceres, Nilton C; Cagle, Nicolette L; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Caparrós, Rut; Cardoso, Pedro; Carpenter, Dan; Carrijo, Tiago F; Carvalho, Anelena L; Cassano, Camila R; Castro, Helena; Castro-Luna, Alejandro A; Rolando, Cerda B; Cerezo, Alexis; Chapman, Kim Alan; Chauvat, Matthieu; Christensen, Morten; Clarke, Francis M; Cleary, Daniel F R; Colombo, Giorgio; Connop, Stuart P; Craig, Michael D; Cruz-López, Leopoldo; Cunningham, Saul A; D'Aniello, Biagio; D'Cruze, Neil; da Silva, Pedro Giovâni; Dallimer, Martin; Danquah, Emmanuel; Darvill, Ben; Dauber, Jens; Davis, Adrian L V; Dawson, Jeff; de Sassi, Claudio; de Thoisy, Benoit; Deheuvels, Olivier; Dejean, Alain; Devineau, Jean-Louis; Diekötter, Tim; Dolia, Jignasu V; Domínguez, Erwin; Dominguez-Haydar, Yamileth; Dorn, Silvia; Draper, Isabel; Dreber, Niels; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Eggleton, Paul; Eigenbrod, Felix; Elek, Zoltán; Entling, Martin H; Esler, Karen J; de Lima, Ricardo F; Faruk, Aisyah; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Fensham, Roderick J; Fernandez, Ignacio C; Ferreira, Catarina C; Ficetola, Gentile F; Fiera, Cristina; Filgueiras, Bruno K C; Fırıncıoğlu, Hüseyin K; Flaspohler, David; Floren, Andreas; Fonte, Steven J; Fournier, Anne; Fowler, Robert E; Franzén, Markus; Fraser, Lauchlan H; Fredriksson, Gabriella M; Freire, Geraldo B; Frizzo, Tiago L M; Fukuda, Daisuke; Furlani, Dario; Gaigher, René; Ganzhorn, Jörg U; García, Karla P; Garcia-R, Juan C; Garden, Jenni G; Garilleti, Ricardo; Ge, Bao-Ming; Gendreau-Berthiaume, Benoit; Gerard, Philippa J; Gheler-Costa, Carla; Gilbert, Benjamin; Giordani, Paolo; Giordano, Simonetta; Golodets, Carly; Gomes, Laurens G L; Gould, Rachelle K; Goulson, Dave; Gove, Aaron D; Granjon, Laurent; Grass, Ingo; Gray, Claudia L; Grogan, James; Gu, Weibin; Guardiola, Moisès; Gunawardene, Nihara R; Gutierrez, Alvaro G; Gutiérrez-Lamus, Doris L; Haarmeyer, Daniela H; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hassan, Shombe N; Hatfield, Richard G; Hawes, Joseph E; Hayward, Matt W; Hébert, Christian; Helden, Alvin J; Henden, John-André; Henschel, Philipp; Hernández, Lionel; Herrera, James P; Herrmann, Farina; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Höfer, Hubert; Hoffmann, Anke; Horgan, Finbarr G; Hornung, Elisabeth; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishida, Hiroaki; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Hernández, F Jiménez; Johnson, McKenzie F; Jolli, Virat; Jonsell, Mats; Juliani, S Nur; Jung, Thomas S; Kapoor, Vena; Kappes, Heike; Kati, Vassiliki; Katovai, Eric; Kellner, Klaus; Kessler, Michael; Kirby, Kathryn R; Kittle, Andrew M; Knight, Mairi E; Knop, Eva; Kohler, Florian; Koivula, Matti; Kolb, Annette; Kone, Mouhamadou; Kőrösi, Ádám; Krauss, Jochen; Kumar, Ajith; Kumar, Raman; Kurz, David J; Kutt, Alex S; Lachat, Thibault; Lantschner, Victoria; Lara, Francisco; Lasky, Jesse R; Latta, Steven C; Laurance, William F; Lavelle, Patrick; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Lehouck, Valérie; Lencinas, María V; Lentini, Pia E; Letcher, Susan G; Li, Qi; Litchwark, Simon A; Littlewood, Nick A; Liu, Yunhui; Lo-Man-Hung, Nancy; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Luskin, Matthew S; MacSwiney G, M Cristina; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Malone, Louise A; Malonza, Patrick K; Malumbres-Olarte, Jagoba; Mandujano, Salvador; Måren, Inger E; Marin-Spiotta, Erika; Marsh, Charles J; Marshall, E J P; Martínez, Eliana; Martínez Pastur, Guillermo; Moreno Mateos, David; Mayfield, Margaret M; Mazimpaka, Vicente; McCarthy, Jennifer L; McCarthy, Kyle P; McFrederick, Quinn S; McNamara, Sean; Medina, Nagore G; Medina, Rafael; Mena, Jose L; Mico, Estefania; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Miranda-Esquivel, Daniel R; Moir, Melinda L; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Mudri-Stojnic, Sonja; Munira, A Nur; Muoñz-Alonso, Antonio; Munyekenye, B F; Naidoo, Robin; Naithani, A; Nakagawa, Michiko; Nakamura, Akihiro; Nakashima, Yoshihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Navarro-Iriarte, Luis; Ndang'ang'a, Paul K; Neuschulz, Eike L; Ngai, Jacqueline T; Nicolas, Violaine; Nilsson, Sven G; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Norton, David A; Nöske, Nicole M; Nowakowski, A Justin; Numa, Catherine; O'Dea, Niall; O'Farrell, Patrick J; Oduro, William; Oertli, Sabine; Ofori-Boateng, Caleb; Oke, Christopher Omamoke; Oostra, Vicencio; Osgathorpe, Lynne M; Otavo, Samuel Eduardo; Page, Navendu V; Paritsis, Juan; Parra-H, Alejandro; Parry, Luke; Pe'er, Guy; Pearman, Peter B; Pelegrin, Nicolás; Pélissier, Raphaël; Peres, Carlos A; Peri, Pablo L; Persson, Anna S; Petanidou, Theodora; Peters, Marcell K; Pethiyagoda, Rohan S; Phalan, Ben; Philips, T Keith; Pillsbury, Finn C; Pincheira-Ulbrich, Jimmy; Pineda, Eduardo; Pino, Joan; Pizarro-Araya, Jaime; Plumptre, A J; Poggio, Santiago L; Politi, Natalia; Pons, Pere; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Ramesh, B R; Ramirez-Pinilla, Martha P; Ranganathan, Jai; Rasmussen, Claus; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Rey Benayas, José M; Rey-Velasco, Juan Carlos; Reynolds, Chevonne; Ribeiro, Danilo Bandini; Richards, Miriam H; Richardson, Barbara A; Richardson, Michael J; Ríos, Rodrigo Macip; Robinson, Richard; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rös, Matthias; Rosselli, Loreta; Rossiter, Stephen J; Roth, Dana S; Roulston, T'ai H; Rousseau, Laurent; Rubio, André V; Ruel, Jean-Claude; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Sam, Katerina; Samnegård, Ulrika; Santana, Joana; Santos, Xavier; Savage, Jade; Schellhorn, Nancy A; Schilthuizen, Menno; Schmiedel, Ute; Schmitt, Christine B; Schon, Nicole L; Schüepp, Christof; Schumann, Katharina; Schweiger, Oliver; Scott, Dawn M; Scott, Kenneth A; Sedlock, Jodi L; Seefeldt, Steven S; Shahabuddin, Ghazala; Shannon, Graeme; Sheil, Douglas; Sheldon, Frederick H; Shochat, Eyal; Siebert, Stefan J; Silva, Fernando A B; Simonetti, Javier A; Slade, Eleanor M; Smith, Jo; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Soto Quiroga, Grimaldo; St-Laurent, Martin-Hugues; Starzomski, Brian M; Stefanescu, Constanti; Steffan-Dewenter, Ingolf; Stouffer, Philip C; Stout, Jane C; Strauch, Ayron M; Struebig, Matthew J; Su, Zhimin; Suarez-Rubio, Marcela; Sugiura, Shinji; Summerville, Keith S; Sung, Yik-Hei; Sutrisno, Hari; Svenning, Jens-Christian; Teder, Tiit; Threlfall, Caragh G; Tiitsaar, Anu; Todd, Jacqui H; Tonietto, Rebecca K; Torre, Ignasi; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Uehara-Prado, Marcio; Urbina-Cardona, Nicolas; Vallan, Denis; Vanbergen, Adam J; Vasconcelos, Heraldo L; Vassilev, Kiril; Verboven, Hans A F; Verdasca, Maria João; Verdú, José R; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Virgilio, Massimiliano; Vu, Lien Van; Waite, Edward M; Walker, Tony R; Wang, Hua-Feng; Wang, Yanping; Watling, James I; Weller, Britta; Wells, Konstans; Westphal, Catrin; Wiafe, Edward D; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Wolters, Volkmar; Woodcock, Ben A; Wu, Jihua; Wunderle, Joseph M; Yamaura, Yuichi; Yoshikura, Satoko; Yu, Douglas W; Zaitsev, Andrey S; Zeidler, Juliane; Zou, Fasheng; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of

  13. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    DEFF Research Database (Denmark)

    Hudson, Lawrence N; Newbold, Tim; Contu, Sara

    2017-01-01

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity ...

  14. 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.

  15. 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.

  16. Pharmacogenetic approaches to the prediction of drug response

    International Nuclear Information System (INIS)

    Vesell, E.S.

    1986-01-01

    The following review of pharmacogenetic progress and methodology is offered to stimulate and suggest analogous studies on drugs of abuse. It is readily acknowledged that formidable methodological problems are posed by adapting to drugs of abuse these pharmacogenetic approaches based on the administration of single safe doses of various prescription drugs to normal subjects under carefully controlled environmental conditions. Results of similarly designed studies on drugs of abuse in addicts might be uninterpretable because of confounding by numerous environmental perturbations, including the smoking of cigarettes and/or marijuana, nutritional variations, and intake of other drugs such as ethanol. Ethical considerations render objectionable the administration to unaddicted subjects of drugs at dosage levels usually ingested by drug abusers. Other approaches would have to be taken in such normal subjects. Possibilities include administration of tracer doses of /sup 14/C- or /sup 13/C- labeled drugs or growth of normal cells in culture to investigate their pharmacokinetic and/or pharmacodynamic responses to various drugs of abuse

  17. Class and compassion: socioeconomic factors predict responses to suffering.

    Science.gov (United States)

    Stellar, Jennifer E; Manzo, Vida M; Kraus, Michael W; Keltner, Dacher

    2012-06-01

    Previous research indicates that lower-class individuals experience elevated negative emotions as compared with their upper-class counterparts. We examine how the environments of lower-class individuals can also promote greater compassionate responding-that is, concern for the suffering or well-being of others. In the present research, we investigate class-based differences in dispositional compassion and its activation in situations wherein others are suffering. Across studies, relative to their upper-class counterparts, lower-class individuals reported elevated dispositional compassion (Study 1), as well as greater self-reported compassion during a compassion-inducing video (Study 2) and for another person during a social interaction (Study 3). Lower-class individuals also exhibited heart rate deceleration-a physiological response associated with orienting to the social environment and engaging with others-during the compassion-inducing video (Study 2). We discuss a potential mechanism of class-based influences on compassion, whereby lower-class individuals' are more attuned to others' distress, relative to their upper-class counterparts.

  18. Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis.

    Science.gov (United States)

    Cheong, Jae-Ho; Yang, Han-Kwang; Kim, Hyunki; Kim, Woo Ho; Kim, Young-Woo; Kook, Myeong-Cherl; Park, Young-Kyu; Kim, Hyung-Ho; Lee, Hye Seung; Lee, Kyung Hee; Gu, Mi Jin; Kim, Ha Yan; Lee, Jinae; Choi, Seung Ho; Hong, Soonwon; Kim, Jong Won; Choi, Yoon Young; Hyung, Woo Jin; Jang, Eunji; Kim, Hyeseon; Huh, Yong-Min; Noh, Sung Hoon

    2018-05-01

    Adjuvant chemotherapy after surgery improves survival of patients with stage II-III, resectable gastric cancer. However, the overall survival benefit observed after adjuvant chemotherapy is moderate, suggesting that not all patients with resectable gastric cancer treated with adjuvant chemotherapy benefit from it. We aimed to develop and validate a predictive test for adjuvant chemotherapy response in patients with resectable, stage II-III gastric cancer. In this multi-cohort, retrospective study, we developed through a multi-step strategy a predictive test consisting of two rule-based classifier algorithms with predictive value for adjuvant chemotherapy response and prognosis. Exploratory bioinformatics analyses identified biologically relevant candidate genes in gastric cancer transcriptome datasets. In the discovery analysis, a four-gene, real-time RT-PCR assay was developed and analytically validated in formalin-fixed, paraffin-embedded (FFPE) tumour tissues from an internal cohort of 307 patients with stage II-III gastric cancer treated at the Yonsei Cancer Center with D2 gastrectomy plus adjuvant fluorouracil-based chemotherapy (n=193) or surgery alone (n=114). The same internal cohort was used to evaluate the prognostic and chemotherapy response predictive value of the single patient classifier genes using associations with 5-year overall survival. The results were validated with a subset (n=625) of FFPE tumour samples from an independent cohort of patients treated in the CLASSIC trial (NCT00411229), who received D2 gastrectomy plus capecitabine and oxaliplatin chemotherapy (n=323) or surgery alone (n=302). The primary endpoint was 5-year overall survival. We identified four classifier genes related to relevant gastric cancer features (GZMB, WARS, SFRP4, and CDX1) that formed the single patient classifier assay. In the validation cohort, the prognostic single patient classifier (based on the expression of GZMB, WARS, and SFRP4) identified 79 (13%) of 625

  19. 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.

  20. 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.

  1. 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.

  2. Prediction of transcriptional regulatory elements for plant hormone responses based on microarray data

    Directory of Open Access Journals (Sweden)

    Yamaguchi-Shinozaki Kazuko

    2011-02-01

    Full Text Available Abstract Background Phytohormones organize plant development and environmental adaptation through cell-to-cell signal transduction, and their action involves transcriptional activation. Recent international efforts to establish and maintain public databases of Arabidopsis microarray data have enabled the utilization of this data in the analysis of various phytohormone responses, providing genome-wide identification of promoters targeted by phytohormones. Results We utilized such microarray data for prediction of cis-regulatory elements with an octamer-based approach. Our test prediction of a drought-responsive RD29A promoter with the aid of microarray data for response to drought, ABA and overexpression of DREB1A, a key regulator of cold and drought response, provided reasonable results that fit with the experimentally identified regulatory elements. With this succession, we expanded the prediction to various phytohormone responses, including those for abscisic acid, auxin, cytokinin, ethylene, brassinosteroid, jasmonic acid, and salicylic acid, as well as for hydrogen peroxide, drought and DREB1A overexpression. Totally 622 promoters that are activated by phytohormones were subjected to the prediction. In addition, we have assigned putative functions to 53 octamers of the Regulatory Element Group (REG that have been extracted as position-dependent cis-regulatory elements with the aid of their feature of preferential appearance in the promoter region. Conclusions Our prediction of Arabidopsis cis-regulatory elements for phytohormone responses provides guidance for experimental analysis of promoters to reveal the basis of the transcriptional network of phytohormone responses.

  3. Characterization of DNAPL Source Zone Architecture and Prediction of Associated Plume Response: Progress and Perspectives

    Science.gov (United States)

    Abriola, L. M.; Pennell, K. D.; Ramsburg, C. A.; Miller, E. L.; Christ, J.; Capiro, N. L.; Mendoza-Sanchez, I.; Boroumand, A.; Ervin, R. E.; Walker, D. I.; Zhang, H.

    2012-12-01

    It is now widely recognized that the distribution of contaminant mass will control both the evolution of aqueous phase plumes and the effectiveness of many source zone remediation technologies at sites contaminated by dense nonaqueous phase liquids (DNAPLs). Advances in the management of sites containing DNAPL source zones, however, are currently hampered by the difficulty associated with characterizing subsurface DNAPL 'architecture'. This presentation provides an overview of recent research, integrating experimental and mathematical modeling studies, designed to improve our ability to characterize DNAPL distributions and predict associated plume response. Here emphasis is placed on estimation of the most information-rich DNAPL architecture metrics, through a combination of localized in situ tests and more readily available plume transect concentration observations. Estimated metrics will then serve as inputs to an upscaled screening model for prediction of long term plume response. Machine learning techniques were developed and refined to identify a variety of source zone metrics and associated confidence intervals through the processing of down gradient concentration data. Estimated metrics include the volumes and volume percentages of DNAPL in pools and ganglia, as well as their ratio (pool fraction). Multiphase flow and transport simulations provided training data for model development and assessment that are representative of field-scale DNAPL source zones and their evolving plumes. Here, a variety of release and site heterogeneity (sequential Gaussian permeability) conditions were investigated. Push-pull tracer tests were also explored as a means to provide localized in situ observations to refine these metric estimates. Here, two-dimensional aquifer cell experiments and mathematical modeling were used to quantify upscaled interphase mass transfer rates and the interplay between injection and extraction rates, local source zone architecture, and tracer

  4. DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

    International Nuclear Information System (INIS)

    Alexander, Brian M.; Wang Xiaozhe; Niemierko, Andrzej; Weaver, David T.; Mak, Raymond H.; Roof, Kevin S.; Fidias, Panagiotis; Wain, John; Choi, Noah C.

    2012-01-01

    Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, with biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need

  5. Characterizing haploinsufficiency of SHELL gene to improve fruit form prediction in introgressive hybrids of oil palm.

    Science.gov (United States)

    Teh, Chee-Keng; Muaz, Siti Dalila; Tangaya, Praveena; Fong, Po-Yee; Ong, Ai-Ling; Mayes, Sean; Chew, Fook-Tim; Kulaveerasingam, Harikrishna; Appleton, David

    2017-06-08

    The fundamental trait in selective breeding of oil palm (Eleais guineensis Jacq.) is the shell thickness surrounding the kernel. The monogenic shell thickness is inversely correlated to mesocarp thickness, where the crude palm oil accumulates. Commercial thin-shelled tenera derived from thick-shelled dura × shell-less pisifera generally contain 30% higher oil per bunch. Two mutations, sh MPOB (M1) and sh AVROS (M2) in the SHELL gene - a type II MADS-box transcription factor mainly present in AVROS and Nigerian origins, were reported to be responsible for different fruit forms. In this study, we have tested 1,339 samples maintained in Sime Darby Plantation using both mutations. Five genotype-phenotype discrepancies and eight controls were then re-tested with all five reported mutations (sh AVROS , sh MPOB , sh MPOB2 , sh MPOB3 and sh MPOB4 ) within the same gene. The integration of genotypic data, pedigree records and shell formation model further explained the haploinsufficiency effect on the SHELL gene with different number of functional copies. Some rare mutations were also identified, suggesting a need to further confirm the existence of cis-compound mutations in the gene. With this, the prediction accuracy of fruit forms can be further improved, especially in introgressive hybrids of oil palm. Understanding causative variant segregation is extremely important, even for monogenic traits such as shell thickness in oil palm.

  6. Immunophenotyping does not improve predictivity of the local lymph node assay in mice.

    Science.gov (United States)

    Strauss, Volker; Kolle, Susanne N; Honarvar, Naveed; Dammann, Martina; Groeters, Sibylle; Faulhammer, Frank; Landsiedel, Robert; van Ravenzwaay, Bennard

    2015-04-01

    The local lymph node assay (LLNA) is a regulatory accepted test for the identification of skin sensitizing substances by measuring radioactive thymidine incorporation into the lymph node. However, there is evidence that LLNA is overestimating the sensitization potential of certain substance classes in particular those exerting skin irritation. Some reports describe the additional use of flow cytometry-based immunophenotyping to better discriminate irritants from sensitizing irritants in LLNA. In the present study, the 22 performance standards plus 8 surfactants were assessed using the radioactive LLNA method. In addition, lymph node cells were immunophenotyped to evaluate the specificity of the lymph node response using cell surface markers such as B220 or CD19, CD3, CD4, CD8, I-A(κ) and CD69 with the aim to allow a better discrimination above all between irritants and sensitizers, but also non-irritating sensitizers and non-sensitizers. However, the markers assessed in this study do not sufficiently differentiate between irritants and irritant sensitizers and therefore did not improve the predictive capacity of the LLNA. Copyright © 2014 John Wiley & Sons, Ltd.

  7. A predictive control framework for torque-based steering assistance to improve safety in highway driving

    Science.gov (United States)

    Ercan, Ziya; Carvalho, Ashwin; Tseng, H. Eric; Gökaşan, Metin; Borrelli, Francesco

    2018-05-01

    Haptic shared control framework opens up new perspectives on the design and implementation of the driver steering assistance systems which provide torque feedback to the driver in order to improve safety. While designing such a system, it is important to account for the human-machine interactions since the driver feels the feedback torque through the hand wheel. The controller should consider the driver's impact on the steering dynamics to achieve a better performance in terms of driver's acceptance and comfort. In this paper we present a predictive control framework which uses a model of driver-in-the-loop steering dynamics to optimise the torque intervention with respect to the driver's neuromuscular response. We first validate the system in simulations to compare the performance of the controller in nominal and model mismatch cases. Then we implement the controller in a test vehicle and perform experiments with a human driver. The results show the effectiveness of the proposed system in avoiding hazardous situations under different driver behaviours.

  8. Prediction of heat-illness symptoms with the prediction of human vascular response in hot environment under resting condition.

    Science.gov (United States)

    Aggarwal, Yogender; Karan, Bhuwan Mohan; Das, Barsa Nand; Sinha, Rakesh Kumar

    2008-04-01

    The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in degrees C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred.

  9. Breast Cancer Spatial Heterogeneity in Near-Infrared Spectra and the Prediction of Neoadjuvant Chemotherapy Response

    Science.gov (United States)

    Santoro, Ylenia

    Breast cancer accounts for more than 20% of all female cancers. Many of these patients receive neoadjuvant chemotherapy (NAC) to reduce the size of the tumor before surgery and to anticipate the efficacy of treatments for after the procedure. Breast cancer is a heterogeneous disease that comes in several clinical and histological forms. The prediction of the efficacy of chemotherapy would potentially select good candidates who would respond while excluding poor candidates who would not benefit from treatment. In this work we investigate the possibility of noninvasively predicting chemotherapy response prior to treatment based on optical biomarkers obtained from tumor spatial heterogeneities of spectral features measured using Diffuse Optical Spectroscopy. We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a Heterogeneity Spectrum (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a Heterogeneity Index (HI) from the HS by weighing specific absorption bands. It has been shown that NAC response is potentially related to tumor heterogeneity. Therefore, we correlate the HI obtained prior to treatment with the final response to NAC. In this thesis we also present a novel digital parallel frequency domain system for tissue imaging. The systems employs a supercontinuum laser with high brightness, and a photomultiplier with a large detection area, both allowing a deep penetration with extremely low power on the sample. The digital parallel acquisition is performed through the use of the Flimbox and it decreases the time required for standard serial systems that need to scan through all modulation frequencies. The all-digital acquisition removes analog noise, avoids the analog mixer and it does not

  10. A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

    Science.gov (United States)

    Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

    2018-02-01

    Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

  11. Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

    Science.gov (United States)

    DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

    2017-06-01

    Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

  12. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change.

    Science.gov (United States)

    Mitchell, Duncan; Snelling, Edward P; Hetem, Robyn S; Maloney, Shane K; Strauss, Willem Maartin; Fuller, Andrea

    2018-02-26

    The accuracy of predictive models (also known as mechanistic or causal models) of animal responses to climate change depends on properly incorporating the principles of heat transfer and thermoregulation into those models. Regrettably, proper incorporation of these principles is not always evident. We have revisited the relevant principles of thermal physiology and analysed how they have been applied in predictive models of large mammals, which are particularly vulnerable, to climate change. We considered dry heat exchange, evaporative heat transfer, the thermoneutral zone and homeothermy, and we examined the roles of size and shape in the thermal physiology of large mammals. We report on the following misconceptions in influential predictive models: underestimation of the role of radiant heat transfer, misassignment of the role and misunderstanding of the sustainability of evaporative cooling, misinterpretation of the thermoneutral zone as a zone of thermal tolerance or as a zone of sustainable energetics, confusion of upper critical temperature and critical thermal maximum, overestimation of the metabolic energy cost of evaporative cooling, failure to appreciate that the current advantages of size and shape will become disadvantageous as climate change advances, misassumptions about skin temperature and, lastly, misconceptions about the relationship between body core temperature and its variability with body mass in large mammals. Not all misconceptions invalidate the models, but we believe that preventing inappropriate assumptions from propagating will improve model accuracy, especially as models progress beyond their current typically static format to include genetic and epigenetic adaptation that can result in phenotypic plasticity. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

  13. Nerve Ultrasound Predicts Treatment Response in Chronic Inflammatory Demyelinating Polyradiculoneuropathy-a Prospective Follow-Up.

    Science.gov (United States)

    Härtig, Florian; Ross, Marlene; Dammeier, Nele Maria; Fedtke, Nadin; Heiling, Bianka; Axer, Hubertus; Décard, Bernhard F; Auffenberg, Eva; Koch, Marilin; Rattay, Tim W; Krumbholz, Markus; Bornemann, Antje; Lerche, Holger; Winter, Natalie; Grimm, Alexander

    2018-04-01

    As reliable biomarkers of disease activity are lacking, monitoring of therapeutic response in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) remains a challenge. We sought to determine whether nerve ultrasound and electrophysiology scoring could close this gap. In CIDP patients (fulfilling EFNS/PNS criteria), we performed high-resolution nerve ultrasound to determine ultrasound pattern sum scores (UPSS) and predominant echotexture nerve conduction study scores (NCSS) as well as Medical Research Council sum scores (MRCSS) and inflammatory neuropathy cause and treatment disability scores (INCAT) at baseline and after 12 months of standard treatment. We retrospectively correlated ultrasound morphology with nerve histology when available. 72/80 CIDP patients featured multifocal nerve enlargement, and 35/80 were therapy-naïve. At baseline, clinical scores correlated with NCSS (r 2  = 0.397 and r 2  = 0.443, p  50% of measured segments, possibly reflecting axonal degeneration; and 3) almost no enlargement, reflecting "burned-out" or "cured" disease without active inflammation. Clinical improvement after 12 months was best in patients with pattern 1 (up to 75% vs up to 43% in pattern 2/3, Fisher's exact test p < 0.05). Nerve ultrasound has additional value not only for diagnosis, but also for classification of disease state and may predict treatment response.

  14. 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

  15. 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.

  16. Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

    Science.gov (United States)

    Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

    2017-05-31

    Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

  17. Response surface analysis to improve dispersed crude oil biodegradation

    Energy Technology Data Exchange (ETDEWEB)

    Zahed, Mohammad A.; Aziz, Hamidi A.; Mohajeri, Leila [School of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang (Malaysia); Isa, Mohamed H. [Civil Engineering Department, Universiti Teknologi PETRONAS, Tronoh, Perak (Malaysia)

    2012-03-15

    In this research, the bioremediation of dispersed crude oil, based on the amount of nitrogen and phosphorus supplementation in the closed system, was optimized by the application of response surface methodology and central composite design. Correlation analysis of the mathematical-regression model demonstrated that a quadratic polynomial model could be used to optimize the hydrocarbon bioremediation (R{sup 2} = 0.9256). Statistical significance was checked by analysis of variance and residual analysis. Natural attenuation was removed by 22.1% of crude oil in 28 days. The highest removal on un-optimized condition of 68.1% were observed by using nitrogen of 20.00 mg/L and phosphorus of 2.00 mg/L in 28 days while optimization process exhibited a crude oil removal of 69.5% via nitrogen of 16.05 mg/L and phosphorus 1.34 mg/L in 27 days therefore optimization can improve biodegradation in shorter time with less nutrient consumption. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  18. Accounting for Landscape Heterogeneity Improves Spatial Predictions of Tree Vulnerability to Drought

    Science.gov (United States)

    Schwantes, A. M.; Parolari, A.; Swenson, J. J.; Johnson, D. M.; Domec, J. C.; Jackson, R. B.; Pelak, N. F., III; Porporato, A. M.

    2017-12-01

    Globally, as climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability differs regionally and locally depending on landscape position. However, most models used in forecasting forest responses to heatwaves and droughts do not incorporate relevant spatial processes. To improve predictions of spatial tree vulnerability, we employed a non-linear stochastic model of soil moisture dynamics across a landscape, accounting for spatial differences in aspect, topography, and soils. Our unique approach integrated plant hydraulics and landscape processes, incorporating effects from lateral redistribution of water using a topographic index and radiation and temperature differences attributable to aspect. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei. We compared our results to a detailed spatial dataset of drought-impacted areas (>25% canopy loss) derived from remote sensing during the severe 2011 drought. We then projected future dynamic water stress through the 21st century using climate projections from 10 global climate models under two scenarios, and compared models with and without landscape heterogeneity. Within this watershed, 42% of J. ashei dominated systems were impacted by the 2011 drought. Modeled dynamic water stress tracked these spatial patterns of observed drought-impacted areas. Total accuracy increased from 59%, when accounting only for soil variability, to 73% when including lateral redistribution of water and radiation and temperature effects. Dynamic water stress was projected to increase through the 21st century, with only minimal buffering from the landscape. During the hotter and more severe droughts projected in the 21st century, up to 90% of the watershed crossed a dynamic water stress threshold associated with canopy loss in 2011. Favorable microsites may exist across a landscape where trees can persist; however, if future droughts are

  19. Demographic and phenotypic responses of juvenile steelhead trout to spatial predictability of food resources

    Science.gov (United States)

    Matthew R. Sloat; Gordon H. Reeves

    2014-01-01

    We manipulated food inputs among patches within experimental streams to determine how variation in foraging behavior influenced demographic and phenotypic responses of juvenile steelhead trout (Oncorhynchus mykiss) to the spatial predictability of food resources. Demographic responses included compensatory adjustments in fish abundance, mean fish...

  20. Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

    Energy Technology Data Exchange (ETDEWEB)

    Gutowski, William J. [Iowa State Univ., Ames, IA (United States)

    2017-12-28

    This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASM can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are: - Changes

  1. A prospective study on personality and the cortisol awakening response to predict posttraumatic stress symptoms in response to military deployment

    NARCIS (Netherlands)

    van Zuiden, Mirjam; Kavelaars, Annemieke; Rademaker, Arthur R.; Vermetten, Eric; Heijnen, Cobi J.; Geuze, Elbert

    2011-01-01

    Few prospective studies on pre-trauma predictors for subsequent development of posttraumatic stress disorder (PTSD) have been conducted. In this study we prospectively investigated whether pre-deployment personality and the cortisol awakening response (CAR) predicted development of PTSD symptoms in

  2. 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.

  3. A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

    Science.gov (United States)

    2017-02-01

    affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP

  4. 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.

  5. 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.

  6. MTR-18 Predictive Biomarkers Of Bevacizumab Response In Recurrent Glioblastoma Patients

    DEFF Research Database (Denmark)

    Urup, Thomas; Michaelsen, Signe Regner; Olsen, Lars Rønn

    2015-01-01

    Bevacizumab (BEV) plus chemotherapy has shown activity in recurrent glioblastoma (GBM). However, the prognosis varies and only one third of patients have a durable clinical response to BEV combination therapy. Recent findings from a randomized phase-3 study (AVAglio) indicate that patients...... with the proneural GBM subtype have a survival benefit when treated with BEV in combination with standard treatment. However, no validated biomarkers able to predict BEV response have been identified and the biology reflecting a clinical BEV response is poorly understood. The primary objective of this study...... was to evaluate the predictive and prognostic value of GBM subtypes in recurrent GBM patients treated with BEV therapy. The secondary objective was to identify biomarkers able to predict response to BEV therapy in recurrent GBM patients. METHODS: A total of 90 recurrent GBM patients treated with BEV combination...

  7. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    Directory of Open Access Journals (Sweden)

    Zhiming Lin

    2015-01-01

    Full Text Available Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P=0.005, CRP (AUC = 0.75; P=0.017, and ASDAS (AUC = 0.778, P=0.007 significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P=0.040 significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2=6.87, P=0.009, and wald χ2=5.171, P=0.023. Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients.

  8. Can a mathematical model predict an individual's trait-like response to both total and partial sleep loss?

    Science.gov (United States)

    Ramakrishnan, Sridhar; Lu, Wei; Laxminarayan, Srinivas; Wesensten, Nancy J; Rupp, Tracy L; Balkin, Thomas J; Reifman, Jaques

    2015-06-01

    Humans display a trait-like response to sleep loss. However, it is not known whether this trait-like response can be captured by a mathematical model from only one sleep-loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep-loss condition. In this paper, we investigated the extent to which the recently developed unified mathematical model of performance (UMP) captured such trait-like features for different sleep-loss conditions. We used the UMP to develop two sets of individual-specific models for 15 healthy adults who underwent two different sleep-loss challenges (order counterbalanced; separated by 2-4 weeks): (i) 64 h of total sleep deprivation (TSD) and (ii) chronic sleep restriction (CSR) of 7 days of 3 h nightly time in bed. We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR, and vice versa. The results showed that the models customized to an individual under one sleep-loss condition accurately predicted performance of the same individual under the other condition, yielding, on average, up to 50% improvement over non-individualized, group-average model predictions. This finding supports the notion that the UMP captures an individual's trait-like response to different sleep-loss conditions. © 2014 European Sleep Research Society.

  9. A pilot validation of a modified Illness Perceptions Questionnaire designed to predict response to cognitive therapy for psychosis.

    Science.gov (United States)

    Marcus, Elena; Garety, Philippa; Weinman, John; Emsley, Richard; Dunn, Graham; Bebbington, Paul; Freeman, Daniel; Kuipers, Elizabeth; Fowler, David; Hardy, Amy; Waller, Helen; Jolley, Suzanne

    2014-12-01

    Clinical responsiveness to cognitive behavioural therapy for psychosis (CBTp) varies. Recent research has demonstrated that illness perceptions predict active engagement in therapy, and, thereby, better outcomes. In this study, we aimed to investigate the psychometric properties of a modification of the Illness Perceptions Questionnaire (M-IPQ) designed to predict response following CBTp. Fifty-six participants with persistent, distressing delusions completed the M-IPQ; forty before a brief CBT intervention targeting persecutory ideation and sixteen before and after a control condition. Additional predictors of outcome (delusional conviction, symptom severity and belief inflexibility) were assessed at baseline. Outcomes were assessed at baseline and at follow-up four to eight weeks later. The M-IPQ comprised two factors measuring problem duration and therapy-specific perceptions of Cure/Control. Associated subscales, formed by summing the relevant items for each factor, were reliable in their structure. The Cure/Control subscale was also reliable over time; showed convergent validity with other predictors of outcome; predicted therapy outcomes; and differentially predicted treatment effects. We measured outcome without an associated measure of engagement, in a small sample. Findings are consistent with hypothesis and existing research, but require replication in a larger, purposively recruited sample. The Cure/Control subscale of the M-IPQ shows promise as a predictor of response to therapy. Specifically targeting these illness perceptions in the early stages of cognitive behavioural therapy may improve engagement and, consequently, outcomes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer

    KAUST Repository

    Boulbes, Delphine R.

    2014-11-11

    Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

  11. HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer

    KAUST Repository

    Boulbes, Delphine R.; Arold, Stefan T.; Chauhan, Gaurav B.; Blachno, Korina V.; Deng, Nanfu; Chang, Wei-Chao; Jin, Quanri; Huang, Tzu-Hsuan; Hsu, Jung-Mao; Brady, Samuel W.; Bartholomeusz, Chandra; Ladbury, John E.; Stone, Steve; Yu, Dihua; Hung, Mien-Chie; Esteva, Francisco J.

    2014-01-01

    Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

  12. Prediction of electroconvulsive therapy response and remission in major depression : meta-analysis

    OpenAIRE

    Diermen, van, Linda; Ameele, van den, Seline; Kamperman, Astrid M.; Sabbe, Bernard G.C.; Vermeulen, Tom; Schrijvers, Didier; Birkenhager, Tom K.

    2018-01-01

    Abstract: Background Electroconvulsive therapy (ECT) is considered to be the most effective treatment in severe major depression. The identification of reliable predictors of ECT response could contribute to a more targeted patient selection and consequently increased ECT response rates. Aims To investigate the predictive value of age, depression severity, psychotic and melancholic features for ECT response and remission in major depression. Method A meta-analysis was conducted according to t...

  13. Afrika Statistika ISSN 2316-090X Improved Randomized Response ...

    African Journals Online (AJOL)

    Key words: Efficiency, Proportion Estimation, Randomized Response ... as drunk driving, abortion, alcoholism, illicit drugs usage, tax evasion, illegal possession of arms are ... is the impact of the response distortion on the survey or test results.

  14. Rapid response predicts 12-month post-treatment outcomes in binge-eating disorder: theoretical and clinical implications

    Science.gov (United States)

    Grilo, C. M.; White, M. A.; Wilson, G. T.; Gueorguieva, R.; Masheb, R. M.

    2011-01-01

    Background We examined rapid response in obese patients with binge-eating disorder (BED) in a clinical trial testing cognitive behavioral therapy (CBT) and behavioral weight loss (BWL). Method Altogether, 90 participants were randomly assigned to CBT or BWL. Assessments were performed at baseline, throughout and post-treatment and at 6- and 12-month follow-ups. Rapid response, defined as ≥70% reduction in binge eating by week four, was determined by receiver operating characteristic curves and used to predict outcomes. Results Rapid response characterized 57% of participants (67% of CBT, 47% of BWL) and was unrelated to most baseline variables. Rapid response predicted greater improvements across outcomes but had different prognostic significance and distinct time courses for CBT versus BWL. Patients receiving CBT did comparably well regardless of rapid response in terms of reduced binge eating and eating disorder psychopathology but did not achieve weight loss. Among patients receiving BWL, those without rapid response failed to improve further. However, those with rapid response were significantly more likely to achieve binge-eating remission (62% v. 13%) and greater reductions in binge-eating frequency, eating disorder psychopathology and weight loss. Conclusions Rapid response to treatment in BED has prognostic significance through 12-month follow-up, provides evidence for treatment specificity and has clinical implications for stepped-care treatment models for BED. Rapid responders who receive BWL benefit in terms of both binge eating and short-term weight loss. Collectively, these findings suggest that BWL might be a candidate for initial intervention in stepped-care models with an evaluation of progress after 1 month to identify non-rapid responders who could be advised to consider a switch to a specialized treatment. PMID:21923964

  15. 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.

  16. RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

    Directory of Open Access Journals (Sweden)

    S. RUBINA

    2013-04-01

    Full Text Available Concurrent chemoradiotherapy (CCRT is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST, the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra, 16 tumor (201 spectra and 13 complete response (151 CR spectra, one partial response (8 PR spectra and one nonresponder (8 NR spectra subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA followed by leave-one-out cross-validation (LOO-CV. Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

  17. Multivariate prediction of spontaneous repetitive responses in ventricular myocardium exposed in vitro to simulated ischemic conditions.

    Science.gov (United States)

    Schiariti, M; Puddu, P E; Rouet, R

    1994-06-01

    Guinea-pig ventricular myocardium was partly exposed to normal Tyrode's superfusion and partly to altered conditions (using modified Tyrode's solution) set to simulate acute myocardial ischemia (PO2 80 +/- 10 mmHg; no glucose; pH 7.00 +/- 0.05; K+ 12 mM). Using a double-chamber tissue bath and standard microelectrode technique, the occurrence of spontaneous repetitive responses was investigated during simulated ischemia (occlusion) and after reperfusing the previously ischemic superfused tissue with normal Tyrode's solution (reperfusion). In 62 experiments (42 animals) the effects of: (1) duration of simulated ischemia (1321 +/- 435 s), (2) stimulation rate (1002 +/- 549 ms) and (3) number of successive simulated ischemic periods (occlusions) (1.58 +/- 0.92) on: (1) resting membrane potential, (2) action potential amplitude, (3) duration of 50 and 90% action potentials and (4) maximal upstroke velocity of action potential were studied. All variables were considered as gradients (delta) between normal and ischemic tissue. Both during occlusion and upon reperfusion, spontaneous repetitive responses were coded as single, couplets, salvos (three to nine and > 10) or total spontaneous repetitive responses (coded present when at least one of the above-mentioned types was seen). The incidence of total spontaneous repetitive responses was 31% (19/62) on occlusion and 85% (53/62) upon reperfusion. Cox's models (forced and stepwise) were used to predict multivariately the occurrence of arrhythmic events considered as both total spontaneous repetitive responses and as separate entities. These models were applicable since continuous monitoring of the experiments enabled exact timing of spontaneous repetitive response onset during both occlusion and reperfusion. In predicting reperfusion spontaneous repetitive responses, total spontaneous repetitive responses and blocks observed during the occlusion period were also considered. Total occlusion spontaneous repetitive responses

  18. Multi-parametric MRI in cervical cancer. Early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Wei; Chen, Bing; Wang, Ai Jun; Zhao, Jian Guo [The General Hospital of Ningxia Medical University, Department of Radiology, Yinchuan (China); Qiang, Jin Wei [Fudan University, Department of Radiology, Jinshan Hospital, Shanghai (China); Tian, Hai Ping [The General Hospital of Ningxia Medical University, Department of Pathology, Yinchuan (China)

    2018-01-15

    To investigate the prediction of response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. Sixty-five patients underwent conventional MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumour groups according to post-treatment MRI. Pretreatment MRI parameters and CPF between the two groups were compared and prognostic factors, optimal thresholds, and predictive performance for post-treatment residual tumour occurrence were estimated. The residual group showed a lower maximum slope of increase (MSI{sub L}) and signal enhancement ratio (SER{sub L}) in low-perfusion subregions, a higher apparent diffusion coefficient (ADC) value, and a higher stage than the non-residual tumour group (p < 0.001, p = 0.003, p < 0.001, and p < 0.001, respectively). MSI{sub L} and ADC were independent prognostic factors. The combination of both measures improved the diagnostic performance compared with individual MRI parameters. A further combination of these two factors with CPF exhibited the highest predictive performance. Pretreatment MSI{sub L} and ADC were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance. (orig.)

  19. Adding Postal Follow-Up to a Web-Based Survey of Primary Care and Gastroenterology Clinic Physician Chiefs Improved Response Rates but not Response Quality or Representativeness.

    Science.gov (United States)

    Partin, Melissa R; Powell, Adam A; Burgess, Diana J; Haggstrom, David A; Gravely, Amy A; Halek, Krysten; Bangerter, Ann; Shaukat, Aasma; Nelson, David B

    2015-09-01

    This study assessed whether postal follow-up to a web-based physician survey improves response rates, response quality, and representativeness. We recruited primary care and gastroenterology chiefs at 125 Veterans Affairs medical facilities to complete a 10-min web-based survey on colorectal cancer screening and diagnostic practices in 2010. We compared response rates, response errors, and representativeness in the primary care and gastroenterology samples before and after adding postal follow-up. Adding postal follow-up increased response rates by 20-25 percentage points; markedly greater increases than predicted from a third e-mail reminder. In the gastroenterology sample, the mean number of response errors made by web responders (0.25) was significantly smaller than the mean number made by postal responders (2.18), and web responders provided significantly longer responses to open-ended questions. There were no significant differences in these outcomes in the primary care sample. Adequate representativeness was achieved before postal follow-up in both samples, as indicated by the lack of significant differences between web responders and the recruitment population on facility characteristics. We conclude adding postal follow-up to this web-based physician leader survey improved response rates but not response quality or representativeness. © The Author(s) 2013.

  20. Metformin use and improved response to therapy in rectal cancer

    International Nuclear Information System (INIS)

    Skinner, Heath D.; Crane, Christopher H.; Garrett, Christopher R.; Eng, Cathy; Chang, George J.; Skibber, John M.; Rodriguez-Bigas, Miguel A.; Kelly, Patrick; Sandulache, Vlad C.; Delclos, Marc E.; Krishnan, Sunil; Das, Prajnan

    2013-01-01

    Locally advanced rectal cancer is commonly treated with chemoradiation prior to total mesorectal excision (TME). Studies suggest that metformin may be an effective chemopreventive agent in this disease as well as a possible adjunct to current therapy. In this study, we examined the effect of metformin use on pathologic complete response (pCR) rates and outcomes in rectal cancer. The charts of 482 patients with locally advanced rectal adenocarcinoma treated from 1996 to 2009 with chemoradiation and TME were reviewed. Median radiation dose was 50.4 Gy (range 19.8–63). Nearly, all patients were treated with concurrent 5-fluorouracil-based chemotherapy (98%) followed by adjuvant chemotherapy (81.3%). Patients were categorized as nondiabetic (422), diabetic not taking metformin (40), or diabetic taking metformin (20). No significant differences between groups were found in clinical tumor classification, nodal classification, tumor distance from the anal verge or circumferential extent, pretreatment carcinoembryonic antigen level, or pathologic differentiation. pCR rates were 16.6% for nondiabetics, 7.5% for diabetics not using metformin, and 35% for diabetics taking metformin, with metformin users having significantly higher pCR rates than either nondiabetics (P = 0.03) or diabetics not using metformin (P = 0.007). Metformin use was significantly associated with pCR rate on univariate (P = 0.05) and multivariate (P = 0.01) analyses. Furthermore, patients taking metformin had significantly increased disease-free (P = 0.013) and overall survival (P = 0.008) compared with other diabetic patients. Metformin use is associated with significantly higher pCR rates as well as improved survival. These promising data warrant further prospective study

  1. Dynamic Variables Fail to Predict Fluid Responsiveness in an Animal Model With Pericardial Effusion.

    Science.gov (United States)

    Broch, Ole; Renner, Jochen; Meybohm, Patrick; Albrecht, Martin; Höcker, Jan; Haneya, Assad; Steinfath, Markus; Bein, Berthold; Gruenewald, Matthias

    2016-10-01

    The reliability of dynamic and volumetric variables of fluid responsiveness in the presence of pericardial effusion is still elusive. The aim of the present study was to investigate their predictive power in a porcine model with hemodynamic relevant pericardial effusion. A single-center animal investigation. Twelve German domestic pigs. Pigs were studied before and during pericardial effusion. Instrumentation included a pulmonary artery catheter and a transpulmonary thermodilution catheter in the femoral artery. Hemodynamic variables like cardiac output (COPAC) and stroke volume (SVPAC) derived from pulmonary artery catheter, global end-diastolic volume (GEDV), stroke volume variation (SVV), and pulse-pressure variation (PPV) were obtained. At baseline, SVV, PPV, GEDV, COPAC, and SVPAC reliably predicted fluid responsiveness (area under the curve 0.81 [p = 0.02], 0.82 [p = 0.02], 0.74 [p = 0.07], 0.74 [p = 0.07], 0.82 [p = 0.02]). After establishment of pericardial effusion the predictive power of dynamic variables was impaired and only COPAC and SVPAC and GEDV allowed significant prediction of fluid responsiveness (area under the curve 0.77 [p = 0.04], 0.76 [p = 0.05], 0.83 [p = 0.01]) with clinically relevant changes in threshold values. In this porcine model, hemodynamic relevant pericardial effusion abolished the ability of dynamic variables to predict fluid responsiveness. COPAC, SVPAC, and GEDV enabled prediction, but their threshold values were significantly changed. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. 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).

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Loop Gain Predicts the Response to Upper Airway Surgery in Patients With Obstructive Sleep Apnea.

    Science.gov (United States)

    Joosten, Simon A; Leong, Paul; Landry, Shane A; Sands, Scott A; Terrill, Philip I; Mann, Dwayne; Turton, Anthony; Rangaswamy, Jhanavi; Andara, Christopher; Burgess, Glen; Mansfield, Darren; Hamilton, Garun S; Edwards, Bradley A

    2017-07-01

    Upper airway surgery is often recommended to treat patients with obstructive sleep apnea (OSA) who cannot tolerate continuous positive airways pressure. However, the response to surgery is variable, potentially because it does not improve the nonanatomical factors (ie, loop gain [LG] and arousal threshold) causing OSA. Measuring these traits clinically might predict responses to surgery. Our primary objective was to test the value of LG and arousal threshold to predict surgical success defined as 50% reduction in apnea-hypopnea index (AHI) and AHI <10 events/hour post surgery. We retrospectively analyzed data from patients who underwent upper airway surgery for OSA (n = 46). Clinical estimates of LG and arousal threshold were calculated from routine polysomnographic recordings presurgery and postsurgery (median of 124 [91-170] days follow-up). Surgery reduced both the AHI (39.1 ± 4.2 vs. 26.5 ± 3.6 events/hour; p < .005) and estimated arousal threshold (-14.8 [-22.9 to -10.2] vs. -9.4 [-14.5 to -6.0] cmH2O) but did not alter LG (0.45 ± 0.08 vs. 0.45 ± 0.12; p = .278). Responders to surgery had a lower baseline LG (0.38 ± 0.02 vs. 0.48 ± 0.01, p < .05) and were younger (31.0 [27.3-42.5] vs. 43.0 [33.0-55.3] years, p < .05) than nonresponders. Lower LG remained a significant predictor of surgical success after controlling for covariates (logistic regression p = .018; receiver operating characteristic area under curve = 0.80). Our study provides proof-of-principle that upper airway surgery most effectively resolves OSA in patients with lower LG. Predicting the failure of surgical treatment, consequent to less stable ventilatory control (elevated LG), can be achieved in the clinic and may facilitate avoidance of surgical failures. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  9. Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

    International Nuclear Information System (INIS)

    Richieri, Raphaelle; Lancon, Christophe; Boyer, Laurent; Farisse, Jean; Colavolpe, Cecile; Mundler, Olivier; Guedj, Eric

    2011-01-01

    The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of 99m Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

  10. Molecular markers predicting radiotherapy response: Report and recommendations from an International Atomic Energy Agency technical meeting

    International Nuclear Information System (INIS)

    West, Catharine M.L.; McKay, Michael J.; Hoelscher, Tobias; Baumann, Michael; Stratford, Ian J.; Bristow, Robert G.; Iwakawa, Mayumi; Imai, Takashi; Zingde, Surekha M.; Anscher, Mitchell S.; Bourhis, Jean; Begg, Adrian C.; Haustermans, Karin; Bentzen, Soren M.; Hendry, Jolyon H.

    2005-01-01

    Purpose: There is increasing interest in radiogenomics and the characterization of molecular profiles that predict normal tissue and tumor radioresponse. A meeting in Amsterdam was organized by the International Atomic Energy Agency to discuss this topic on an international basis. Methods and Materials: This report is not completely exhaustive, but highlights some of the ongoing studies and new initiatives being carried out worldwide in the banking of tumor and normal tissue samples underpinning the development of molecular marker profiles for predicting patient response to radiotherapy. It is generally considered that these profiles will more accurately define individual or group radiosensitivities compared with the nondefinitive findings from the previous era of cellular-based techniques. However, so far there are only a few robust reports of molecular markers predicting normal tissue or tumor response. Results: Many centers in different countries have initiated tissue and tumor banks to store samples from clinical trials for future molecular profiling analysis, to identify profiles that predict for radiotherapy response. The European Society for Therapeutic Radiology and Oncology GENEtic pathways for the Prediction of the effects of Irradiation (GENEPI) project, to store, document, and analyze sample characteristics vs. response, is the most comprehensive in this regard. Conclusions: The next 5-10 years are likely to see the results of these and other correlative studies, and promising associations of profiles with response should be validated in larger definitive trials

  11. 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.

  12. 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

  13. 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...

  14. 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...

  15. 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 ...

  16. 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

  17. Neoadjuvant chemotherapy with trastuzumab in HER2-positive breast cancer: pathologic complete response rate, predictive and prognostic factors

    Directory of Open Access Journals (Sweden)

    I.P.C. Buzatto

    Full Text Available The purpose of this study was to retrospectively review the pathologic complete response (pCR rate from patients (n=86 with stage II and III HER2-positive breast cancer treated with neoadjuvant chemotherapy at our institution from 2008 to 2013 and to determine possible predictive and prognostic factors. Immunohistochemistry for hormone receptors and Ki-67 was carried out. Clinical and pathological features were analyzed as predictive factors of response to therapy. For survival analysis, we used Kaplan-Meier curves to estimate 5-year survival rates and the log-rank test to compare the curves. The addition of trastuzumab to neoadjuvant chemotherapy significantly improved pCR rate from 4.8 to 46.8%, regardless of the number of preoperative trastuzumab cycles (P=0.0012. Stage II patients achieved a higher response rate compared to stage III (P=0.03. The disease-free and overall survivals were not significantly different between the group of patients that received trastuzumab in the neoadjuvant setting (56.3 and 70% at 5 years, respectively and the group that initiated it post-operatively (75.8 and 88.7% at 5 years, respectively. Axillary pCR post neoadjuvant chemotherapy with trastuzumab was associated with reduced risk of recurrence (HR=0.34; P=0.03 and death (HR=0.21; P=0.02. In conclusion, we confirmed that trastuzumab improves pCR rates and verified that this improvement occurs even with less than four cycles of the drug. Hormone receptors and Ki-67 expressions were not predictive of response in this subset of patients. Axillary pCR clearly denotes prognosis after neoadjuvant target therapy and should be considered to be a marker of resistance, providing an opportunity to investigate new strategies for HER2-positive treatment.

  18. Improving Providers' Role Definitions to Decrease Overcrowding and Improve In-Hospital Cardiac Arrest Response.

    Science.gov (United States)

    Leary, Marion; Schweickert, William; Neefe, Stacie; Tsypenyuk, Boris; Falk, Scott Austin; Holena, Daniel N

    2016-07-01

    How nontechnical factors such as inadequate role definition and overcrowding affect outcomes of in-hospital cardiac arrest (IHCA) is unknown. Using a bundled intervention, we sought to improve providers' role definitions and decrease overcrowding during IHCA events. To determine if a bundled intervention consisting of a nurse/physician leadership dyad, visual cues for provider roles, and a "role check" would lead to reductions in crowding and improve perceptions of communication and team leadership. Baseline data on the number and type of IHCA providers were collected. Providers were asked to complete a postevent survey rating communication and leadership. A bundled intervention was then introduced. Data were then obtained for the subsequent IHCA events. Twenty ICHA events were captured before and 34 after the intervention. The number of physicians present at pulse checks 2 (median [interquartile range]: 6 [5-8] before vs 5 [3-6] after, P = .02) and 3 (7 [5-9] vs 4 [4-5], P = .004) decreased significantly after the intervention. The overall number of providers at the third pulse check (18 [14-22] before vs 14 [12-16] after, P = .04) also decreased after the intervention. On a 10-point Likert scale, ratings of communication (8 [7-8]) and physician leadership (8 [7-9]) did not differ significantly from before to after the intervention. Both the physician leads (90%) and patients' primary nurses (97%) were able to identify clear nurse leaders. A bundled intervention targeted at improving IHCA response led to a decrease in overcrowding at ICHA events without substantial changes in the perceptions of communication or physician leadership. ©2016 American Association of Critical-Care Nurses.

  19. 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.

  20. 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...

  1. 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.

  2. Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study.

    Science.gov (United States)

    Balconi, Michela; Grippa, Elisabetta; Vanutelli, Maria Elide

    2015-12-01

    This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  3. Prediction of human population responses to toxic compounds by a collaborative competition.

    Science.gov (United States)

    Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

    2015-09-01

    The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour.

    Directory of Open Access Journals (Sweden)

    Jan Ketil Arnulf

    Full Text Available Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60-86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.

  9. 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

  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. Use of molecular markers for predicting therapy response in cancer patients.

    LENUS (Irish Health Repository)

    Duffy, Michael J

    2012-02-01

    Predictive markers are factors that are associated with upfront response or resistance to a particular therapy. Predictive markers are important in oncology as tumors of the same tissue of origin vary widely in their response to most available systemic therapies. Currently recommended oncological predictive markers include both estrogen and progesterone receptors for identifying patients with breast cancers likely to benefit from hormone therapy, HER-2 for the identification of breast cancer patients likely to benefit from trastuzumab, specific K-RAS mutations for the identification of patients with advanced colorectal cancer unlikely to benefit from either cetuximab or panitumumab and specific EGFR mutations for selecting patients with advanced non-small-cell lung cancer for treatment with tyrosine kinase inhibitors such as gefitinib and erlotinib. The availability of predictive markers should increase drug efficacy and decrease toxicity, thus leading to a more personalized approach to cancer treatment.

  12. Predicting Improvement in Writer's Cramp Symptoms following Botulinum Neurotoxin Injection Therapy

    Directory of Open Access Journals (Sweden)

    Mallory Jackman

    2016-09-01

    Full Text Available Introduction: Writer's cramp is a specific focal hand dystonia causing abnormal posturing and tremor in the upper limb. The most popular medical intervention, botulinum neurotoxin type A (BoNT-A therapy, is variably effective for 50–70% of patients. BoNT-A non-responders undergo ineffective treatment and may experience significant side effects. Various assessments have been used to determine response prediction to BoNT-A, but not in the same population of patients. Methods: A comprehensive assessment was employed to measure various symptom aspects. Clinical scales, full upper-limb kinematic measures, self-report, and task performance measures were assessed for nine writer's cramp patients at baseline. Patients received two BoNT-A injections then were classified as responders or non-responders based on a quantified self-report measure. Baseline scores were compared between groups, across all measures, to determine which scores predicted a positive BoNT-A response. Results: Five of nine patients were responders. No kinematic measures were predictably different between groups. Analyses revealed three features that predicted a favorable response and separated the two groups: higher than average cramp severity and cramp frequency, and below average cramp latency. Discussion: Non-kinematic measures appear to be superior in making such predictions. Specifically, measures of cramp severity, frequency, and latency during performance of a specific set of writing and drawing tasks were predictive factors. Since kinematic was not used to determine the injection pattern and the injections were visually guided, it may still be possible to use individual patient kinematics for better outcomes. 

  13. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    OpenAIRE

    Hudson, LN; Newbold, T; Contu, S; Hill, SLL; Lysenko, I; De Palma, A; Phillips, HRP; Alhusseini, TI; Bedford, FE; Bennett, DJ; Booth, H; Burton, VJ; Chng, CWT; Choimes, A; Correia, DLP

    2017-01-01

    The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make free...

  14. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

    OpenAIRE

    Hudson, L. N.; Newbold, T.; Contu, S.; Hill, S. L.; Lysenko, I.; De Palma, A.; Phillips, H. R.; Alhusseini, T. I.; Bedford, F. E.; Bennett, D. J.; Booth, H.; Burton, V. J.; Chng, C. W.; Choimes, A.; Correia, D. L.

    2017-01-01

    The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make free...

  15. [Prediction of the molecular response to pertubations from single cell measurements].

    Science.gov (United States)

    Remacle, Françoise; Levine, Raphael D

    2014-12-01

    The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure. © 2014 médecine/sciences – Inserm.

  16. 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

  17. 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

  18. Operational mesoscale atmospheric dispersion prediction using high performance parallel computing cluster for emergency response

    International Nuclear Information System (INIS)

    Srinivas, C.V.; Venkatesan, R.; Muralidharan, N.V.; Das, Someshwar; Dass, Hari; Eswara Kumar, P.

    2005-08-01

    An operational atmospheric dispersion prediction system is implemented on a cluster super computer for 'Online Emergency Response' for Kalpakkam nuclear site. The numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48 hour forecast of the local weather and radioactive plume dispersion due to hypothetical air borne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. Results of MM5 run time performance for 1-day prediction are reported on all the machines available for testing. A reduction of 5 times in runtime is achieved using 9 dual Xeon nodes (18 physical/36 logical processors) compared to a single node sequential run. Based on the above run time results a cluster computer facility with 9-node Dual Xeon is commissioned at IGCAR for model operation. The run time of a triple nested domain MM5 is about 4 h for 24 h forecast. The system has been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions and using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Slight improvement is noticed in rainfall, winds, geopotential heights and the vertical atmospheric structure while using NCEP data probably because of its high spatial and temporal resolution. (author)

  19. Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

    Energy Technology Data Exchange (ETDEWEB)

    Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier [Centre Hospitalo-Universitaire de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Assistance Publique des Hopitaux de Marseille, Marseille Cedex 5 (France); Niboyet, Jean [Clinique La Phoceanne, Unite d' Etude et de Traitement de la Douleur, Marseille (France)

    2007-08-15

    Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 {+-} 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 {+-} 4.2 before ketamine and 31.8 {+-} 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

  20. Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

    International Nuclear Information System (INIS)

    Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier; Niboyet, Jean

    2007-01-01

    Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 ± 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 ± 4.2 before ketamine and 31.8 ± 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

  1. Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

    Science.gov (United States)

    Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

    2011-01-01

    Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

  2. 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.

  3. 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.

  4. 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.

  5. Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

    Science.gov (United States)

    Yoo, Byong Chul; Yeo, Seung-Gu

    2017-03-01

    Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

  6. 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.

  7. 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

  8. FDG-PET Response Prediction in Pediatric Hodgkin’s Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value

    Energy Technology Data Exchange (ETDEWEB)

    Hussien, Amr Elsayed M. [Department of Nuclear Medicine (KME), Forschungszentrum Jülich, Medical Faculty, Heinrich-Heine-University Düsseldorf, Jülich, 52426 (Germany); Department of Nuclear Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany); Furth, Christian [Department of Radiology and Nuclear Medicine, Medical School, Otto-von-Guericke University Magdeburg, Magdeburg, 39120 (Germany); Schönberger, Stefan [Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany); Hundsdoerfer, Patrick [Department of Pediatric Oncology and Hematology, Charité Campus Virchow, Humboldt-University Berlin, Berlin, 13353 (Germany); Steffen, Ingo G.; Amthauer, Holger [Department of Radiology and Nuclear Medicine, Medical School, Otto-von-Guericke University Magdeburg, Magdeburg, 39120 (Germany); Müller, Hans-Wilhelm; Hautzel, Hubertus, E-mail: h.hautzel@fz-juelich.de [Department of Nuclear Medicine (KME), Forschungszentrum Jülich, Medical Faculty, Heinrich-Heine-University Düsseldorf, Jülich, 52426 (Germany); Department of Nuclear Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, 40225 (Germany)

    2015-01-28

    Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

  9. FDG-PET Response Prediction in Pediatric Hodgkin’s Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value

    International Nuclear Information System (INIS)

    Hussien, Amr Elsayed M.; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G.; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

    2015-01-01

    Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV

  10. Modeling transducer impulse responses for predicting calibrated pressure pulses with the ultrasound simulation program Field II

    DEFF Research Database (Denmark)

    Bæk, David; Jensen, Jørgen Arendt; Willatzen, Morten

    2010-01-01

    FIELD II is a simulation software capable of predicting the field pressure in front of transducers having any complicated geometry. A calibrated prediction with this program is, however, dependent on an exact voltage-to-surface acceleration impulse response of the transducer. Such impulse response...... is not calculated by FIELD II. This work investigates the usability of combining a one-dimensional multilayer transducer modeling principle with the FIELD II software. Multilayer here refers to a transducer composed of several material layers. Measurements of pressure and current from Pz27 piezoceramic disks...... transducer model and the FIELD II software in combination give good agreement with measurements....

  11. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

  12. Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

    Science.gov (United States)

    Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

    2015-01-01

    Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

  13. 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.

  14. 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

  15. 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...

  16. 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...

  17. 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.

  18. Atrophy of spared grey matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke

    Science.gov (United States)

    Gauthier, Lynne V.; Taub, Edward; Mark, Victor W.; Barghi, Ameen; Uswatte, Gitendra

    2011-01-01

    Background and Purpose Although the motor deficit following stroke is clearly due to the structural brain damage that has been sustained, this relationship is attenuated from the acute to chronic phases. We investigated the possibility that motor impairment and response to Constraint-Induced Movement therapy (CI therapy) in chronic stroke patients may relate more strongly to the structural integrity of brain structures remote from the lesion than to measures of overt tissue damage. Methods Voxel-based morphometry (VBM) analysis was performed on MRI scans from 80 chronic stroke patients to investigate whether variations in grey matter density were correlated with extent of residual motor impairment or with CI therapy-induced motor recovery. Results Decreased grey matter density in non-infarcted motor regions was significantly correlated with magnitude of residual motor deficit. In addition, reduced grey matter density in multiple remote brain regions predicted a lesser extent of motor improvement from CI therapy. Conclusions Atrophy in seemingly healthy parts of the brain that are distant from the infarct accounts for at least a portion of the sustained motor deficit in chronic stroke. PMID:22096036

  19. 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

  20. The potential biomarkers in predicting pathologic response of breast cancer to three different chemotherapy regimens: a case control study

    Directory of Open Access Journals (Sweden)

    Xu Chaoyang

    2009-07-01

    Full Text Available Abstract Background Preoperative chemotherapy (PCT has become the standard of care in locally advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to therapy would help optimize treatment, improve treatment outcomes, and avoid unnecessary exposure to potential toxicities. This study is to determine whether selected biomarkers could predict pathologic response (PR of breast tumors to three different PCT regimens, and to identify a subset of patients who would benefit from a given type of treatment. Methods 118 patients with primary breast tumor were identified and three PCT regimens including DEC (docetaxel+epirubicin+cyclophosphamide, VFC (vinorelbine/vincristine+5-fluorouracil+cyclophosphamide and EFC (epirubicin+5-fluorouracil+cyclophosphamide were investigated. Expression of steroid receptors, HER2, P-gp, MRP, GST-pi and Topo-II was evaluated by immunohistochemical scoring on tumor tissues obtained before and after PCT. The PR of breast carcinoma was graded according to Sataloff's classification. Chi square test, logistic regression and Cochran-Mantel-Haenszel assay were performed to determine the association between biomarkers and PR, as well as the effectiveness of each regimen on induction of PR. Results There was a clear-cut correlation between the expression of ER and decreased PR to PCT in all three different regimens (p p Conclusion ER is an independent predictive factor for PR to PCT regimens including DEC, VFC and EFC in primary breast tumors, while HER2 is only predictive for DEC regimen. Expression of PgR, Topo-II, P-gp, MRP and GST-pi are not predictive for PR to any PCT regimens investigated. Results obtained in this clinical study may be helpful for the selection of appropriate treatments for breast cancer patients.

  1. 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.

  2. DISIS: prediction of drug response through an iterative sure independence screening.

    Directory of Open Access Journals (Sweden)

    Yun Fang

    Full Text Available Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.

  3. 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.

  4. 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.

  5. Prediction of therapy response to interferon-alpha in chronic viral hepatitis-B by liver and hepatobiliary scintigraphy

    International Nuclear Information System (INIS)

    Caglar, M.; Sari, O.; Akcan, Y.

    2002-01-01

    Interferon (IFN) provides effective treatment in some patients with chronic hepatitis. The clarification of factors predictive of therapy response would be helpful in identifying patients who would benefit from treatment. In this study, we evaluated the potential utility of Tc-99m sulfur colloid liver/spleen and Tc-99m-disofenin hepatobiliary scintigraphy to predict therapy response to IFN in patients with chronic active hepatitis. The study group consisted of ten patients with chronic viral hepatitis B who were treated with 4.5 units of interferon alpha for 12 months. Prior to the start of the therapy, sulfur colloid scintigraphy was obtained by which the liver/spleen ratios were derived. Hepatobiliary scintigraphy was performed on a separate day and time-activity curves were generated from regions of interest drawn over the liver, heart and gall-bladder. The index of blood and liver clearance time was calculated. Histological grading and laboratory values were obtained for clinical correlation. Responders (n=6) to IFN were defined as those who improved clinically with normalized transaminase levels and had hepatitis B envelope antigen (HBeAg) seroconversion. On sulfur colloid (SC) scintigraphy, the liver/spleen ratio of non-responders was significantly lower than responders (median values: 0.69 vs. 1.16, p=0.01) but on hepatobiliary scintigraphy no statistically significant parameters were found to predict response to interferon therapy. (author)

  6. Final technical report. Can microbial functional traits predict the response and resilience of decomposition to global change?

    Energy Technology Data Exchange (ETDEWEB)

    Allison, Steven D. [Univ. of California, Irvine, CA (United States)

    2015-09-24

    The role of specific micro-organisms in the carbon cycle, and their responses to environmental change, are unknown in most ecosystems. This knowledge gap limits scientists’ ability to predict how important ecosystem processes, like soil carbon storage and loss, will change with climate and other environmental factors. The investigators addressed this knowledge gap by transplanting microbial communities from different environments into new environments and measuring the response of community composition and carbon cycling over time. Using state-of-the-art sequencing techniques, computational tools, and nanotechnology, the investigators showed that microbial communities on decomposing plant material shift dramatically with natural and experimentally-imposed drought. Microbial communities also shifted in response to added nitrogen, but the effects were smaller. These changes had implications for carbon cycling, with lower rates of carbon loss under drought conditions, and changes in the efficiency of decomposition with nitrogen addition. Even when transplanted into the same conditions, microbial communities from different environments remained distinct in composition and functioning for up to one year. Changes in functioning were related to differences in enzyme gene content across different microbial groups. Computational approaches developed for this project allowed the conclusions to be tested more broadly in other ecosystems, and new computer models will facilitate the prediction of microbial traits and functioning across environments. The data and models resulting from this project benefit the public by improving the ability to predict how microbial communities and carbon cycling functions respond to climate change, nutrient enrichment, and other large-scale environmental changes.

  7. 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

  8. rTMS neuromodulation improves electrocortical functional measures of information processing and behavioral responses in autism

    Directory of Open Access Journals (Sweden)

    Estate M Sokhadze

    2014-08-01

    Full Text Available Objectives: Reports in autism spectrum disorders (ASD of a minicolumnopathy with consequent deficits of lateral inhibition help explain observed behavioral and executive dysfunctions. We propose that neuromodulation based on rTMS will enhance lateral inhibition through activation of inhibitory double bouquet interneurons and will be accompanied by improvements in the prefrontal executive functions. Methods: The current study used ERPs in a visual oddball task with illusory figures. We compared clinical, behavioral and electrocortical outcomes in 2 groups of children with autism (TMS, wait-list group [WTL]. We predicted that 18 session long course in autistic patients will have better behavioral and ERP outcomes as compared to age- and IQ-matched wait-list group. We used 18 sessions of 1Hz rTMS applied over the dorso-lateral prefrontal cortex in 27 individuals with ASD diagnosis. The WTL group was comprised of 27 age-matched ASD subjects. Results: Post-TMS evaluations showed decreased irritability and hyperactivity and decreased stereotypic behaviors. Following rTMS we found decreased amplitude and prolonged latency in the fronto-central ERPs to non-targets in the TMS group. These ERP changes along with increased centro-parietal ERPs to targets are indicative of more efficient processing of information post-TMS. Another finding was increased magnitude of error-related negativity (ERN during commission errors. We calculated normative post-error reaction time (RT slowing response in both groups and found that rTMS was accompanied by post-error RT slowing and higher accuracy of responses, whereas the WTL group kept on showing typical for ASD post-error RT speeding and had higher error rate. Conclusion: Results from our study indicate that rTMS improves executive functioning in ASD as evidenced by normalization of ERP responses and behavioral reactions during executive function test, and also by improvements in clinical behavioral evaluations.

  9. 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

  10. Ways to improve of corporate social responsibility in Ukrainian forestry

    Directory of Open Access Journals (Sweden)

    Martyinenko Irina

    2016-02-01

    Full Text Available The article analyzes the marketing techniques of corporate social responsibility in the current conditions of the Ukrainian wood industry with an emphasis on the need to conduct non-financial reporting

  11. Test-Anchored Vibration Response Predictions for an Acoustically Energized Curved Orthogrid Panel with Mounted Components

    Science.gov (United States)

    Frady, Gregory P.; Duvall, Lowery D.; Fulcher, Clay W. G.; Laverde, Bruce T.; Hunt, Ronald A.

    2011-01-01

    rich body of vibroacoustic test data was recently generated at Marshall Space Flight Center for component-loaded curved orthogrid panels typical of launch vehicle skin structures. The test data were used to anchor computational predictions of a variety of spatially distributed responses including acceleration, strain and component interface force. Transfer functions relating the responses to the input pressure field were generated from finite element based modal solutions and test-derived damping estimates. A diffuse acoustic field model was applied to correlate the measured input sound pressures across the energized panel. This application quantifies the ability to quickly and accurately predict a variety of responses to acoustically energized skin panels with mounted components. Favorable comparisons between the measured and predicted responses were established. The validated models were used to examine vibration response sensitivities to relevant modeling parameters such as pressure patch density, mesh density, weight of the mounted component and model form. Convergence metrics include spectral densities and cumulative root-mean squared (RMS) functions for acceleration, velocity, displacement, strain and interface force. Minimum frequencies for response convergence were established as well as recommendations for modeling techniques, particularly in the early stages of a component design when accurate structural vibration requirements are needed relatively quickly. The results were compared with long-established guidelines for modeling accuracy of component-loaded panels. A theoretical basis for the Response/Pressure Transfer Function (RPTF) approach provides insight into trends observed in the response predictions and confirmed in the test data. The software developed for the RPTF method allows easy replacement of the diffuse acoustic field with other pressure fields such as a turbulent boundary layer (TBL) model suitable for vehicle ascent. Structural responses

  12. Improving Cybersecurity Incident Response Team (CSIRT) Skills, Dynamics and Effectiveness

    Science.gov (United States)

    2017-03-01

    Analysts. Cognitive prompts can reduce overconfidence and information bias. One such strategy is the “Five-Why Analysis,” developed by Toyota and used...building trust among CSIRTs and MTS members (including those from other CSIRTs and agencies), as well as developing an environment of psychological safety...recommendations for optimal CSIRT performance. 15. SUBJECT TERMS Cyber Incident Response, Response Teams, Cognitive Task Analysis 16. SECURITY

  13. Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

    Science.gov (United States)

    Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

    2016-08-01

    Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value for patients undergoing procedures

  14. On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

    Science.gov (United States)

    Jen, Chian-Li; Tilwick, Leon

    2000-01-01

    This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

  15. 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.

  16. 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

  17. 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.

  18. Depression and anxiety predict sex-specific cortisol responses to interpersonal stress.

    Science.gov (United States)

    Powers, Sally I; Laurent, Heidemarie K; Gunlicks-Stoessel, Meredith; Balaban, Susan; Bent, Eileen

    2016-07-01

    Clinical theories posit interpersonal stress as an important factor in the emergence and exacerbation of depression and anxiety, while neuroendocrine research confirms the association of these syndromes with dysregulation in a major stress response system, the hypothalamic-pituitary-adrenal (HPA) axis. However, the proposal that depression and anxiety symptoms and diagnoses are associated with problematic HPA responses to close relationship stress has not been directly tested. We examined 196 heterosexual dating couples' depression and anxiety symptoms and diagnoses, assessed with questionnaires and diagnostic interviews, in relation to cortisol responses to discussion of an unresolved relationship conflict. Participants provided seven salivary samples in anticipation of and directly following the discussion, and throughout an hour-long recovery period, which were assayed for cortisol. Multilevel models of the HPA response predicted by symptoms or diagnoses showed that women's depressive symptoms predicted attenuated cortisol levels, with a flatter response curve. In contrast, men's depression symptoms and women's anxiety symptoms and diagnoses predicted higher cortisol levels. These findings highlight the importance of examining sex differences in responses to interpersonal stressors for understanding HPA dysregulation in internalizing psychopathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis

    Energy Technology Data Exchange (ETDEWEB)

    Bajpai, Jyoti; Bakhshi, Sameer [Dr. B. R. A. Institute Rotary Cancer Hospital, Department of Medical Oncology, All India Institute of Medical Sciences, New Delhi (India); Gamnagatti, Shivanand [All India Institute of Medical Sciences, Department of Radiodiagnosis, New Delhi (India); Kumar, Rakesh; Malhotra, Arun [All India Institute of Medical Sciences, Department of Nuclear Medicine, New Delhi (India); Sr