predictable biological distributions: Topics by WorldWideScience.org

Sample records for predictable biological distributions

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

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Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.
Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

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Simon van Mourik

2014-06-01

Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.
Biological and ecological characteristics of soft ticks (Ixodida: Argasidae and their impact for predicting tick and associated disease distribution

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Vial L.

2009-09-01

Full Text Available As evidence of global changes is accumulating, scientists are challenged to detect distribution changes of vectors, reservoirs and pathogens caused by anthropogenic and/or environmental changes. Statistical and mathematical distribution models are emerging for ixodid hard ticks whereas no prediction has ever been developed for argasid ones. These last organisms remain unknown and under-reported; they differ from hard ticks by many structural, biological and ecological properties, which complicate direct adaptation of hard tick models. However, investigations on bibliographic resources concerning these ticks suggest that distribution modelling based on natural niche concept and using environmental factors especially climate is also possible, bearing in mind the scale of prediction and their specificities including their nidicolous lifestyle, an indiscriminate host feeding and a short bloodmeal duration, as well as a flexible development cycle through diapause periods.
Incorporating uncertainty in predictive species distribution modelling.

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Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.
Confronting species distribution model predictions with species functional traits.

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Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.
Prediction of Some Biological Film Characteristic

International Nuclear Information System (INIS)

Ibrahim, G.; Khalaf, K.

2004-01-01

A multi components biofilm model has been developed and discussed which accounts for all well established biological processes with biofilm(s): s ubstrate utilization [aerobically, axenically and anaerobically]. D enitrification process. n itrification process. t he effect of diffusion and mass transfer limitations. The model has predicted some important characteristics of bio films such as:- s pace distribution of substrate(s) within bio films. W eight or thickness of biofilm layer (aerobic layer, anoxic layer and anaerobic layer). The results indicates that the relative thickness of biofilm layers (aerobically, anoxically and anaerobic) is highly affected by the availability of the main electron acceptor (O 2 and NO 3 and the organic load)
Predictable return distributions

DEFF Research Database (Denmark)

Pedersen, Thomas Quistgaard

trace out the entire distribution. A univariate quantile regression model is used to examine stock and bond return distributions individually, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that certain parts of the return distributions......-of-sample analyses show that the relative accuracy of the state variables in predicting future returns varies across the distribution. A portfolio study shows that an investor with power utility can obtain economic gains by applying the empirical return distribution in portfolio decisions instead of imposing...
Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate

International Nuclear Information System (INIS)

Gulliford, Sarah L.; Webb, Steve; Rowbottom, Carl G.; Corne, David W.; Dearnaley, David P.

2004-01-01

Background and purpose: This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated. Patients and methods: A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data. Results: It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome. Conclusion: ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes
78 FR 57293 - Distribution of Reference Biological Standards and Biological Preparations

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2013-09-18

... DEPARTMENT OF HEALTH AND HUMAN SERVICES 42 CFR Part 7 [Docket No. CDC-2013-0013] RIN 0920-AA52 Distribution of Reference Biological Standards and Biological Preparations AGENCY: Centers for Disease Control and Prevention (HHS/CDC), Department of Health and Human Services (HHS). ACTION: Confirmation of...
Predicting spiral wave patterns from cell properties in a model of biological self-organization.

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Geberth, Daniel; Hütt, Marc-Thorsten

2008-09-01

In many biological systems, biological variability (i.e., systematic differences between the system components) can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In principle, the distribution of single-element properties should thus allow predicting features of such patterns. For a mathematical model of a paradigmatic and well-studied pattern formation process, spiral waves of cAMP signaling in colonies of the slime mold Dictyostelium discoideum, we explore this possibility and observe a pronounced anticorrelation between spiral waves and cell properties (namely, the firing rate) and particularly a clustering of spiral wave tips in regions devoid of spontaneously firing (pacemaker) cells. Furthermore, we observe local inhomogeneities in the distribution of spiral chiralities, again induced by the pacemaker distribution. We show that these findings can be explained by a simple geometrical model of spiral wave generation.
Do predictions from Species Sensitivity Distributions match with field data?

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Smetanová, S.; Bláha, L.; Liess, M.; Schäfer, R.B.; Beketov, M.A.

2014-01-01

Species Sensitivity Distribution (SSD) is a statistical model that can be used to predict effects of contaminants on biological communities, but only few comparisons of this model with field studies have been conducted so far. In the present study we used measured pesticides concentrations from streams in Germany, France, and Finland, and we used SSD to calculate msPAF (multiple substance potentially affected fraction) values based on maximum toxic stress at localities. We compared these SSD-based predictions with the actual effects on stream invertebrates quantified by the SPEAR pesticides bioindicator. The results show that the msPAFs correlated well with the bioindicator, however, the generally accepted SSD threshold msPAF of 0.05 (5% of species are predicted to be affected) severely underestimated the observed effects (msPAF values causing significant effects are 2–1000-times lower). These results demonstrate that validation with field data is required to define the appropriate thresholds for SSD predictions. - Highlights: • We validated the statistical model Species Sensitivity Distribution with field data. • Good correlation was found between the model predictions and observed effects. • But, the generally accepted threshold msPAF 0.05 severely underestimated the effects. - Comparison of the SSD-based prediction with the field data evaluated with the SPEAR pesticides index shows that SSD threshold msPAF of 0.05 severely underestimates the effects observed in the field
Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change.

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Wang, Wen J; He, Hong S; Thompson, Frank R; Spetich, Martin A; Fraser, Jacob S

2018-09-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are not well represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts. We investigate how species biological traits and environmental heterogeneity affect species distribution shifts. We used a species-specific, spatially explicit forest dynamic model LANDIS PRO, which incorporates site-scale tree species demography and competition, landscape-scale dispersal and disturbances, and regional-scale abiotic controls, to simulate the distribution shifts of four representative tree species with distinct biological traits in the central hardwood forest region of United States. Our results suggested that biological traits (e.g., dispersal capacity, maturation age) were important for determining tree species distribution shifts. Environmental heterogeneity, on average, reduced shift rates by 8% compared to perfect environmental conditions. The average distribution shift rates ranged from 24 to 200myear -1 under climate change scenarios, implying that many tree species may not able to keep up with climate change because of limited dispersal capacity, long generation time, and environmental heterogeneity. We suggest that climate-distribution models should include species demographic processes (e.g., fecundity, dispersal, colonization), biological traits (e.g., dispersal capacity, maturation age), and environmental heterogeneity (e.g., habitat fragmentation) to improve future predictions of species distribution shifts in response to changing climates. Copyright © 2018 Elsevier B.V. All rights reserved.
Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution

International Nuclear Information System (INIS)

Yang Yong; Xing Lei

2005-01-01

It is well known that the spatial biology distribution (e.g., clonogen density, radiosensitivity, tumor proliferation rate, functional importance) in most tumors and sensitive structures is heterogeneous. Recent progress in biological imaging is making the mapping of this distribution increasingly possible. The purpose of this work is to establish a theoretical framework to quantitatively incorporate the spatial biology data into intensity modulated radiation therapy (IMRT) inverse planning. In order to implement this, we first derive a general formula for determining the desired dose to each tumor voxel for a known biology distribution of the tumor based on a linear-quadratic model. The desired target dose distribution is then used as the prescription for inverse planning. An objective function with the voxel-dependent prescription is constructed with incorporation of the nonuniform dose prescription. The functional unit density distribution in a sensitive structure is also considered phenomenologically when constructing the objective function. Two cases with different hypothetical biology distributions are used to illustrate the new inverse planning formalism. For comparison, treatments with a few uniform dose prescriptions and a simultaneous integrated boost are also planned. The biological indices, tumor control probability (TCP) and normal tissue complication probability (NTCP), are calculated for both types of plans and the superiority of the proposed technique over the conventional dose escalation scheme is demonstrated. Our calculations revealed that it is technically feasible to produce deliberately nonuniform dose distributions with consideration of biological information. Compared with the conventional dose escalation schemes, the new technique is capable of generating biologically conformal IMRT plans that significantly improve the TCP while reducing or keeping the NTCPs at their current levels. Biologically conformal radiation therapy (BCRT
Predicting biological system objectives de novo from internal state measurements

Directory of Open Access Journals (Sweden)

Maranas Costas D

2008-01-01

Full Text Available Abstract Background Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth and the subsequent application of linear programming (LP to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning. Results We present a novel method called Biological Objective Solution Search (BOSS for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, BOSS identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant in silico flux distribution and available experimental (e.g., isotopomer flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of Saccharomyces cerevisiae. Conclusion We illustrate how BOSS offers insight into the functional organization of biochemical networks
Biologically effective dose distribution based on the linear quadratic model and its clinical relevance

International Nuclear Information System (INIS)

Lee, Steve P.; Leu, Min Y.; Smathers, James B.; McBride, William H.; Parker, Robert G.; Withers, H. Rodney

1995-01-01

Purpose: Radiotherapy plans based on physical dose distributions do not necessarily entirely reflect the biological effects under various fractionation schemes. Over the past decade, the linear-quadratic (LQ) model has emerged as a convenient tool to quantify biological effects for radiotherapy. In this work, we set out to construct a mechanism to display biologically oriented dose distribution based on the LQ model. Methods and Materials: A computer program that converts a physical dose distribution calculated by a commercially available treatment planning system to a biologically effective dose (BED) distribution has been developed and verified against theoretical calculations. This software accepts a user's input of biological parameters for each structure of interest (linear and quadratic dose-response and repopulation kinetic parameters), as well as treatment scheme factors (number of fractions, fractional dose, and treatment time). It then presents a two-dimensional BED display in conjunction with anatomical structures. Furthermore, to facilitate clinicians' intuitive comparison with conventional fractionation regimen, a conversion of BED to normalized isoeffective dose (NID) is also allowed. Results: Two sample cases serve to illustrate the application of our tool in clinical practice. (a) For an orthogonal wedged pair of x-ray beams treating a maxillary sinus tumor, the biological effect at the ipsilateral mandible can be quantified, thus illustrates the so-called 'double-trouble' effects very well. (b) For a typical four-field, evenly weighted prostate treatment using 10 MV x-rays, physical dosimetry predicts a comparable dose at the femoral necks between an alternate two-fields/day and four-fields/day schups. However, our BED display reveals an approximate 21% higher BED for the two-fields/day scheme. This excessive dose to the femoral necks can be eliminated if the treatment is delivered with a 3:2 (anterio-posterior/posterio-anterior (AP
Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

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Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.
Biological instability in a chlorinated drinking water distribution network.

Science.gov (United States)

Nescerecka, Alina; Rubulis, Janis; Vital, Marius; Juhna, Talis; Hammes, Frederik

2014-01-01

The purpose of a drinking water distribution system is to deliver drinking water to the consumer, preferably with the same quality as when it left the treatment plant. In this context, the maintenance of good microbiological quality is often referred to as biological stability, and the addition of sufficient chlorine residuals is regarded as one way to achieve this. The full-scale drinking water distribution system of Riga (Latvia) was investigated with respect to biological stability in chlorinated drinking water. Flow cytometric (FCM) intact cell concentrations, intracellular adenosine tri-phosphate (ATP), heterotrophic plate counts and residual chlorine measurements were performed to evaluate the drinking water quality and stability at 49 sampling points throughout the distribution network. Cell viability methods were compared and the importance of extracellular ATP measurements was examined as well. FCM intact cell concentrations varied from 5×10(3) cells mL(-1) to 4.66×10(5) cells mL(-1) in the network. While this parameter did not exceed 2.1×10(4) cells mL(-1) in the effluent from any water treatment plant, 50% of all the network samples contained more than 1.06×10(5) cells mL(-1). This indisputably demonstrates biological instability in this particular drinking water distribution system, which was ascribed to a loss of disinfectant residuals and concomitant bacterial growth. The study highlights the potential of using cultivation-independent methods for the assessment of chlorinated water samples. In addition, it underlines the complexity of full-scale drinking water distribution systems, and the resulting challenges to establish the causes of biological instability.
Assessment of subchannel code ASSERT-PV for flow-distribution predictions

International Nuclear Information System (INIS)

Nava-Dominguez, A.; Rao, Y.F.; Waddington, G.M.

2014-01-01

Highlights: • Assessment of the subchannel code ASSERT-PV 3.2 for the prediction of flow distribution. • Open literature and in-house experimental data to quantify ASSERT-PV predictions. • Model changes assessed against vertical and horizontal flow experiments. • Improvement of flow-distribution predictions under CANDU-relevant conditions. - Abstract: This paper reports an assessment of the recently released subchannel code ASSERT-PV 3.2 for the prediction of flow-distribution in fuel bundles, including subchannel void fraction, quality and mass fluxes. Experimental data from open literature and from in-house tests are used to assess the flow-distribution models in ASSERT-PV 3.2. The prediction statistics using the recommended model set of ASSERT-PV 3.2 are compared to those from previous code versions. Separate-effects sensitivity studies are performed to quantify the contribution of each flow-distribution model change or enhancement to the improvement in flow-distribution prediction. The assessment demonstrates significant improvement in the prediction of flow-distribution in horizontal fuel channels containing CANDU bundles
Assessment of subchannel code ASSERT-PV for flow-distribution predictions

Energy Technology Data Exchange (ETDEWEB)

Nava-Dominguez, A., E-mail: navadoma@aecl.ca; Rao, Y.F., E-mail: raoy@aecl.ca; Waddington, G.M., E-mail: waddingg@aecl.ca

2014-08-15

Highlights: • Assessment of the subchannel code ASSERT-PV 3.2 for the prediction of flow distribution. • Open literature and in-house experimental data to quantify ASSERT-PV predictions. • Model changes assessed against vertical and horizontal flow experiments. • Improvement of flow-distribution predictions under CANDU-relevant conditions. - Abstract: This paper reports an assessment of the recently released subchannel code ASSERT-PV 3.2 for the prediction of flow-distribution in fuel bundles, including subchannel void fraction, quality and mass fluxes. Experimental data from open literature and from in-house tests are used to assess the flow-distribution models in ASSERT-PV 3.2. The prediction statistics using the recommended model set of ASSERT-PV 3.2 are compared to those from previous code versions. Separate-effects sensitivity studies are performed to quantify the contribution of each flow-distribution model change or enhancement to the improvement in flow-distribution prediction. The assessment demonstrates significant improvement in the prediction of flow-distribution in horizontal fuel channels containing CANDU bundles.
Toward scalable parts families for predictable design of biological circuits.

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Lucks, Julius B; Qi, Lei; Whitaker, Weston R; Arkin, Adam P

2008-12-01

Our current ability to engineer biological circuits is hindered by design cycles that are costly in terms of time and money, with constructs failing to operate as desired, or evolving away from the desired function once deployed. Synthetic biologists seek to understand biological design principles and use them to create technologies that increase the efficiency of the genetic engineering design cycle. Central to the approach is the creation of biological parts--encapsulated functions that can be composited together to create new pathways with predictable behaviors. We define five desirable characteristics of biological parts--independence, reliability, tunability, orthogonality and composability, and review studies of small natural and synthetic biological circuits that provide insights into each of these characteristics. We propose that the creation of appropriate sets of families of parts with these properties is a prerequisite for efficient, predictable engineering of new function in cells and will enable a large increase in the sophistication of genetic engineering applications.

A distributed approach for parameters estimation in System Biology models

International Nuclear Information System (INIS)

Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.

2009-01-01

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.
Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model.

Science.gov (United States)

Jaikuna, Tanwiwat; Khadsiri, Phatchareewan; Chawapun, Nisa; Saekho, Suwit; Tharavichitkul, Ekkasit

2017-02-01

To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL) model. The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR), and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD 2 ) was calculated using biological effective dose (BED) based on the LQL model. The software calculation and the manual calculation were compared for EQD 2 verification with pair t -test statistical analysis using IBM SPSS Statistics version 22 (64-bit). Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS) in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV) determined by D 90% , 0.56% in the bladder, 1.74% in the rectum when determined by D 2cc , and less than 1% in Pinnacle. The difference in the EQD 2 between the software calculation and the manual calculation was not significantly different with 0.00% at p -values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT) and 0.240, 0.320, and 0.849 for brachytherapy (BT) in HR-CTV, bladder, and rectum, respectively. The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.
Prediction of oil droplet size distribution in agitated aquatic environments

International Nuclear Information System (INIS)

Khelifa, A.; Lee, K.; Hill, P.S.

2004-01-01

Oil spilled at sea undergoes many transformations based on physical, biological and chemical processes. Vertical dispersion is the hydrodynamic mechanism controlled by turbulent mixing due to breaking waves, vertical velocity, density gradients and other environmental factors. Spilled oil is dispersed in the water column as small oil droplets. In order to estimate the mass of an oil slick in the water column, it is necessary to know how the droplets formed. Also, the vertical dispersion and fate of oil spilled in aquatic environments can be modelled if the droplet-size distribution of the oil droplets is known. An oil spill remediation strategy can then be implemented. This paper presented a newly developed Monte Carlo model to predict droplet-size distribution due to Brownian motion, turbulence and a differential settling at equilibrium. A kinematic model was integrated into the proposed model to simulate droplet breakage. The key physical input of the model is the maximum droplet size permissible in the simulation. Laboratory studies were found to be in good agreement with field studies. 26 refs., 1 tab., 5 figs
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Science.gov (United States)

Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

2018-05-17

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.
Particle size distribution of iron nanomaterials in biological medium by SR-SAXS method

International Nuclear Information System (INIS)

Jing Long; Feng Weiyue; Wang Bing; Wang Meng; Ouyang Hong; Zhao Yuliang; Chai Zhifang; Wang Yun; Wang Huajiang; Zhu Motao; Wu Zhonghua

2009-01-01

A better understanding of biological effects of nanomaterials in organisms requests knowledge of the physicochemical properties of nanomaterials in biological systems. Affected by high concentration salts and proteins in biological medium, nanoparticles are much easy to agglomerate,hence the difficulties in characterizing size distribution of the nanomaterials in biological medium.In this work, synchrotron radiation small angle X-ray scattering(SR-SAXS) was used to determine size distributions of Fe, Fe 2 O 3 and Fe 3 O 4 nanoparticles of various concentrations in PBS and DMEM culture medium. The results show that size distributions of the nanomaterials could perfectly analyzed by SR-SAXS. The SR-SAXS data were not affected by the particle content and types of the dispersion medium.It is concluded that SR-SAXS can be used for size measurement of nanomaterials in unstable dispersion systems. (authors)
Quantifying the connectivity of scale-free and biological networks

Energy Technology Data Exchange (ETDEWEB)

Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

2004-07-01

Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.
Predictive modelling of complex agronomic and biological systems.

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Keurentjes, Joost J B; Molenaar, Jaap; Zwaan, Bas J

2013-09-01

Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead. © 2013 John Wiley & Sons Ltd.
Popularity Prediction Tool for ATLAS Distributed Data Management

Science.gov (United States)

Beermann, T.; Maettig, P.; Stewart, G.; Lassnig, M.; Garonne, V.; Barisits, M.; Vigne, R.; Serfon, C.; Goossens, L.; Nairz, A.; Molfetas, A.; Atlas Collaboration

2014-06-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.
Popularity prediction tool for ATLAS distributed data management

International Nuclear Information System (INIS)

Beermann, T; Maettig, P; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.
Distribution and Biological Effects of Nanoparticles in the Reproductive System.

Science.gov (United States)

Liu, Ying; Li, Hongxia; Xiao, Kai

2016-01-01

Nanoparticles have shown great potential in biomedical applications such as imaging probes and drug delivery. However, the increasing use of nanoparticles has raised concerns about their adverse effects on human health and environment. Reproductive tissues and gametes represent highly delicate biological systems with the essential function of transmitting genetic information to the offspring, which is highly sensitive to environmental toxicants. This review aims to summarzie the penetration of physiological barriers (blood-testis barrier and placental barrier), distribution and biological effects of nanoparticles in the reproductive system, which is essential to control the beneficial effects of nanoparticles applications and to avoid their adverse effects on the reproductive system. We referred to a large number of relevant peer-reviewed research articles about the reproductive toxicity of nanoparticles. The comprehensive information was summarized into two parts: physiological barrier penetration and biological effects of nanoparticles in male or female reproductive system; distribution and metabolism of nanoparticles in the reproductive system. The representative examples were also presented in four tables. The in vitro and in vivo studies imply that some nanoparticles are able to cross the blood-testis barrier or placental barrier, and their penetration depends on the physicochemical characteristics of nanoparticles (e.g., composition, shape, particle size and surface coating). The toxicity assays indicate that nanoparticles might induce adverse physiological effects and impede fertility or embryogenesis. The barrier penetration, adverse physiological effects, distribution and metabolism are closely related to physicochemical characteristics of nanoparticles. Further systematic and mechanistic studies using well-characterized nanoparticles, relevant administration routes, and doses relevant to the expected exposure level are required to improve our
An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems.

Science.gov (United States)

Lu, Chuan; King, Ross D

2009-08-15

Distribution analysis is one of the most basic forms of statistical analysis. Thanks to improved analytical methods, accurate and extensive quantitative measurements can now be made of the mRNA, protein and metabolite from biological systems. Here, we report a large-scale analysis of the population abundance distributions of the transcriptomes, proteomes and metabolomes from varied biological systems. We compared the observed empirical distributions with a number of distributions: power law, lognormal, loglogistic, loggamma, right Pareto-lognormal (PLN) and double PLN (dPLN). The best-fit for mRNA, protein and metabolite population abundance distributions was found to be the dPLN. This distribution behaves like a lognormal distribution around the centre, and like a power law distribution in the tails. To better understand the cause of this observed distribution, we explored a simple stochastic model based on geometric Brownian motion. The distribution indicates that multiplicative effects are causally dominant in biological systems. We speculate that these effects arise from chemical reactions: the central-limit theorem then explains the central lognormal, and a number of possible mechanisms could explain the long tails: positive feedback, network topology, etc. Many of the components in the central lognormal parts of the empirical distributions are unidentified and/or have unknown function. This indicates that much more biology awaits discovery.
MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

Science.gov (United States)

Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

2017-01-01

Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any
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.
Applications of contact predictions to structural biology

Directory of Open Access Journals (Sweden)

Felix Simkovic

2017-05-01

methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.
Application of 'SPICE' to predict temperature distribution in heat pipes

Energy Technology Data Exchange (ETDEWEB)

Li, H M; Liu, Y; Damodaran, M [Nanyang Technological Univ., Singapore (SG). School of Mechanical and Production Engineering

1991-11-01

This article presents a new alternative approach to predict temperature distribution in heat pipes. In this method, temperature distribution in a heat pipe, modelled as an analogous electrical circuit, is predicted by applying SPICE, a general-purpose circuit simulation program. SPICE is used to simulate electrical circuit designs before the prototype is assembled. Useful predictions are obtained for heat pipes with and without adiabatic sections and for heat pipes with various evaporator and condenser lengths. Comparison of the predicted results with experiments demonstrates fairly good agreement. It is also shown how interdisciplinary developments could be used appropriately. (author).
Clinical history and biologic age predicted falls better than objective functional tests.

Science.gov (United States)

Gerdhem, Paul; Ringsberg, Karin A M; Akesson, Kristina; Obrant, Karl J

2005-03-01

Fall risk assessment is important because the consequences, such as a fracture, may be devastating. The objective of this study was to find the test or tests that best predicted falls in a population-based sample of elderly women. The fall-predictive ability of a questionnaire, a subjective estimate of biologic age and objective functional tests (gait, balance [Romberg and sway test], thigh muscle strength, and visual acuity) were compared in 984 randomly selected women, all 75 years of age. A recalled fall was the most important predictor for future falls. Only recalled falls and intake of psycho-active drugs independently predicted future falls. Women with at least five of the most important fall predictors (previous falls, conditions affecting the balance, tendency to fall, intake of psychoactive medication, inability to stand on one leg, high biologic age) had an odds ratio of 11.27 (95% confidence interval 4.61-27.60) for a fall (sensitivity 70%, specificity 79%). The more time-consuming objective functional tests were of limited importance for fall prediction. A simple clinical history, the inability to stand on one leg, and a subjective estimate of biologic age were more important as part of the fall risk assessment.
Genomic Signal Processing: Predicting Basic Molecular Biological Principles

Science.gov (United States)

Alter, Orly

2005-03-01

Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of
Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

Science.gov (United States)

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

2017-08-10

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of
Predictive Analytics for Coordinated Optimization in Distribution Systems

Energy Technology Data Exchange (ETDEWEB)

Yang, Rui [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2018-04-13

This talk will present NREL's work on developing predictive analytics that enables the optimal coordination of all the available resources in distribution systems to achieve the control objectives of system operators. Two projects will be presented. One focuses on developing short-term state forecasting-based optimal voltage regulation in distribution systems; and the other one focuses on actively engaging electricity consumers to benefit distribution system operations.
Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

Science.gov (United States)

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

On-line test of power distribution prediction system for boiling water reactors

International Nuclear Information System (INIS)

Nishizawa, Y.; Kiguchi, T.; Kobayashi, S.; Takumi, K.; Tanaka, H.; Tsutsumi, R.; Yokomi, M.

1982-01-01

A power distribution prediction system for boiling water reactors has been developed and its on-line performance test has proceeded at an operating commercial reactor. This system predicts the power distribution or thermal margin in advance of control rod operations and core flow rate change. This system consists of an on-line computer system, an operator's console with a color cathode-ray tube, and plant data input devices. The main functions of this system are present power distribution monitoring, power distribution prediction, and power-up trajectory prediction. The calculation method is based on a simplified nuclear thermal-hydraulic calculation, which is combined with a method of model identification to the actual reactor core state. It has been ascertained by the on-line test that the predicted power distribution (readings of traversing in-core probe) agrees with the measured data within 6% root-mean-square. The computing time required for one prediction calculation step is less than or equal to 1.5 min by an HIDIC-80 on-line computer
Biological distribution of 51Cr-heparin

International Nuclear Information System (INIS)

Almeida, M.A.T.M. de.

1979-01-01

The kinetics of heparin in normal Wistar rats using the radioactive tracer 51 Cr, has been studied. The labeled and purified 51 Cr-heparin was injected into rats intravenously and by intraperitoneal injection. In measuring the radioactivity of organs it was possible to conclude that the tissues rich in mast cells, liver and spleen, were found to take up the greater amounts of heparin. The curve that represents the logarithm of the concentration of heparin versus time is biexponential. The half-lives of the two exponential were determined. The volume of distribution, the rate constant and the renal clearance were determined by the values of the plasma levels and urinary excretions. The biological half-time, the turnover rate and the turnover time were determined by measuring the residual radioactivity of the total body and urinary excretions. With the data obtained from the mentioned experiments a compartmental model was performed in which the plasma is the central compartment for the distribution of the drug, exchanging with another extraplasmatic compartment and finally the drug being stored in reticulo endothelial system cells. (Author) [pt
A Predictive Distribution Model for Cooperative Braking System of an Electric Vehicle

Directory of Open Access Journals (Sweden)

Hongqiang Guo

2014-01-01

Full Text Available A predictive distribution model for a series cooperative braking system of an electric vehicle is proposed, which can solve the real-time problem of the optimum braking force distribution. To get the predictive distribution model, firstly three disciplines of the maximum regenerative energy recovery capability, the maximum generating efficiency and the optimum braking stability are considered, then an off-line process optimization stream is designed, particularly the optimal Latin hypercube design (Opt LHD method and radial basis function neural network (RBFNN are utilized. In order to decouple the variables between different disciplines, a concurrent subspace design (CSD algorithm is suggested. The established predictive distribution model is verified in a dynamic simulation. The off-line optimization results show that the proposed process optimization stream can improve the regenerative energy recovery efficiency, and optimize the braking stability simultaneously. Further simulation tests demonstrate that the predictive distribution model can achieve high prediction accuracy and is very beneficial for the cooperative braking system.
The biology of eukaryotic promoter prediction - a review

DEFF Research Database (Denmark)

Pedersen, Anders Gorm; Baldi, Pierre; Chauvin, Yves

1999-01-01

between functional promoters has been estimated to be in the range of 30-40 kilobases. Although it is conceivable that some of these predicted promoters correspond to cryptic initiation sites that are used in vivo, it is likely that most are false positives. This suggests that it is important to carefully......Computational prediction of eukaryotic promoters from the nucleotide sequence is one of the most attractive problems in sequence analysis today, but it is also a very difficult one. Thus, current methods predict in the order of one promoter per kilobase in human DNA, while the average distance...... reconsider the biological data that forms the basis of current algorithms, and we here present a review of data that may be useful in this regard. The review covers the following topics: (1) basal transcription and core promoters, (2) activated transcription and transcription factor binding sites, (3) Cp...
Estimating Predictive Variance for Statistical Gas Distribution Modelling

International Nuclear Information System (INIS)

Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo

2009-01-01

Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.
Distributed model predictive control made easy

CERN Document Server

Negenborn, Rudy

2014-01-01

The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Maxent modelling for predicting the potential distribution of Thai Palms

DEFF Research Database (Denmark)

Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach

2011-01-01

on presence data. The aim was to identify potential hot spot areas, assess the determinants of palm distribution ranges, and provide a firmer knowledge base for future conservation actions. We focused on a relatively small number of climatic, environmental and spatial variables in order to avoid...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...
On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

Science.gov (United States)

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.
Thematic and spatial resolutions affect model-based predictions of tree species distribution.

Science.gov (United States)

Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.
Predicting the distribution of spiral waves from cell properties in a developmental-path model of Dictyostelium pattern formation.

Directory of Open Access Journals (Sweden)

Daniel Geberth

2009-07-01

Full Text Available The slime mold Dictyostelium discoideum is one of the model systems of biological pattern formation. One of the most successful answers to the challenge of establishing a spiral wave pattern in a colony of homogeneously distributed D. discoideum cells has been the suggestion of a developmental path the cells follow (Lauzeral and coworkers. This is a well-defined change in properties each cell undergoes on a longer time scale than the typical dynamics of the cell. Here we show that this concept leads to an inhomogeneous and systematic spatial distribution of spiral waves, which can be predicted from the distribution of cells on the developmental path. We propose specific experiments for checking whether such systematics are also found in data and thus, indirectly, provide evidence of a developmental path.
Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.
The biology and distribution of the monkfish Lophius vomerinus off ...

African Journals Online (AJOL)

The monkfish Lophius vomerinus is economically the most important bycatch species in the South African demersal hake fishery. To assist in the development of a bycatch management plan for the species, age and growth characteristics, reproductive and feeding biology, and distribution patterns were investigated.
Evaluation of the biological and scanning distribution of hydroxyapatite-153Sm radiotherapeutic agent

International Nuclear Information System (INIS)

Herrera, J.; Paredes, N.; Portilla, A.; Miranda, J.; Carrillo, D.

1999-01-01

Fixation of 153 Sm labeled hydroxyapatite (HA) in the synovial capsule and extra articular localization were evaluated by means of biological distribution tests and gamma scanning studies. These were carried out using HA- 153 Sm with particle size ranging between 5 and μm, and radiochemical purity above 99%. Animal models used were wistar rats and new zealand rabbits. Rabbits were injected with 7,4 MBq of HA- 153 Sm while rats received between 1,85 and 92,6 MBq of HA- 153 Sm. In both cases injection was given in the intra articular area. After injection, scanning images were obtained in rabbits on the 1 st , 3 rd and 7 st day and in rats on the 2 nd and 7 th day. Biological distribution studies are conducted in the 2 hours to 9 days range in rats and one the 7 th day in rabbits. No extra articular localization of HA- 153 Sm was found in scanning conducted on rabbits by the 1 st , 3 rd and 7 st day after injection, neither on rats by the 2 nd and 7 th day. Biological distributions for rabbits and rats show localization above 99% in the intra articular area, during the evaluated periods of time. The evaluations of the biological distribution and the scintigraphic images show that fixation of HA- 153 Sm in the synovial capsule up to the 9 th day is very high
Predictive Distribution of the Dirichlet Mixture Model by the Local Variational Inference Method

DEFF Research Database (Denmark)

Ma, Zhanyu; Leijon, Arne; Tan, Zheng-Hua

2014-01-01

the predictive likelihood of the new upcoming data, especially when the amount of training data is small. The Bayesian estimation of a Dirichlet mixture model (DMM) is, in general, not analytically tractable. In our previous work, we have proposed a global variational inference-based method for approximately...... calculating the posterior distributions of the parameters in the DMM analytically. In this paper, we extend our previous study for the DMM and propose an algorithm to calculate the predictive distribution of the DMM with the local variational inference (LVI) method. The true predictive distribution of the DMM...... is analytically intractable. By considering the concave property of the multivariate inverse beta function, we introduce an upper-bound to the true predictive distribution. As the global minimum of this upper-bound exists, the problem is reduced to seek an approximation to the true predictive distribution...
Stand diameter distribution modelling and prediction based on Richards function.

Directory of Open Access Journals (Sweden)

Ai-guo Duan

Full Text Available The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM or maximum likelihood estimates method (MLEM were applied to estimate the parameters of models, and the parameter prediction method (PPM and parameter recovery method (PRM were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1 R distribution presented a more accurate simulation than three-parametric Weibull function; (2 the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3 the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4 the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.
Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

Directory of Open Access Journals (Sweden)

Luan Yihui

2009-09-01

Full Text Available Abstract Background Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Results Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Conclusion Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.
Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach.

Science.gov (United States)

Wang, Wenhui; Nunez-Iglesias, Juan; Luan, Yihui; Sun, Fengzhu

2009-09-03

Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks. Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks. Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.
Predicting Polylepis distribution: vulnerable and increasingly important Andean woodlands

Directory of Open Access Journals (Sweden)

Brian R. Zutta

2012-11-01

Full Text Available Polylepis woodlands are a vital resource for preserving biodiversity and hydrological functions, which will be altered by climate change and challenge the sustainability of local human communities. However, these highaltitude Andean ecosystems are becoming increasingly vulnerable due to anthropogenic pressure including fragmentation, deforestation and the increase in livestock. Predicting the distribution of native woodlands has become increasingly important to counteract the negative effects of climate change through reforestation and conservation. The objective of this study was to develop and analyze the distribution models of two species that form extensive woodlands along the Andes, namely Polylepis sericea and P. weberbaueri. This study utilized the program Maxent, climate and remotely sensed environmental layers at 1 km resolution. The predicted distribution model for P. sericea indicated that the species could be located in a variety of habitats along the Andean Cordillera, while P. weberbaueri was restricted to the high elevations of southern Peru and Bolivia. For both species, elevation and temperature metrics were the most significant factors for predicted distribution. Further model refinement of Polylepis and other Andean species using increasingly available satellite data demonstrate the potential to help define areas of diversity and improve conservation strategies for the Andes.
Exploitation of complex network topology for link prediction in biological interactomes

KAUST Repository

Alanis Lobato, Gregorio

2014-01-01

In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable
[Effects of sampling plot number on tree species distribution prediction under climate change].

Science.gov (United States)

Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

2013-05-01

Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

Biomine: predicting links between biological entities using network models of heterogeneous databases

Directory of Open Access Journals (Sweden)

Eronen Lauri

2012-06-01

Full Text Available Abstract Background Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Results Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. Conclusions The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable
Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

Science.gov (United States)

Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A

2012-07-02

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of
Hybrid ATDL-gamma distribution model for predicting area source acid gas concentrations

Energy Technology Data Exchange (ETDEWEB)

Jakeman, A J; Taylor, J A

1985-01-01

An air quality model is developed to predict the distribution of concentrations of acid gas in an urban airshed. The model is hybrid in character, combining reliable features of a deterministic ATDL-based model with statistical distributional approaches. The gamma distribution was identified from a range of distributional models as the best model. The paper shows that the assumptions of a previous hybrid model may be relaxed and presents a methodology for characterizing the uncertainty associated with model predictions. Results are demonstrated for the 98-percentile predictions of 24-h average data over annual periods at six monitoring sites. This percentile relates to the World Health Organization goal for acid gas concentrations.
Predicting plant distribution in an heterogeneous Alpine landscape: does soil matter?

Science.gov (United States)

Buri, Aline; Cianfrani, Carmen; Pradervand, Jean-Nicolas; Guisan, Antoine

2016-04-01

Topographic and climatic factors are usually used to predict plant distribution because they are known to explain their presence or absence. Soil properties have been widely shown to influence plant growth and distributions. However, they are rarely taken into account as predictors of plant species distribution models (SDM) in an edaphically heterogeneous landscape. Or, when it happens, interpolation techniques are used to project soil factors in space. In heterogeneous landscape, such as in the Alps region, where soil properties change abruptly as a function of environmental conditions over short distances, interpolation techniques require a huge quantities of samples to be efficient. This is costly and time consuming, and bring more errors than predictive approach for an equivalent number of samples. In this study we aimed to assess whether soil proprieties may be generalized over entire mountainous geographic extents and can improve predictions of plant distributions over traditional topo-climatic predictors. First, we used a predictive approach to map two soil proprieties based on field measurements in the western Swiss Alps region; the soil pH and the ratio of stable isotopes 13C/12C (called δ13CSOM). We used ensemble forecasting techniques combining together several predictive algorithms to build models of the geographic variation in the values of both soil proprieties and projected them in the entire study area. As predictive factors, we employed very high resolution topo-climatic data. In a second step, output maps from the previous task were used as an input for vegetation regional models. We integrated the predicted soil proprieties to a set of basic topo-climatic predictors known to be important to model plants species. Then we modelled the distribution of 156 plant species inhabiting the study area. Finally, we compared the quality of the models having or not soil proprieties as predictors to evaluate their effect on the predictive power of our models
Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline.

Science.gov (United States)

Peñaranda, Diego A; Simonetti, Javier A

2015-06-01

The recognition that growing proportions of species worldwide are endangered has led to the development of comparative analyses to elucidate why some species are more prone to extinction than others. Understanding factors and patterns of species vulnerability might provide an opportunity to develop proactive conservation strategies. Such comparative analyses are of special concern at national scales because this is the scale at which most conservation initiatives take place. We applied powerful ensemble learning models to test for biological correlates of the risk of decline among the Bolivian mammals to understand species vulnerability at a national scale and to predict the population trend for poorly known species. Risk of decline was nonrandomly distributed: higher proportions of large-sized taxa were under decline, whereas small-sized taxa were less vulnerable. Body mass, mode of life (i.e., aquatic, terrestrial, volant), geographic range size, litter size, home range, niche specialization, and reproductive potential were strongly associated with species vulnerability. Moreover, we found interacting and nonlinear effects of key traits on the risk of decline of mammals at a national scale. Our model predicted 35 data-deficient species in decline on the basis of their biological vulnerability, which should receive more attention in order to prevent their decline. Our results highlight the relevance of comparative analysis at relatively narrow geographical scales, reveal previously unknown factors related to species vulnerability, and offer species-by-species outcomes that can be used to identify targets for conservation, especially for insufficiently known species. © 2015 Society for Conservation Biology.
Building predictive models of soil particle-size distribution

Directory of Open Access Journals (Sweden)

Alessandro Samuel-Rosa

2013-04-01

Full Text Available Is it possible to build predictive models (PMs of soil particle-size distribution (psd in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index. The PMs explained more than half of the data variance. This performance is similar to (or even better than that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd of soils in regions of complex geology.
Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2013-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...
Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...
Predictive access control for distributed computation

DEFF Research Database (Denmark)

Yang, Fan; Hankin, Chris; Nielson, Flemming

2013-01-01

We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....
Hafnium oxide nanoparticles: toward an in vitro predictive biological effect?

International Nuclear Information System (INIS)

Marill, Julie; Anesary, Naeemunnisa Mohamed; Zhang, Ping; Vivet, Sonia; Borghi, Elsa; Levy, Laurent; Pottier, Agnes

2014-01-01

Hafnium oxide, NBTXR3 nanoparticles were designed for high dose energy deposition within cancer cells when exposed to ionizing radiation. The purpose of this study was to assess the possibility of predicting in vitro the biological effect of NBTXR3 nanoparticles when exposed to ionizing radiation. Cellular uptake of NBTXR3 nanoparticles was assessed in a panel of human cancer cell lines (radioresistant and radiosensitive) by transmission electron microscopy. The radioenhancement of NBTXR3 nanoparticles was measured by the clonogenic survival assay. NBTXR3 nanoparticles were taken up by cells in a concentration dependent manner, forming clusters in the cytoplasm. Differential nanoparticle uptake was observed between epithelial and mesenchymal or glioblastoma cell lines. The dose enhancement factor increased with increase NBTXR3 nanoparticle concentration and radiation dose. Beyond a minimum number of clusters per cell, the radioenhancement of NBTXR3 nanoparticles could be estimated from the radiation dose delivered and the radiosensitivity of the cancer cell lines. Our preliminary results suggest a predictable in vitro biological effect of NBTXR3 nanoparticles exposed to ionizing radiation
DBH Prediction Using Allometry Described by Bivariate Copula Distribution

Science.gov (United States)

Xu, Q.; Hou, Z.; Li, B.; Greenberg, J. A.

2017-12-01

Forest biomass mapping based on single tree detection from the airborne laser scanning (ALS) usually depends on an allometric equation that relates diameter at breast height (DBH) with per-tree aboveground biomass. The incapability of the ALS technology in directly measuring DBH leads to the need to predict DBH with other ALS-measured tree-level structural parameters. A copula-based method is proposed in the study to predict DBH with the ALS-measured tree height and crown diameter using a dataset measured in the Lassen National Forest in California. Instead of exploring an explicit mathematical equation that explains the underlying relationship between DBH and other structural parameters, the copula-based prediction method utilizes the dependency between cumulative distributions of these variables, and solves the DBH based on an assumption that for a single tree, the cumulative probability of each structural parameter is identical. Results show that compared with the bench-marking least-square linear regression and the k-MSN imputation, the copula-based method obtains better accuracy in the DBH for the Lassen National Forest. To assess the generalization of the proposed method, prediction uncertainty is quantified using bootstrapping techniques that examine the variability of the RMSE of the predicted DBH. We find that the copula distribution is reliable in describing the allometric relationship between tree-level structural parameters, and it contributes to the reduction of prediction uncertainty.
Publication Growth in Biological Sub-Fields: Patterns, Predictability and Sustainability

Directory of Open Access Journals (Sweden)

Marco Pautasso

2012-11-01

Full Text Available Biologists are producing ever-increasing quantities of papers. The question arises of whether current rates of increase in scientific outputs are sustainable in the long term. I studied this issue using publication data from the Web of Science (1991–2010 for 18 biological sub-fields. In the majority of cases, an exponential regression explains more variation than a linear one in the number of papers published each year as a function of publication year. Exponential growth in publication numbers is clearly not sustainable. About 75% of the variation in publication growth among biological sub-fields over the two studied decades can be predicted by publication data from the first six years. Currently trendy fields such as structural biology, neuroscience and biomaterials cannot be expected to carry on growing at the current pace, because in a few decades they would produce more papers than the whole of biology combined. Synthetic and systems biology are problematic from the point of view of knowledge dissemination, because in these fields more than 80% of existing papers have been published over the last five years. The evidence presented here casts a shadow on how sustainable the recent increase in scientific publications can be in the long term.
Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

Science.gov (United States)

Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

2010-09-13

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.
Efficacy of a composite biological age score to predict ten-year survival among Kansas and Nebraska Mennonites.

Science.gov (United States)

Uttley, M; Crawford, M H

1994-02-01

In 1980 and 1981 Mennonite descendants of a group of Russian immigrants participated in a multidisciplinary study of biological aging. The Mennonites live in Goessel, Kansas, and Henderson, Nebraska. In 1991 the survival status of the participants was documented by each church secretary. Data are available for 1009 individuals, 177 of whom are now deceased. They ranged from 20 to 95 years in age when the data were collected. Biological ages were computed using a stepwise multiple regression procedure based on 38 variables previously identified as being related to survival, with chronological age as the dependent variable. Standardized residuals place participants in either a predicted-younger or a predicted-older group. The independence of the variables biological age and survival status is tested with the chi-square statistic. The significance of biological age differences between surviving and deceased Mennonites is determined by t test values. The two statistics provide consistent results. Predicted age group classification and survival status are related. The group of deceased participants is generally predicted to be older than the group of surviving participants, although neither statistic is significant for all subgroups of Mennonites. In most cases, however, individuals in the predicted-older groups are at a relatively higher risk of dying compared with those in the predicted-younger groups, although the increased risk is not always significant.
Biological lifestyle factors in adult distance education: predicting cognitive and learning performance

NARCIS (Netherlands)

Gijselaers, Jérôme

2015-01-01

Gijselaers, H. J. M. (2015, 20 October). Biological lifestyle factors in adult distance education: predicting cognitive and learning performance. Presentation given for the inter-faculty Data Science group at the Open University of the Netherlands, Heerlen, The Netherlands.
Quantum Mechanics predicts evolutionary biology.

Science.gov (United States)

Torday, J S

2018-07-01

Nowhere are the shortcomings of conventional descriptive biology more evident than in the literature on Quantum Biology. In the on-going effort to apply Quantum Mechanics to evolutionary biology, merging Quantum Mechanics with the fundamentals of evolution as the First Principles of Physiology-namely negentropy, chemiosmosis and homeostasis-offers an authentic opportunity to understand how and why physics constitutes the basic principles of biology. Negentropy and chemiosmosis confer determinism on the unicell, whereas homeostasis constitutes Free Will because it offers a probabilistic range of physiologic set points. Similarly, on this basis several principles of Quantum Mechanics also apply directly to biology. The Pauli Exclusion Principle is both deterministic and probabilistic, whereas non-localization and the Heisenberg Uncertainty Principle are both probabilistic, providing the long-sought after ontologic and causal continuum from physics to biology and evolution as the holistic integration recognized as consciousness for the first time. Copyright © 2018 Elsevier Ltd. All rights reserved.
Prediction of spatial distribution for some land use allometric ...

African Journals Online (AJOL)

Prediction of spatial distribution for some land use allometric characteristics in land use planning models with geostatistic and Geographical Information System (GIS) (Case study: Boein and Miandasht, Isfahan Province, Iran)
ASSERT and COBRA predictions of flow distribution in vertical bundles

International Nuclear Information System (INIS)

Tahir, A.; Carver, M.B.

1983-01-01

COBRA and ASSERT are subchannel codes which compute flow and enthalpy distributions in rod bundles. COBRA is a well known code, ASSERT is under development at CRNL. This paper gives a comparison of the two codes with boiling experiments in vertical seven rod bundles. ASSERT predictions of the void distribution are shown to be in good agreement with reported experimental results, while COBRA predictions are unsatisfactory. The mixing models in both COBRA and ASSERT are briefly discussed. The reasons for the failure of COBRA-IV and the success of ASSERT in simulating the experiments are highlighted
Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

Science.gov (United States)

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.
Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis.

Directory of Open Access Journals (Sweden)

Camille Mellin

Full Text Available The use of biological surrogates as proxies for biodiversity patterns is gaining popularity, particularly in marine systems where field surveys can be expensive and species richness high. Yet, uncertainty regarding their applicability remains because of inconsistency of definitions, a lack of standard methods for estimating effectiveness, and variable spatial scales considered. We present a Bayesian meta-analysis of the effectiveness of biological surrogates in marine ecosystems. Surrogate effectiveness was defined both as the proportion of surrogacy tests where predictions based on surrogates were better than random (i.e., low probability of making a Type I error; P and as the predictability of targets using surrogates (R(2. A total of 264 published surrogacy tests combined with prior probabilities elicited from eight international experts demonstrated that the habitat, spatial scale, type of surrogate and statistical method used all influenced surrogate effectiveness, at least according to either P or R(2. The type of surrogate used (higher-taxa, cross-taxa or subset taxa was the best predictor of P, with the higher-taxa surrogates outperforming all others. The marine habitat was the best predictor of R(2, with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a <10-km spatial scale, in low-complexity marine habitats such as soft bottoms, and using multivariate-based methods. Comparisons with terrestrial studies in terms of the methods used to study surrogates revealed that marine applications still ignore some problems with several widely used statistical approaches to surrogacy. Our study provides a benchmark for the reliable use of biological surrogates in marine ecosystems, and highlights directions for future development of biological surrogates in predicting biodiversity.

Prediction of monthly average global solar radiation based on statistical distribution of clearness index

International Nuclear Information System (INIS)

Ayodele, T.R.; Ogunjuyigbe, A.S.O.

2015-01-01

In this paper, probability distribution of clearness index is proposed for the prediction of global solar radiation. First, the clearness index is obtained from the past data of global solar radiation, then, the parameters of the appropriate distribution that best fit the clearness index are determined. The global solar radiation is thereafter predicted from the clearness index using inverse transformation of the cumulative distribution function. To validate the proposed method, eight years global solar radiation data (2000–2007) of Ibadan, Nigeria are used to determine the parameters of appropriate probability distribution for clearness index. The calculated parameters are then used to predict the future monthly average global solar radiation for the following year (2008). The predicted values are compared with the measured values using four statistical tests: the Root Mean Square Error (RMSE), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and the coefficient of determination (R"2). The proposed method is also compared to the existing regression models. The results show that logistic distribution provides the best fit for clearness index of Ibadan and the proposed method is effective in predicting the monthly average global solar radiation with overall RMSE of 0.383 MJ/m"2/day, MAE of 0.295 MJ/m"2/day, MAPE of 2% and R"2 of 0.967. - Highlights: • Distribution of clearnes index is proposed for prediction of global solar radiation. • The clearness index is obtained from the past data of global solar radiation. • The parameters of distribution that best fit the clearness index are determined. • Solar radiation is predicted from the clearness index using inverse transformation. • The method is effective in predicting the monthly average global solar radiation.
Predicting the probability of slip in gait: methodology and distribution study.

Science.gov (United States)

Gragg, Jared; Yang, James

2016-01-01

The likelihood of a slip is related to the available and required friction for a certain activity, here gait. Classical slip and fall analysis presumed that a walking surface was safe if the difference between the mean available and required friction coefficients exceeded a certain threshold. Previous research was dedicated to reformulating the classical slip and fall theory to include the stochastic variation of the available and required friction when predicting the probability of slip in gait. However, when predicting the probability of a slip, previous researchers have either ignored the variation in the required friction or assumed the available and required friction to be normally distributed. Also, there are no published results that actually give the probability of slip for various combinations of required and available frictions. This study proposes a modification to the equation for predicting the probability of slip, reducing the previous equation from a double-integral to a more convenient single-integral form. Also, a simple numerical integration technique is provided to predict the probability of slip in gait: the trapezoidal method. The effect of the random variable distributions on the probability of slip is also studied. It is shown that both the required and available friction distributions cannot automatically be assumed as being normally distributed. The proposed methods allow for any combination of distributions for the available and required friction, and numerical results are compared to analytical solutions for an error analysis. The trapezoidal method is shown to be highly accurate and efficient. The probability of slip is also shown to be sensitive to the input distributions of the required and available friction. Lastly, a critical value for the probability of slip is proposed based on the number of steps taken by an average person in a single day.
The role of biological fertility in predicting family size

DEFF Research Database (Denmark)

Joffe, M; Key, J; Best, N

2009-01-01

for the first child. CONCLUSIONS: Within the limits of the available data quality, family size appears to be predicted by biological fertility, even after adjustment for maternal age, if the woman was at least 20 years old when the couple's first attempt at conception started. The contribution of behavioural......BACKGROUND: It is plausible that a couple's ability to achieve the desired number of children is limited by biological fertility, especially if childbearing is postponed. Family size has declined and semen quality may have deteriorated in much of Europe, although studies have found an increase....... Potential confounders were maternal age when unprotected sex began prior to the first birth, and maternal smoking. Desired family size was available in only one of the datasets. RESULTS: Couples with a TTP of at least 12 months tended to have smaller families, with odds ratios for the risk of not having...
Distributed estimation based on observations prediction in wireless sensor networks

KAUST Repository

Bouchoucha, Taha

2015-03-19

We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.
Exploitation of complex network topology for link prediction in biological interactomes

KAUST Repository

Alanis Lobato, Gregorio

2014-06-01

The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.
Cell-size distribution in epithelial tissue formation and homeostasis.

Science.gov (United States)

Puliafito, Alberto; Primo, Luca; Celani, Antonio

2017-03-01

How cell growth and proliferation are orchestrated in living tissues to achieve a given biological function is a central problem in biology. During development, tissue regeneration and homeostasis, cell proliferation must be coordinated by spatial cues in order for cells to attain the correct size and shape. Biological tissues also feature a notable homogeneity of cell size, which, in specific cases, represents a physiological need. Here, we study the temporal evolution of the cell-size distribution by applying the theory of kinetic fragmentation to tissue development and homeostasis. Our theory predicts self-similar probability density function (PDF) of cell size and explains how division times and redistribution ensure cell size homogeneity across the tissue. Theoretical predictions and numerical simulations of confluent non-homeostatic tissue cultures show that cell size distribution is self-similar. Our experimental data confirm predictions and reveal that, as assumed in the theory, cell division times scale like a power-law of the cell size. We find that in homeostatic conditions there is a stationary distribution with lognormal tails, consistently with our experimental data. Our theoretical predictions and numerical simulations show that the shape of the PDF depends on how the space inherited by apoptotic cells is redistributed and that apoptotic cell rates might also depend on size. © 2017 The Author(s).
Prediction method for thermal ratcheting of a cylinder subjected to axially moving temperature distribution

International Nuclear Information System (INIS)

Wada, Hiroshi; Igari, Toshihide; Kitade, Shoji.

1989-01-01

A prediction method was proposed for plastic ratcheting of a cylinder, which was subjected to axially moving temperature distribution without primary stress. First, a mechanism of this ratcheting was proposed, which considered the movement of temperature distribution as a driving force of this phenomenon. Predictive equations of the ratcheting strain for two representative temperature distributions were proposed based on this mechanism by assuming the elastic-perfectly-plastic material behavior. Secondly, an elastic-plastic analysis was made on a cylinder subjected to the representative two temperature distributions. Analytical results coincided well with the predicted results, and the applicability of the proposed equations was confirmed. (author)
Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa

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Clement Nyamunura Mweya

2013-10-01

Full Text Available Background: The East African region has experienced several Rift Valley fever (RVF outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. Methods: Diverse ecological niche modelling techniques have been developed for this purpose: we present a maximum entropy (Maxent approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Results: Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Conclusion: Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods.
Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa.

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Mweya, Clement Nyamunura; Kimera, Sharadhuli Iddi; Kija, John Bukombe; Mboera, Leonard E G

2013-01-01

The East African region has experienced several Rift Valley fever (RVF) outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. DIVERSE ECOLOGICAL NICHE MODELLING TECHNIQUES HAVE BEEN DEVELOPED FOR THIS PURPOSE: we present a maximum entropy (Maxent) approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex) responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods.
Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

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Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

2013-04-01

Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.
Biological traits explain the distribution and colonisation ability of the invasive shore crab Hemigrapsus takanoi

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Gothland, M.; Dauvin, J. C.; Denis, L.; Dufossé, F.; Jobert, S.; Ovaert, J.; Pezy, J. P.; Tous Rius, A.; Spilmont, N.

2014-04-01

Comprehending marine invasions requires a better knowledge of the biological traits of invasive species, and the future spread of invasive species may be predicted through comprehensive overviews of their distribution. This study thus presents the current distribution of a non-indigenous species, the Asian shore crab Hemigrapsus takanoi, as well as the species population characteristics (size distribution and cohorts), based on a five-year survey (2008-2012) along the French coast of the English Channel. Two large populations were found near harbours: one on the Opal Coast (where density reached 61 ± 22 ind.m-2, mean ± s.d., in Dunkirk harbour) and one on the Calvados coast (density up to 26 ± 6 ind.m-2, mean ± s.d, in Honfleur harbour). H. takanoi exhibited a short life cycle, a rapid growth, an early sexual maturity and a high adult mortality. These features, combined with previously described high fecundity and high dispersal ability, endow this species with an 'r-selected strategy'. This strategy, which usually characterises species with a high colonisation ability, would explain the success of H. takanoi for colonising the French coast of the Channel. However, the species was found only in harbours and their vicinity; H. takanoi thus exhibited a discontinuous distribution along the 700 km of coastline. These results are discussed regarding sediment preference and potential introduction vectors. Hemigrapsus takanoi is now considered as established on the French coast and further studies are needed to evaluate the consequences of its introduction on the structure and functioning of the impacted shores.
Novel non-invasive biological predictive index for liver fibrosis in hepatitis C virus genotype 4 patients

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Khattab, Mahmoud; Sakr, Mohamed Amin; Fattah, Mohamed Abdel; Mousa, Youssef; Soliman, Elwy; Breedy, Ashraf; Fathi, Mona; Gaber, Salwa; Altaweil, Ahmed; Osman, Ashraf; Hassouna, Ahmed; Motawea, Ibrahim

2016-01-01

AIM To investigate the diagnostic ability of a non-invasive biological marker to predict liver fibrosis in hepatitis C genotype 4 patients with high accuracy. METHODS A cohort of 332 patients infected with hepatitis C genotype 4 was included in this cross-sectional study. Fasting plasma glucose, insulin, C-peptide, and angiotensin-converting enzyme serum levels were measured. Insulin resistance was mathematically calculated using the homeostasis model of insulin resistance (HOMA-IR). RESULTS Fibrosis stages were distributed based on Metavir score as follows: F0 = 43, F1 = 136, F2 = 64, F3 = 45 and F4 = 44. Statistical analysis relied upon reclassification of fibrosis stages into mild fibrosis (F0-F) = 179, moderate fibrosis (F2) = 64, and advanced fibrosis (F3-F4) = 89. Univariate analysis indicated that age, log aspartate amino transaminase, log HOMA-IR and log platelet count were independent predictors of liver fibrosis stage (P < 0.0001). A stepwise multivariate discriminant functional analysis was used to drive a discriminative model for liver fibrosis. Our index used cut-off values of ≥ 0.86 and ≤ -0.31 to diagnose advanced and mild fibrosis, respectively, with receiving operating characteristics of 0.91 and 0.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio were: 73%, 91%, 75%, 90% and 8.0 respectively for advanced fibrosis, and 67%, 88%, 84%, 70% and 4.9, respectively, for mild fibrosis. CONCLUSION Our predictive model is easily available and reproducible, and predicted liver fibrosis with acceptable accuracy. PMID:27917265
Novel non-invasive biological predictive index for liver fibrosis in hepatitis C virus genotype 4 patients.

Science.gov (United States)

Khattab, Mahmoud; Sakr, Mohamed Amin; Fattah, Mohamed Abdel; Mousa, Youssef; Soliman, Elwy; Breedy, Ashraf; Fathi, Mona; Gaber, Salwa; Altaweil, Ahmed; Osman, Ashraf; Hassouna, Ahmed; Motawea, Ibrahim

2016-11-18

To investigate the diagnostic ability of a non-invasive biological marker to predict liver fibrosis in hepatitis C genotype 4 patients with high accuracy. A cohort of 332 patients infected with hepatitis C genotype 4 was included in this cross-sectional study. Fasting plasma glucose, insulin, C-peptide, and angiotensin-converting enzyme serum levels were measured. Insulin resistance was mathematically calculated using the homeostasis model of insulin resistance (HOMA-IR). Fibrosis stages were distributed based on Metavir score as follows: F0 = 43, F1 = 136, F2 = 64, F3 = 45 and F4 = 44. Statistical analysis relied upon reclassification of fibrosis stages into mild fibrosis (F0-F) = 179, moderate fibrosis (F2) = 64, and advanced fibrosis (F3-F4) = 89. Univariate analysis indicated that age, log aspartate amino transaminase, log HOMA-IR and log platelet count were independent predictors of liver fibrosis stage ( P < 0.0001). A stepwise multivariate discriminant functional analysis was used to drive a discriminative model for liver fibrosis. Our index used cut-off values of ≥ 0.86 and ≤ -0.31 to diagnose advanced and mild fibrosis, respectively, with receiving operating characteristics of 0.91 and 0.88, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio were: 73%, 91%, 75%, 90% and 8.0 respectively for advanced fibrosis, and 67%, 88%, 84%, 70% and 4.9, respectively, for mild fibrosis. Our predictive model is easily available and reproducible, and predicted liver fibrosis with acceptable accuracy.
An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

Science.gov (United States)

Pearce, J; Ferrier, S; Scotts, D

2001-06-01

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.
Research on Fault Prediction of Distribution Network Based on Large Data

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Jinglong Zhou

2017-01-01

Full Text Available With the continuous development of information technology and the improvement of distribution automation level. Especially, the amount of on-line monitoring and statistical data is increasing, and large data is used data distribution system, describes the technology to collect, data analysis and data processing of the data distribution system. The artificial neural network mining algorithm and the large data are researched in the fault diagnosis and prediction of the distribution network.
Predicting the Potential Distribution of Polygala tenuifolia Willd. under Climate Change in China.

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Hongjun Jiang

Full Text Available Global warming has created opportunities and challenges for the survival and development of species. Determining how climate change may impact multiple ecosystem levels and lead to various species adaptations is necessary for both biodiversity conservation and sustainable biological resource utilization. In this study, we employed Maxent to predict changes in the habitat range and altitude of Polygala tenuifolia Willd. under current and future climate scenarios in China. Four representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5 were modeled for two time periods (2050 and 2070. The model inputs included 732 presence points and nine sets of environmental variables under the current conditions and the four RCPs in 2050 and 2070. The area under the receiver-operating characteristic (ROC curve (AUC was used to evaluate model performance. All of the AUCs were greater than 0.80, thereby placing these models in the "very good" category. Using a jackknife analysis, the precipitation in the warmest quarter, annual mean temperature, and altitude were found to be the top three variables that affect the range of P. tenuifolia. Additionally, we found that the predicted highly suitable habitat was in reasonable agreement with its actual distribution. Furthermore, the highly suitable habitat area was slowly reduced over time.
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)
Novel Radiobiological Gamma Index for Evaluation of 3-Dimensional Predicted Dose Distribution

Energy Technology Data Exchange (ETDEWEB)

Sumida, Iori, E-mail: sumida@radonc.med.osaka-u.ac.jp [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Yamaguchi, Hajime; Kizaki, Hisao; Aboshi, Keiko; Tsujii, Mari; Yoshikawa, Nobuhiko; Yamada, Yuji [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan); Suzuki, Osamu; Seo, Yuji [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Isohashi, Fumiaki [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan); Yoshioka, Yasuo [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Ogawa, Kazuhiko [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan)

2015-07-15

Purpose: To propose a gamma index-based dose evaluation index that integrates the radiobiological parameters of tumor control (TCP) and normal tissue complication probabilities (NTCP). Methods and Materials: Fifteen prostate and head and neck (H&N) cancer patients received intensity modulated radiation therapy. Before treatment, patient-specific quality assurance was conducted via beam-by-beam analysis, and beam-specific dose error distributions were generated. The predicted 3-dimensional (3D) dose distribution was calculated by back-projection of relative dose error distribution per beam. A 3D gamma analysis of different organs (prostate: clinical [CTV] and planned target volumes [PTV], rectum, bladder, femoral heads; H&N: gross tumor volume [GTV], CTV, spinal cord, brain stem, both parotids) was performed using predicted and planned dose distributions under 2%/2 mm tolerance and physical gamma passing rate was calculated. TCP and NTCP values were calculated for voxels with physical gamma indices (PGI) >1. We propose a new radiobiological gamma index (RGI) to quantify the radiobiological effects of TCP and NTCP and calculate radiobiological gamma passing rates. Results: The mean RGI gamma passing rates for prostate cases were significantly different compared with those of PGI (P<.03–.001). The mean RGI gamma passing rates for H&N cases (except for GTV) were significantly different compared with those of PGI (P<.001). Differences in gamma passing rates between PGI and RGI were due to dose differences between the planned and predicted dose distributions. Radiobiological gamma distribution was visualized to identify areas where the dose was radiobiologically important. Conclusions: RGI was proposed to integrate radiobiological effects into PGI. This index would assist physicians and medical physicists not only in physical evaluations of treatment delivery accuracy, but also in clinical evaluations of predicted dose distribution.
Zebrafish whole-adult-organism chemogenomics for large-scale predictive and discovery chemical biology.

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Siew Hong Lam

2008-07-01

Full Text Available The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly, is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated aromatic hydrocarbons [P(HAHs] and estrogenic compounds (ECs, we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR and estrogen receptor (ER agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology.
Nonparametric Bayesian predictive distributions for future order statistics

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Richard A. Johnson; James W. Evans; David W. Green

1999-01-01

We derive the predictive distribution for a specified order statistic, determined from a future random sample, under a Dirichlet process prior. Two variants of the approach are treated and some limiting cases studied. A practical application to monitoring the strength of lumber is discussed including choices of prior expectation and comparisons made to a Bayesian...

[Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model].

Science.gov (United States)

Zhang, Lin; Hou, Xuexia; Liu, Huixin; Liu, Wei; Wan, Kanglin; Hao, Qin

2016-01-01

To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt). The sero-diagnosis data of Lyme disease in 6 counties (Huzhu, Zeku, Tongde, Datong, Qilian and Xunhua) and the environmental and anthropogenic data including altitude, human footprint, normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected. By using the data of Huzhu Zeku and Tongde, the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt. The prediction results were compared with the human sero-prevalence of Lyme disease in Datong, Qilian and Xunhua counties in Qinghai. Three hot spots of Lyme disease were predicted in Qinghai, which were all in the east forest areas. Furthermore, the NDVI showed the most important role in the model prediction, followed by human footprint. Datong, Qilian and Xunhua counties were all in eastern Qinghai. Xunhua was in hot spot areaⅡ, Datong was close to the north of hot spot area Ⅲ, while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas. The data were well modeled in MaxEnt (Area Under Curve=0.980). The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction. MaxEnt could be used in predicting the potential distribution patterns of Lyme disease. The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.
Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea

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Penny Masuoka

2010-11-01

Full Text Available Over 35,000 cases of Japanese encephalitis (JE are reported worldwide each year. Culex tritaeniorhynchus is the primary vector of the JE virus, while wading birds are natural reservoirs and swine amplifying hosts. As part of a JE risk analysis, the ecological niche modeling programme, Maxent, was used to develop a predictive model for the distribution of Cx. tritaeniorhynchus in the Republic of Korea, using mosquito collection data, temperature, precipitation, elevation, land cover and the normalized difference vegetation index (NDVI. The resulting probability maps from the model were consistent with the known environmental limitations of the mosquito with low probabilities predicted for forest covered mountains. July minimum temperature and land cover were the most important variables in the model. Elevation, summer NDVI (July-September, precipitation in July, summer minimum temperature (May-August and maximum temperature for fall and winter months also contributed to the model. Comparison of the Cx. tritaeniorhynchus model to the distribution of JE cases in the Republic of Korea from 2001 to 2009 showed that cases among a highly vaccinated Korean population were located in high-probability areas for Cx. tritaeniorhynchus. No recent JE cases were reported from the eastern coastline, where higher probabilities of mosquitoes were predicted, but where only small numbers of pigs are raised. The geographical distribution of reported JE cases corresponded closely with the predicted high-probability areas for Cx. tritaeniorhynchus, making the map a useful tool for health risk analysis that could be used for planning preventive public health measures.
Multi-scale approach for predicting fish species distributions across coral reef seascapes.

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Simon J Pittman

Full Text Available Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT and Maximum Entropy Species Distribution Modelling (MaxEnt. The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9 for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9. In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy than BRT (68% map accuracy. We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support
Predictions for an invaded world: A strategy to predict the distribution of native and non-indigenous species at multiple scales

Science.gov (United States)

Reusser, D.A.; Lee, H.

2008-01-01

Habitat models can be used to predict the distributions of marine and estuarine non-indigenous species (NIS) over several spatial scales. At an estuary scale, our goal is to predict the estuaries most likely to be invaded, but at a habitat scale, the goal is to predict the specific locations within an estuary that are most vulnerable to invasion. As an initial step in evaluating several habitat models, model performance for a suite of benthic species with reasonably well-known distributions on the Pacific coast of the US needs to be compared. We discuss the utility of non-parametric multiplicative regression (NPMR) for predicting habitat- and estuary-scale distributions of native and NIS. NPMR incorporates interactions among variables, allows qualitative and categorical variables, and utilizes data on absence as well as presence. Preliminary results indicate that NPMR generally performs well at both spatial scales and that distributions of NIS are predicted as well as those of native species. For most species, latitude was the single best predictor, although similar model performance could be obtained at both spatial scales with combinations of other habitat variables. Errors of commission were more frequent at a habitat scale, with omission and commission errors approximately equal at an estuary scale. ?? 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved.
Fair value versus historical cost-based valuation for biological assets: predictability of financial information

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Josep M. Argilés

2011-08-01

This paper performs an empirical study with a sample of Spanish farms valuing biological assets at HC and a sample applying FV, finding no significant differences between both valuation methods to assess future cash flows. However, most tests reveal more predictive power of future earnings under fair valuation of biological assets, which is not explained by differences in volatility of earnings and profitability. The study also evidences the existence of flawed HC accounting practices for biological assets in agriculture, which suggests scarce information content of this valuation method in the predominant small business units existing in the agricultural sector in advanced Western countries.
Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

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Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.
Biological stability in drinking water distribution systems : A novel approach for systematic microbial water quality monitoring

NARCIS (Netherlands)

Prest, E.I.E.D.

2015-01-01

Challenges to achieve biological stability in drinking water distribution systems Drinking water is distributed from the treatment facility to consumers through extended man-made piping systems. The World Health Organization drinking water guidelines (2006) stated that “Water entering the
A systems biology approach to transcription factor binding site prediction.

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Xiang Zhou

2010-03-01

Full Text Available The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs, identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy
Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

Science.gov (United States)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.
Performance prediction model for distributed applications on multicore clusters

CSIR Research Space (South Africa)

Khanyile, NP

2012-07-01

Full Text Available discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency...
Predicting the distribution of bed material accumulation using river network sediment budgets

Science.gov (United States)

Wilkinson, Scott N.; Prosser, Ian P.; Hughes, Andrew O.

2006-10-01

Assessing the spatial distribution of bed material accumulation in river networks is important for determining the impacts of erosion on downstream channel form and habitat and for planning erosion and sediment management. A model that constructs spatially distributed budgets of bed material sediment is developed to predict the locations of accumulation following land use change. For each link in the river network, GIS algorithms are used to predict bed material supply from gullies, river banks, and upstream tributaries and to compare total supply with transport capacity. The model is tested in the 29,000 km2 Murrumbidgee River catchment in southeast Australia. It correctly predicts the presence or absence of accumulation in 71% of river links, which is significantly better performance than previous models, which do not account for spatial variability in sediment supply and transport capacity. Representing transient sediment storage is important for predicting smaller accumulations. Bed material accumulation is predicted in 25% of the river network, indicating its importance as an environmental problem in Australia.
A new simplified allometric approach for predicting the biological half-life of radionuclides in reptiles

International Nuclear Information System (INIS)

Beresford, N.A.; Wood, M.D.

2014-01-01

A major source of uncertainty in the estimation of radiation dose to wildlife is the prediction of internal radionuclide activity concentrations. Allometric (mass-dependent) relationships describing biological half-life (T 1/2b ) of radionuclides in organisms can be used to predict organism activity concentrations. The establishment of allometric expressions requires experimental data which are often lacking. An approach to predict the T 1/2b in homeothermic vertebrates has recently been proposed. In this paper we have adapted this to be applicable to reptiles. For Cs, Ra and Sr, over a mass range of 0.02–1.5 kg, resultant predictions were generally within a factor of 6 of reported values demonstrating that the approach can be used when measured T 1/2b data are lacking. However, the effect of mass on reptilian radionuclide T 1/2b is minimal. If sufficient measured data are available for a given radionuclide then it is likely that these would give a reasonable estimate of T 1/2b in any reptile species. - Highlights: • An allometric approach to predict radionuclide T 1/2b values in reptiles is derived. • Predictions are generally within a factor of six of measured values. • Radionuclide biological half-life is in-effect mass independent
Prediction future asset price which is non-concordant with the historical distribution

Science.gov (United States)

Seong, Ng Yew; Hin, Pooi Ah

2015-12-01

This paper attempts to predict the major characteristics of the future asset price which is non-concordant with the distribution estimated from the price today and the prices on a large number of previous days. The three major characteristics of the i-th non-concordant asset price are the length of the interval between the occurrence time of the previous non-concordant asset price and that of the present non-concordant asset price, the indicator which denotes that the non-concordant price is extremely small or large by its values -1 and 1 respectively, and the degree of non-concordance given by the negative logarithm of the probability of the left tail or right tail of which one of the end points is given by the observed future price. The vector of three major characteristics of the next non-concordant price is modelled to be dependent on the vectors corresponding to the present and l - 1 previous non-concordant prices via a 3-dimensional conditional distribution which is derived from a 3(l + 1)-dimensional power-normal mixture distribution. The marginal distribution for each of the three major characteristics can then be derived from the conditional distribution. The mean of the j-th marginal distribution is an estimate of the value of the j-th characteristics of the next non-concordant price. Meanwhile, the 100(α/2) % and 100(1 - α/2) % points of the j-th marginal distribution can be used to form a prediction interval for the j-th characteristic of the next non-concordant price. The performance measures of the above estimates and prediction intervals indicate that the fitted conditional distribution is satisfactory. Thus the incorporation of the distribution of the characteristics of the next non-concordant price in the model for asset price has a good potential of yielding a more realistic model.
A maximum entropy model for predicting wild boar distribution in Spain

Directory of Open Access Journals (Sweden)

Jaime Bosch

2014-09-01

Full Text Available Wild boar (Sus scrofa populations in many areas of the Palearctic including the Iberian Peninsula have grown continuously over the last century. This increase has led to numerous different types of conflicts due to the damage these mammals can cause to agriculture, the problems they create in the conservation of natural areas, and the threat they pose to animal health. In the context of both wildlife management and the design of health programs for disease control, it is essential to know how wild boar are distributed on a large spatial scale. Given that the quantifying of the distribution of wild species using census techniques is virtually impossible in the case of large-scale studies, modeling techniques have thus to be used instead to estimate animals’ distributions, densities, and abundances. In this study, the potential distribution of wild boar in Spain was predicted by integrating data of presence and environmental variables into a MaxEnt approach. We built and tested models using 100 bootstrapped replicates. For each replicate or simulation, presence data was divided into two subsets that were used for model fitting (60% of the data and cross-validation (40% of the data. The final model was found to be accurate with an area under the receiver operating characteristic curve (AUC value of 0.79. Six explanatory variables for predicting wild boar distribution were identified on the basis of the percentage of their contribution to the model. The model exhibited a high degree of predictive accuracy, which has been confirmed by its agreement with satellite images and field surveys.
Predictive Models of Nanotoxicity: Relationship of Physicochemical Properties to Particle Movement Through Biological Barriers

Science.gov (United States)

Understanding the linkage between the physicochemical (PC) properties of nanoparticles (NP) and their activation of biological systems is poorly understood, yet fundamental to predicting nanotoxicity, idenitifying mode of actions and developing appropriate and effective regul...
Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.

Science.gov (United States)

He, Jichao; Wanik, David W; Hartman, Brian M; Anagnostou, Emmanouil N; Astitha, Marina; Frediani, Maria E B

2017-03-01

This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources. © 2016 Society for Risk Analysis.
A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

Science.gov (United States)

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.
Single-mode biological distributed feedback lasers based on vitamin B2 doped gelatin

DEFF Research Database (Denmark)

Vannahme, Christoph; Maier-Flaig, F.; Lemmer, U.

Biological second-order distributed feedback (DFB) lasers are presented. Riboflavin (vitamin B2) doped gelatin as active material is spin-coated onto nanoimprinted polymer with low refractive index. DFB grating periods of 368 nm and 384 nm yield laser emission at 543 nm and 562 nm, respectively....
Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

Directory of Open Access Journals (Sweden)

Elizabeth A. Becker

2016-02-01

Full Text Available Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”, but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE, observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.
Validation of model predictions of pore-scale fluid distributions during two-phase flow

Science.gov (United States)

Bultreys, Tom; Lin, Qingyang; Gao, Ying; Raeini, Ali Q.; AlRatrout, Ahmed; Bijeljic, Branko; Blunt, Martin J.

2018-05-01

Pore-scale two-phase flow modeling is an important technology to study a rock's relative permeability behavior. To investigate if these models are predictive, the calculated pore-scale fluid distributions which determine the relative permeability need to be validated. In this work, we introduce a methodology to quantitatively compare models to experimental fluid distributions in flow experiments visualized with microcomputed tomography. First, we analyzed five repeated drainage-imbibition experiments on a single sample. In these experiments, the exact fluid distributions were not fully repeatable on a pore-by-pore basis, while the global properties of the fluid distribution were. Then two fractional flow experiments were used to validate a quasistatic pore network model. The model correctly predicted the fluid present in more than 75% of pores and throats in drainage and imbibition. To quantify what this means for the relevant global properties of the fluid distribution, we compare the main flow paths and the connectivity across the different pore sizes in the modeled and experimental fluid distributions. These essential topology characteristics matched well for drainage simulations, but not for imbibition. This suggests that the pore-filling rules in the network model we used need to be improved to make reliable predictions of imbibition. The presented analysis illustrates the potential of our methodology to systematically and robustly test two-phase flow models to aid in model development and calibration.

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

Science.gov (United States)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.
Soil architecture and distribution of organic matter

NARCIS (Netherlands)

Kooistra, M.J.; Noordwijk, van M.

1996-01-01

The biological component of soil structure varies greatly in quality and quantity, occurs on different scales, and varies throughout the year. It is far less predictable than the physical part and human impact. The occurrence and distribution of organic matter depends on several processes, related
Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property

DEFF Research Database (Denmark)

Chan, Siu Hung Joshua; Solem, Christian; Jensen, Peter Ruhdal

2014-01-01

biologically feasible EFMs by considering their graphical properties. A previous study on the transcriptional regulation of metabolic genes found that distinct branches at a branch point metabolite usually belong to distinct metabolic pathways. This suggests an intuitive property of biologically feasible EFMs......, i.e. minimal branching. RESULTS: We developed the concept of minimal branching EFM and derived the minimal branching decomposition (MBD) to decompose flux distributions. Testing in the core Escherichia coli metabolic network indicated that MBD can distinguish branches at branch points and greatly...... knowledge, which facilitates interpretation. Comparison of the methods applied to a complex flux distribution in Lactococcus lactis similarly showed the advantages of MBD. The minimal branching EFM concept underlying MBD should be useful in other applications....
GGPPS1 predicts the biological character of hepatocellular carcinoma in patients with cirrhosis

International Nuclear Information System (INIS)

Yu, De-cai; Liu, Jia; Chen, Jun; Shao, Jiao-jiao; Shen, Xiao; Xia, Hong-guang; Li, Chao-jun; Xue, Bin; Ding, Yi-tao

2014-01-01

Hepatocellular carcinoma (HCC) has been associated with diabetes and obesity, but a possible connection with the metabolic syndrome (MetS) and its potential interaction with hepatitis and cirrhosis are open to discussion. Our previous investigations have shown that GGPPS1 plays a critical role during hyperinsulinism. In this report, the expression and distribution of GGPPS1 in liver cancer, and its clinical significance were investigated. 70 patients with hepatocellular carcinoma (HCC) were included in this study. Three different types of tissues from each HCC patient were assembled immediately after surgical resection: tumor-free tissue >5 cm far from tumor edge (TF), adjacent nonmalignant tissue within 2 cm (AT), and tissue from the tumor (TT). Normal liver tissues from 10 liver transplant donors served as healthy control (HC) while 10 patients with liver cirrhosis as cirrhosis control (CC). The expression and distribution of GGPPS1 were detected by immunohistochemistry, western blots, or real-time PCR. The relationship between the expression of GGPPS1 and clinic pathologic index were analyzed. We found that GGPPS1 was intensified mainly in the cytoplasm of liver tumor cells. Both the expression of GGPPS1 mRNA and protein were upregulated in TT comparing to AT or TF. Meanwhile, HCC patients with cirrhosis had relative higher expression of GGPPS1. In addition, many pathologic characters show close correlation with GGPPS1, such as tumor stage, vessel invasion, and early recurrence. GGPPS1 may play a critical role during the development of HCC from cirrhosis and is of clinical significance for predicting biological character of HCC
Prediction of sound transmission loss through multilayered panels by using Gaussian distribution of directional incident energy

Science.gov (United States)

Kang; Ih; Kim; Kim

2000-03-01

In this study, a new prediction method is suggested for sound transmission loss (STL) of multilayered panels of infinite extent. Conventional methods such as random or field incidence approach often given significant discrepancies in predicting STL of multilayered panels when compared with the experiments. In this paper, appropriate directional distributions of incident energy to predict the STL of multilayered panels are proposed. In order to find a weighting function to represent the directional distribution of incident energy on the wall in a reverberation chamber, numerical simulations by using a ray-tracing technique are carried out. Simulation results reveal that the directional distribution can be approximately expressed by the Gaussian distribution function in terms of the angle of incidence. The Gaussian function is applied to predict the STL of various multilayered panel configurations as well as single panels. The compared results between the measurement and the prediction show good agreements, which validate the proposed Gaussian function approach.
Predictive modeling of deep-sea fish distribution in the Azores

Science.gov (United States)

Parra, Hugo E.; Pham, Christopher K.; Menezes, Gui M.; Rosa, Alexandra; Tempera, Fernando; Morato, Telmo

2017-11-01

Understanding the link between fish and their habitat is essential for an ecosystem approach to fisheries management. However, determining such relationship is challenging, especially for deep-sea species. In this study, we applied generalized additive models (GAMs) to relate presence-absence and relative abundance data of eight economically-important fish species to environmental variables (depth, slope, aspect, substrate type, bottom temperature, salinity and oxygen saturation). We combined 13 years of catch data collected from systematic longline surveys performed across the region. Overall, presence-absence GAMs performed better than abundance models and predictions made for the observed data successfully predicted the occurrence of the eight deep-sea fish species. Depth was the most influential predictor of all fish species occurrence and abundance distributions, whereas other factors were found to be significant for some species but did not show such a clear influence. Our results predicted that despite the extensive Azores EEZ, the habitats available for the studied deep-sea fish species are highly limited and patchy, restricted to seamounts slopes and summits, offshore banks and island slopes. Despite some identified limitations, our GAMs provide an improved knowledge of the spatial distribution of these commercially important fish species in the region.
Study of burden distribution characteristics (IV): the development of a distribution predicting model in which coke collapse has been taken into account

Energy Technology Data Exchange (ETDEWEB)

Kamisaka, E; Okuno, Y; Irita, T; Matsuzaki, M; Isoyama, T; Kunitomo, K

1984-01-01

Using results quoted in a previous report (see Tetsu To Hagane, Vol. 68, page S 701, 1982), coke collapse has been quantified by means of landslide theory, according to which the stability of the burden is given by a safety factor which equals resistance moment/sliding moment. This has enabled coke collapse to be introduced in a model for predicting burden distribution. Application of this model has resulted in more accurate predictions of burden distribution, the computed values being in close agreement with the results of distribution experiments. 1 reference.
A Popularity Based Prediction and Data Redistribution Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Maettig, P

2014-01-01

This paper presents a system to predict future data popularity for data-intensive systems, such as ATLAS distributed data management (DDM). Using these predictions it is possible to make a better distribution of data, helping to reduce the waiting time for jobs using with this data. This system is based on a tracer infrastructure that is able to monitor and store historical data accesses and which is used to create popularity reports. These reports provide detailed summaries about data accesses in the past, including information about the accessed files, the involved users and the sites. From this past data it is possible to then make near-term forecasts for data popularity in the future. The prediction system introduced in this paper makes use of both simple prediction methods as well as predictions made by neural networks. The best prediction method is dependent on the type of data and the data is carefully filtered for use in either system. The second part of the paper introduces a system that effectively ...
Plant ecdysteroids: plant sterols with intriguing distributions, biological effects and relations to plant hormones.

Science.gov (United States)

Tarkowská, Danuše; Strnad, Miroslav

2016-09-01

The present review summarises current knowledge of phytoecdysteroids' biosynthesis, distribution within plants, biological importance and relations to plant hormones. Plant ecdysteroids (phytoecdysteroids) are natural polyhydroxylated compounds that have a four-ringed skeleton, usually composed of either 27 carbon atoms or 28-29 carbon atoms (biosynthetically derived from cholesterol or other plant sterols, respectively). Their physiological roles in plants have not yet been confirmed and their occurrence is not universal. Nevertheless, they are present at high concentrations in various plant species, including commonly consumed vegetables, and have a broad spectrum of pharmacological and medicinal properties in mammals, including hepatoprotective and hypoglycaemic effects, and anabolic effects on skeletal muscle, without androgenic side-effects. Furthermore, phytoecdysteroids can enhance stress resistance by promoting vitality and enhancing physical performance; thus, they are considered adaptogens. This review summarises current knowledge of phytoecdysteroids' biosynthesis, distribution within plants, biological importance and relations to plant hormones.
Improved ability of biological and previous caries multimarkers to predict caries disease as revealed by multivariate PLS modelling

Directory of Open Access Journals (Sweden)

Ericson Thorild

2009-11-01

Full Text Available Abstract Background Dental caries is a chronic disease with plaque bacteria, diet and saliva modifying disease activity. Here we have used the PLS method to evaluate a multiplicity of such biological variables (n = 88 for ability to predict caries in a cross-sectional (baseline caries and prospective (2-year caries development setting. Methods Multivariate PLS modelling was used to associate the many biological variables with caries recorded in thirty 14-year-old children by measuring the numbers of incipient and manifest caries lesions at all surfaces. Results A wide but shallow gliding scale of one fifth caries promoting or protecting, and four fifths non-influential, variables occurred. The influential markers behaved in the order of plaque bacteria > diet > saliva, with previously known plaque bacteria/diet markers and a set of new protective diet markers. A differential variable patterning appeared for new versus progressing lesions. The influential biological multimarkers (n = 18 predicted baseline caries better (ROC area 0.96 than five markers (0.92 and a single lactobacilli marker (0.7 with sensitivity/specificity of 1.87, 1.78 and 1.13 at 1/3 of the subjects diagnosed sick, respectively. Moreover, biological multimarkers (n = 18 explained 2-year caries increment slightly better than reported before but predicted it poorly (ROC area 0.76. By contrast, multimarkers based on previous caries predicted alone (ROC area 0.88, or together with biological multimarkers (0.94, increment well with a sensitivity/specificity of 1.74 at 1/3 of the subjects diagnosed sick. Conclusion Multimarkers behave better than single-to-five markers but future multimarker strategies will require systematic searches for improved saliva and plaque bacteria markers.
Predicted and measured velocity distribution in a model heat exchanger

International Nuclear Information System (INIS)

Rhodes, D.B.; Carlucci, L.N.

1984-01-01

This paper presents a comparison between numerical predictions, using the porous media concept, and measurements of the two-dimensional isothermal shell-side velocity distributions in a model heat exchanger. Computations and measurements were done with and without tubes present in the model. The effect of tube-to-baffle leakage was also investigated. The comparison was made to validate certain porous media concepts used in a computer code being developed to predict the detailed shell-side flow in a wide range of shell-and-tube heat exchanger geometries
A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

International Nuclear Information System (INIS)

Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

2011-01-01

A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.
Prediction of vertical distribution and ambient development temperature of Baltic cod, Gadus morhua L., eggs

DEFF Research Database (Denmark)

Wieland, Kai; Jarre, Astrid

1997-01-01

An artificial neural network (ANN) model was established to predict the vertical distribution of Baltic cod eggs. Data from vertical distribution sampling in the Bornholm Basin over the period 1986-1995 were used to train and test the network, while data sets from sampling in 1996 were used...... for validation. The model explained 82% of the variance between observed and predicted relative frequencies of occurrence of the eggs in relation to salinity, temperature and oxygen concentration; The ANN fitted all observations satisfactorily except for one sampling date, where an exceptional hydrographic...... situation was observed. Mean ambient temperatures, calculated from the predicted vertical distributions of the eggs and used for the computation of egg developmental times, were overestimated by 0.05 degrees C on average. This corresponds to an error in prediction of egg developmental time of less than 1%...
Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

Science.gov (United States)

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10 mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and
Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change

Science.gov (United States)

Wen J. Wang; Hong S. He; Frank R. Thompson; Martin A. Spetich; Jacob S. Fraser

2018-01-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are notwell represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts.We investigate how species biological...
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
Predictive models of threatened plant species distribution in the Iberian arid south-east

OpenAIRE

Benito, Blas M.

2013-01-01

Poster on the distribution of three rare, endemic and endangered annual plants of arid zones in the south-eastern Iberian peninsula. Presented in the workshop "Predictive Modelling of Species Distribution: New Tools for the XXI Century (Baeza, Spain, november 2005).
Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change.

Directory of Open Access Journals (Sweden)

Giovanni Rapacciuolo

Full Text Available Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records
PASS-predicted design, synthesis and biological evaluation of cyclic nitrones as nootropics.

Science.gov (United States)

Marwaha, Alka; Goel, R K; Mahajan, Mohinder P

2007-09-15

Out of 400 virtually designed imidazoline N-oxides, five cyclic nitrones were selected on the basis of PASS prediction as potent nootropics and were evaluated for their biological activities in albino mice. The selected N-alkyl and aryl-substituted nitrones were found to be excellent nootropics. A series of lead compounds acting as cognition enhancers have been provided, which can be further exploited in search of such New Chemical Entities (NCEs).
Predicting Dynamical Crime Distribution From Environmental and Social Influences

Directory of Open Access Journals (Sweden)

Simon Garnier

2018-05-01

Full Text Available Understanding how social and environmental factors contribute to the spatio-temporal distribution of criminal activities is a fundamental question in modern criminology. Thanks to the development of statistical techniques such as Risk Terrain Modeling (RTM, it is possible to evaluate precisely the criminogenic contribution of environmental features to a given location. However, the role of social information in shaping the distribution of criminal acts is largely understudied by the criminological research literature. In this paper we investigate the existence of spatio-temporal correlations between successive robbery events, after controlling for environmental influences as estimated by RTM. We begin by showing that a robbery event increases the likelihood of future robberies at and in the neighborhood of its location. This event-dependent influence decreases exponentially with time and as an inverse function of the distance to the original event. We then combine event-dependence and environmental influences in a simulation model to predict robbery patterns at the scale of a large city (Newark, NJ. We show that this model significantly improves upon the predictions of RTM alone and of a model taking into account event-dependence only when tested against real data that were not used to calibrate either model. We conclude that combining risk from exposure (past event and vulnerability (environment, following from the Theory of Risky Places, when modeling crime distribution can improve crime suppression and prevention efforts by providing more accurate forecasting of the most likely locations of criminal events.

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Directory of Open Access Journals (Sweden)

Rodrigo Lopez Farias

2018-03-01

Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.
Distribution of tessera terrain on Venus: Prediction for Magellan

International Nuclear Information System (INIS)

Bindschadler, D.L.; Head, J.W.; Kreslavsky, M.A.; Shkuratov, Yu.G.; Ivanov, M.A.; Basilevsky, A.T.

1990-01-01

Tessera terrain is the dominant tectonic unit in the northern hemisphere of Venus and is characterized by complex sets of intersecting structural trends and distinctive radar properties due to a high degree of meter and sub-meter scale (5 cm to 10 m) roughness. Based on these distinctive radar properties, a prediction of the global distribution of tessera can be made using Pioneer Venus (PV) reflectivity and roughness data. Where available, Venera 15/16 and Arecibo images and PV diffuse scattering data were used to evaluate the prediction. From this assessment, the authors conclude that most of the regions with prediction values greater than 0.6 (out of 1) are likely to be tessera, and are almost certain to be tectonically deformed. Lada Terra and Phoebe Regio are very likely to contain tessera terrain, while much of Aphrodite Terra is most likely to be either tessera or a landform which has not yet been recognized on Venus. This prediction map will assist in targeting Magellan investigations of Venus tectonics
The effects of model and data complexity on predictions from species distributions models

DEFF Research Database (Denmark)

García-Callejas, David; Bastos, Miguel

2016-01-01

How complex does a model need to be to provide useful predictions is a matter of continuous debate across environmental sciences. In the species distributions modelling literature, studies have demonstrated that more complex models tend to provide better fits. However, studies have also shown...... that predictive performance does not always increase with complexity. Testing of species distributions models is challenging because independent data for testing are often lacking, but a more general problem is that model complexity has never been formally described in such studies. Here, we systematically...
On distributed model predictive control for vehicle platooning with a recursive feasibility guarantee

NARCIS (Netherlands)

Shi, Shengling; Lazar, Mircea

2017-01-01

This paper proposes a distributed model predictive control algorithm for vehicle platooning and more generally networked systems in a chain structure. The distributed models of the vehicle platoon are coupled through the input of the preceding vehicles. Using the principles of robust model
Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease.

Science.gov (United States)

Torres, Matthew P; Dewhurst, Henry; Sundararaman, Niveda

2016-11-01

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable
Prospective Tests on Biological Models of Acupuncture

Directory of Open Access Journals (Sweden)

Charles Shang

2009-01-01

Full Text Available The biological effects of acupuncture include the regulation of a variety of neurohumoral factors and growth control factors. In science, models or hypotheses with confirmed predictions are considered more convincing than models solely based on retrospective explanations. Literature review showed that two biological models of acupuncture have been prospectively tested with independently confirmed predictions: The neurophysiology model on the long-term effects of acupuncture emphasizes the trophic and anti-inflammatory effects of acupuncture. Its prediction on the peripheral effect of endorphin in acupuncture has been confirmed. The growth control model encompasses the neurophysiology model and suggests that a macroscopic growth control system originates from a network of organizers in embryogenesis. The activity of the growth control system is important in the formation, maintenance and regulation of all the physiological systems. Several phenomena of acupuncture such as the distribution of auricular acupuncture points, the long-term effects of acupuncture and the effect of multimodal non-specific stimulation at acupuncture points are consistent with the growth control model. The following predictions of the growth control model have been independently confirmed by research results in both acupuncture and conventional biomedical sciences: (i Acupuncture has extensive growth control effects. (ii Singular point and separatrix exist in morphogenesis. (iii Organizers have high electric conductance, high current density and high density of gap junctions. (iv A high density of gap junctions is distributed as separatrices or boundaries at body surface after early embryogenesis. (v Many acupuncture points are located at transition points or boundaries between different body domains or muscles, coinciding with the connective tissue planes. (vi Some morphogens and organizers continue to function after embryogenesis. Current acupuncture research suggests a
Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

DEFF Research Database (Denmark)

Helles, Glennie; Fonseca, Rasmus

2009-01-01

residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary...... seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been...... done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. Results: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle...
Two sample Bayesian prediction intervals for order statistics based on the inverse exponential-type distributions using right censored sample

Directory of Open Access Journals (Sweden)

M.M. Mohie El-Din

2011-10-01

Full Text Available In this paper, two sample Bayesian prediction intervals for order statistics (OS are obtained. This prediction is based on a certain class of the inverse exponential-type distributions using a right censored sample. A general class of prior density functions is used and the predictive cumulative function is obtained in the two samples case. The class of the inverse exponential-type distributions includes several important distributions such the inverse Weibull distribution, the inverse Burr distribution, the loglogistic distribution, the inverse Pareto distribution and the inverse paralogistic distribution. Special cases of the inverse Weibull model such as the inverse exponential model and the inverse Rayleigh model are considered.
What are the most crucial soil factors for predicting the distribution of alpine plant species?

Science.gov (United States)

Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

2017-12-01

Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil p
Predicting Posttraumatic Stress Symptom Prevalence and Local Distribution after an Earthquake with Scarce Data.

Science.gov (United States)

Dussaillant, Francisca; Apablaza, Mauricio

2017-08-01

After a major earthquake, the assignment of scarce mental health emergency personnel to different geographic areas is crucial to the effective management of the crisis. The scarce information that is available in the aftermath of a disaster may be valuable in helping predict where are the populations that are in most need. The objectives of this study were to derive algorithms to predict posttraumatic stress (PTS) symptom prevalence and local distribution after an earthquake and to test whether there are algorithms that require few input data and are still reasonably predictive. A rich database of PTS symptoms, informed after Chile's 2010 earthquake and tsunami, was used. Several model specifications for the mean and centiles of the distribution of PTS symptoms, together with posttraumatic stress disorder (PTSD) prevalence, were estimated via linear and quantile regressions. The models varied in the set of covariates included. Adjusted R2 for the most liberal specifications (in terms of numbers of covariates included) ranged from 0.62 to 0.74, depending on the outcome. When only including peak ground acceleration (PGA), poverty rate, and household damage in linear and quadratic form, predictive capacity was still good (adjusted R2 from 0.59 to 0.67 were obtained). Information about local poverty, household damage, and PGA can be used as an aid to predict PTS symptom prevalence and local distribution after an earthquake. This can be of help to improve the assignment of mental health personnel to the affected localities. Dussaillant F , Apablaza M . Predicting posttraumatic stress symptom prevalence and local distribution after an earthquake with scarce data. Prehosp Disaster Med. 2017;32(4):357-367.
Prediction of wind energy distribution in complex terrain using CFD

DEFF Research Database (Denmark)

Xu, Chang; Li, Chenqi; Yang, Jianchuan

2013-01-01

Based on linear models, WAsP software predicts wind energy distribution, with a good accuracy for flat terrain, but with a large error under complicated topography. In this paper, numerical simulations are carried out using the FLUENT software on a mesh generated by the GAMBIT and ARGIS software ...
Application of a computer model to predict optimum slaughter end points for different biological types of feeder cattle.

Science.gov (United States)

Williams, C B; Bennett, G L

1995-10-01

A bioeconomic model was developed to predict slaughter end points of different genotypes of feeder cattle, where profit/rotation and profit/day were maximized. Growth, feed intake, and carcass weight and composition were simulated for 17 biological types of steers. Distribution of carcass weight and proportion in four USDA quality and five USDA yield grades were obtained from predicted carcass weights and composition. Average carcass value for each genotype was calculated from these distributions under four carcass pricing systems that varied from value determined on quality grade alone to value determined on yield grade alone. Under profitable market conditions, rotation length was shorter and carcass weights lighter when the producer's goal was maximum profit/day, compared with maximum profit/rotation. A carcass value system based on yield grade alone resulted in greater profit/rotation and in lighter and leaner carcasses than a system based on quality grade alone. High correlations ( > .97) were obtained between breed profits obtained with different sets of input/output prices and carcass price discount weight ranges. This suggests that breed rankings on the basis of breed profits may not be sensitive to changes in input/output market prices. Steers that were on a grower-stocker system had leaner carcasses, heavier optimum carcass weight, greater profits, and less variation in optimum carcass weights between genotypes than steers that were started on a high-energy finishing diet at weaning. Overall results suggest that breed choices may change with different carcass grading and value systems and postweaning production systems. This model has potential to provide decision support in marketing fed cattle.
The electron density and temperature distributions predicted by bow shock models of Herbig-Haro objects

International Nuclear Information System (INIS)

Noriega-Crespo, A.; Bohm, K.H.; Raga, A.C.

1990-01-01

The observable spatial electron density and temperature distributions for series of simple bow shock models, which are of special interest in the study of Herbig-Haro (H-H) objects are computed. The spatial electron density and temperature distributions are derived from forbidden line ratios. It should be possible to use these results to recognize whether an observed electron density or temperature distribution can be attributed to a bow shock, as is the case in some Herbig-Haro objects. As an example, the empirical and predicted distributions for H-H 1 are compared. The predicted electron temperature distributions give the correct temperature range and they show very good diagnostic possibilities if the forbidden O III (4959 + 5007)/4363 wavelength ratio is used. 44 refs
Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models

Science.gov (United States)

Ramírez-Albores, Jorge E.; Bustamante, Ramiro O.

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of
Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

International Nuclear Information System (INIS)

Winkler, David A.

2016-01-01

Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based, have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods. - Highlights: • Nanomaterials regulators need good information to make good decisions. • Nanomaterials and their interactions with biology are very complex. • Computational methods use existing data to predict properties of new nanomaterials. • Statistical, data driven modelling methods have been successfully applied to this task. • Much more must be learnt before robust toolkits will be widely usable by regulators.
Predicting the geographical distribution of two invasive termite species from occurrence data.

Science.gov (United States)

Tonini, Francesco; Divino, Fabio; Lasinio, Giovanna Jona; Hochmair, Hartwig H; Scheffrahn, Rudolf H

2014-10-01

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.
Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

Science.gov (United States)

Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.

2018-04-01

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.
Predicting moisture content and density distribution of Scots pine by microwave scanning of sawn timber

International Nuclear Information System (INIS)

Johansson, J.; Hagman, O.; Fjellner, B.A.

2003-01-01

This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density distribution of Scots pine (Pinus sylvestris) using microwave sensors. The material was initially of green moisture content and was thereafter dried in several steps to zero moisture content. At each step, all the pieces were weighed, scanned with a microwave sensor (Satimo 9,4GHz), and computed tomography (CT)-scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave sensor were used as predictors, and CT images that correlated with known moisture content were used as response variables. Multivariate models to predict average moisture content and density were calibrated using the partial least squares (PLS) regression. The models for average moisture content and density were applied at the pixel level, and the distribution was visualized. The results show that it is possible to predict both moisture content distribution and density distribution with high accuracy using microwave sensors. (author)
Life prediction for white OLED based on LSM under lognormal distribution

Science.gov (United States)

Zhang, Jianping; Liu, Fang; Liu, Yu; Wu, Helen; Zhu, Wenqing; Wu, Wenli; Wu, Liang

2012-09-01

In order to acquire the reliability information of White Organic Light Emitting Display (OLED), three groups of OLED constant stress accelerated life tests (CSALTs) were carried out to obtain failure data of samples. Lognormal distribution function was applied to describe OLED life distribution, and the accelerated life equation was determined by Least square method (LSM). The Kolmogorov-Smirnov test was performed to verify whether the white OLED life meets lognormal distribution or not. Author-developed software was employed to predict the average life and the median life. The numerical results indicate that the white OLED life submits to lognormal distribution, and that the accelerated life equation meets inverse power law completely. The estimated life information of the white OLED provides manufacturers and customers with important guidelines.
The nature and use of prediction skills in a biological computer simulation

Science.gov (United States)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

Science.gov (United States)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method
Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.
Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease*

Science.gov (United States)

Dewhurst, Henry; Sundararaman, Niveda

2016-01-01

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable
Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

NARCIS (Netherlands)

Mayer, Gert; Heerspink, Hiddo J. L.; Aschauer, Constantin; Heinzel, Andreas; Heinze, Georg; Kainz, Alexander; Sunzenauer, Judith; Perco, Paul; de Zeeuw, Dick; Rossing, Peter; Pena, Michelle; Oberbauer, Rainer

OBJECTIVE: Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large
Climate change and plant distribution: local models predict high-elevation persistence

DEFF Research Database (Denmark)

Randin, Christophe F.; Engler, Robin; Normand, Signe

2009-01-01

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates...
Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios.

Directory of Open Access Journals (Sweden)

Xuezhen Ge

Full Text Available As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier (Coleoptera: Curculionidae has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981-2010 and future climate warming estimates based on simulated climate data for the 2020s (2011-2040 provided by the Tyndall Center for Climate Change Research (TYN SC 2.0. Additionally, the Ecoclimatic Index (EI values calculated for different climatic conditions (current and future, as simulated by the B2 scenario were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas.
Towards a Universal Biology: Is the Origin and Evolution of Life Predictable?

Science.gov (United States)

Rothschild, Lynn J.

2017-01-01

The origin and evolution of life seems an unpredictable oddity, based on the quirks of contingency. Celebrated by the late Stephen Jay Gould in several books, "evolution by contingency" has all the adventure of a thriller, but lacks the predictive power of the physical sciences. Not necessarily so, replied Simon Conway Morris, for convergence reassures us that certain evolutionary responses are replicable. The outcome of this debate is critical to Astrobiology. How can we understand where we came from on Earth without prophesy? Further, we cannot design a rational strategy for the search for life elsewhere - or to understand what the future will hold for life on Earth and beyond - without extrapolating from pre-biotic chemistry and evolution. There are several indirect approaches to understanding, and thus describing, what life must be. These include philosophical approaches to defining life (is there even a satisfactory definition of life?), using what we know of physics, chemistry and life to imagine alternate scenarios, using different approaches that life takes as pseudoreplicates (e.g., ribosomal vs non-ribosomal protein synthesis), and experimental approaches to understand the art of the possible. Given that: (1) Life is a process based on physical components rather than simply an object; (2). Life is likely based on organic carbon and needs a solvent for chemistry, most likely water, and (3) Looking for convergence in terrestrial evolution we can predict certain tendencies, if not quite "laws", that provide predictive power. Biological history must obey the laws of physics and chemistry, the principles of natural selection, the constraints of an evolutionary past, genetics, and developmental biology. This amalgam creates a surprising amount of predictive power in the broad outline. Critical is the apparent prevalence of organic chemistry, and uniformity in the universe of the laws of chemistry and physics. Instructive is the widespread occurrence of
Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

Directory of Open Access Journals (Sweden)

Ehsaneddin Asgari

Full Text Available We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec to refer to biological sequences in general with protein-vectors (ProtVec for proteins (amino-acid sequences and gene-vectors (GeneVec for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups. Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be
A consensus approach for estimating the predictive accuracy of dynamic models in biology.

Science.gov (United States)

Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Müller, Dirk; Balsa-Canto, Eva; Schmid, Joachim; Banga, Julio R

2015-04-01

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Quantification of Hg excretion and distribution in biological samples of mercury-dental-amalgam users and its correlation with biological variables.

Science.gov (United States)

Gul, Nayab; Khan, Sardar; Khan, Abbas; Nawab, Javed; Shamshad, Isha; Yu, Xinwei

2016-10-01

This is the first study conducted to quantify the excretion and distribution of mercury (Hg) with time (days) in the biological samples collected from Hg dental amalgam users (MDA). The individuals, with Hg-based dental filling were selected, and their biological samples (red blood cells (RBCs), plasma, urine, hair, and nails) were collected on first, third, and 12th day of fillings. The concentrations of Hg observed in the biological samples of MDA were also correlated with the biological variables such as age, weight, restoration, fish consumption, number, and surface area of fillings. The concentrations of Hg in the biological samples of MDA were found 6-8 times higher than the non-amalgam users (control). The concentrations of Hg in the RBCs (4.39 μg/L), plasma (3.02 μg/L), and urine (22.5 μg/L) on first day of filling were found comparatively higher than the concentrations observed on third day (2.15, 1.46, and 12.3 μg/L for RBCs, plasma, urine, respectively) and 12th day (3.05, 2.5, 9.12 μg/L for RBCs, plasma, urine, respectively), while Hg concentrations were found lower in the hair and nails on third day of fillings (1.53 μg/g for hair and 2.35 μg/g for nails) as compared to the 12th day (2.95 μg/g for hair and 3.5 μg/g for nails). The correlations were found significant (p ˂ 0.05) between Hg concentrations in the biological samples of MDA and biological variables (the number of restoration, fish consumption, number, and surface area of fillings), while no significant (p ˃ 0.05) correlations were observed for Hg concentrations in the biological samples with age and weight of MDA. These observations unveil the fact that the use of Hg-based dental filling is the undesirable exposure to Hg which should be replaced by composite (a safer filling material).
Spatial distribution of common Minke whale (Balaenoptera acutorostrata) as an indication of a biological hotspot in the East Sea

Science.gov (United States)

Lee, Dasom; An, Yong Rock; Park, Kyum Joon; Kim, Hyun Woo; Lee, Dabin; Joo, Hui Tae; Oh, Young Geun; Kim, Su Min; Kang, Chang Keun; Lee, Sang Heon

2017-09-01

The minke whale (Balaenoptera acutorostrata) is the most common baleen whale among several marine mammal species observed in Korea. Since a high concentrated condition of prey to whales can be obtained by physical structures, the foraging whale distribution can be an indicator of biological hotspot. Our main objective is verifying the coastal upwelling-southwestern East Sea as a productive biological hotspot based on the geographical distribution of minke whales. Among the cetacean research surveys of the National Institute of Fisheries Science since 1999, 9 years data for the minke whales available in the East Sea were used for this study. The regional primary productivity derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) was used for a proxy of biological productivity. Minke whales observed during the sighting surveys were mostly concentrated in May and found mostly (approximately 70%) in the southwestern coastal areas (whales was found in recent years, which indicate that the major habitats of mink whales have been shifted into the north of the common coastal upwelling regions. This is consistent with the recently reported unprecedented coastal upwelling in the mid-eastern coast of Korea. Based on high phytoplankton productivity and high distribution of minke whales, the southwestern coastal regions can be considered as one of biological hotspots in the East Sea. These regions are important for ecosystem dynamics and the population biology of top marine predators, especially migratory whales and needed to be carefully managed from a resource management perspective.
Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication.

Science.gov (United States)

Song, Kun; Cui, Yichong; Zhang, Xijin; Pan, Yingji; Xu, Junli; Xu, Kaiqin; Da, Liangjun

2017-10-01

Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian-based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co-occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes' distribution under eutrophication stress.
Ortholog prediction of the Aspergillus genus applicable for synthetic biology

DEFF Research Database (Denmark)

Rasmussen, Jane Lind Nybo; Vesth, Tammi Camilla; Theobald, Sebastian

of genotype-to-phenotype. To achieve this, we have developed orthologous protein prediction software that utilizes genus-wide genetic diversity. The approach is optimized for large data sets, based on BLASTp considering protein identity and alignment coverage, and clustering using single linkage of bi......The Aspergillus genus contains leading industrial microorganisms, excelling in producing bioactive compounds and enzymes. Using synthetic biology and bioinformatics, we aim to re-engineer these organisms for applications within human health, pharmaceuticals, environmental engineering, and food......-directional hits. The result is orthologous protein families describing the genomic and functional features of individual species, clades and the core/pan genome of Aspergillus; and applicable to genotype-to-phenotype analyses in other microbial genera....
Distribution and ecology of gammarus lacustris G. O. Sars in Norway, with notes on its morphology and biology

Energy Technology Data Exchange (ETDEWEB)

Oekland, K A

1969-01-01

The distribution of Gammarus lacustris in Norway is found to be affected mainly by hydrographical and geological factors. Of special interest is the species absence in the more acid lakes. In some areas, inefficiencies in dispersal and predation by fish also influence the distribution patterns. A tendency for the larger animals to occur in lakes rich in lime is demonstrated. Morphology, breeding biology, depth distribution, and parasites are also treated.
Measurement and prediction of aromatic solute distribution coefficients for aqueous-organic solvent systems. Final report

Energy Technology Data Exchange (ETDEWEB)

Campbell, J.R.; Luthy, R.G.

1984-06-01

Experimental and modeling activities were performed to assess techniques for measurement and prediction of distribution coefficients for aromatic solutes between water and immiscible organic solvents. Experiments were performed to measure distribution coefficients in both clean water and wastewater systems, and to assess treatment of a wastewater by solvent extraction. The theoretical portions of this investigation were directed towards development of techniques for prediction of solute-solvent/water distribution coefficients. Experiments were performed to assess treatment of a phenolic-laden coal conversion wastewater by solvent extraction. The results showed that solvent extraction for recovery of phenolic material offered several wastewater processing advantages. Distribution coefficients were measured in clean water and wastewater systems for aromatic solutes of varying functionality with different solvent types. It was found that distribution coefficients for these compounds in clean water systems were not statistically different from distribution coefficients determined in a complex coal conversion process wastewater. These and other aromatic solute distribution coefficient data were employed for evaluation of modeling techniques for prediction of solute-solvent/water distribution coefficients. Eight solvents were selected in order to represent various chemical classes: toluene and benzene (aromatics), hexane and heptane (alkanes), n-octanol (alcohols), n-butyl acetate (esters), diisopropyl ether (ethers), and methylisobutyl ketone (ketones). The aromatic solutes included: nonpolar compounds such as benzene, toluene and naphthalene, phenolic compounds such as phenol, cresol and catechol, nitrogenous aromatics such as aniline, pyridine and aminonaphthalene, and other aromatic solutes such as naphthol, quinolinol and halogenated compounds. 100 references, 20 figures, 34 tables.
WE-B-304-03: Biological Treatment Planning

International Nuclear Information System (INIS)

Orton, C.

2015-01-01

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations
Synthesis and biology of cyclic imine toxins, an emerging class of potent, globally distributed marine toxins.

Science.gov (United States)

Stivala, Craig E; Benoit, Evelyne; Aráoz, Rómulo; Servent, Denis; Novikov, Alexei; Molgó, Jordi; Zakarian, Armen

2015-03-01

From a small group of exotic compounds isolated only two decades ago, Cyclic Imine (CI) toxins have become a major class of marine toxins with global distribution. Their distinct chemical structure, biological mechanism of action, and intricate chemistry ensures that CI toxins will continue to be the subject of fascinating fundamental studies in the broad fields of chemistry, chemical biology, and toxicology. The worldwide occurrence of potent CI toxins in marine environments, their accumulation in shellfish, and chemical stability are important considerations in assessing risk factors for human health. This review article aims to provide an account of chemistry, biology, and toxicology of CI toxins from their discovery to the present day.
The influence of coarse-scale environmental features on current and predicted future distributions of narrow-range endemic crayfish populations

Science.gov (United States)

Dyer, Joseph J.; Brewer, Shannon K.; Worthington, Thomas A.; Bergey, Elizabeth A.

2013-01-01

1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario
An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study

International Nuclear Information System (INIS)

Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Saboohi, Hadi; Abdul Wahab, Ainuddin Wahid; Protić, Milan; Zalnezhad, Erfan; Mirhashemi, Seyed Mohammad Amin

2014-01-01

Highlights: • Probabilistic distribution functions of wind speed. • Two parameter Weibull probability distribution. • To build an effective prediction model of distribution of wind speed. • Support vector regression application as probability function for wind speed. - Abstract: The probabilistic distribution of wind speed is among the more significant wind characteristics in examining wind energy potential and the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can be easily obtained. Therefore, the probability distribution of wind speed is a very important piece of information required in assessing wind energy potential. For this reason, a large number of studies have been established concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Although the two-parameter Weibull distribution comprises a widely used and accepted method, solving the function is very challenging. In this study, the polynomial and radial basis functions (RBF) are applied as the kernel function of support vector regression (SVR) to estimate two parameters of the Weibull distribution function according to previously established analytical methods. Rather than minimizing the observed training error, SVR p oly and SVR r bf attempt to minimize the generalization error bound, so as to achieve generalized performance. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved using the SVR approach compared to other soft computing methodologies
The Density Functional Theory of Flies: Predicting distributions of interacting active organisms

Science.gov (United States)

Kinkhabwala, Yunus; Valderrama, Juan; Cohen, Itai; Arias, Tomas

On October 2nd, 2016, 52 people were crushed in a stampede when a crowd panicked at a religious gathering in Ethiopia. The ability to predict the state of a crowd and whether it is susceptible to such transitions could help prevent such catastrophes. While current techniques such as agent based models can predict transitions in emergent behaviors of crowds, the assumptions used to describe the agents are often ad hoc and the simulations are computationally expensive making their application to real-time crowd prediction challenging. Here, we pursue an orthogonal approach and ask whether a reduced set of variables, such as the local densities, are sufficient to describe the state of a crowd. Inspired by the theoretical framework of Density Functional Theory, we have developed a system that uses only measurements of local densities to extract two independent crowd behavior functions: (1) preferences for locations and (2) interactions between individuals. With these two functions, we have accurately predicted how a model system of walking Drosophila melanogaster distributes itself in an arbitrary 2D environment. In addition, this density-based approach measures properties of the crowd from only observations of the crowd itself without any knowledge of the detailed interactions and thus it can make predictions about the resulting distributions of these flies in arbitrary environments, in real-time. This research was supported in part by ARO W911NF-16-1-0433.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

Science.gov (United States)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is
Synthesis, biological distribution and radiation dosimetry of Te-123m analogues of hexadecenoic acid

International Nuclear Information System (INIS)

Basmadjian, G.P.; Ice, R.D.; Mills, S.L.

1982-01-01

The synthesis and biological distribution of four Te-123m analogues of hexadecenoic acid in rats, rabbits and dogs were described for use as possible myocardial imaging agents. The heart-to-blood ratios ranged from 0.13 for 3-telluranonadecenoic acid in rats at 5 mins to 6.25 for 18-methyl-17-tellura-9-nonadecenoic acid in dogs at 24 hrs. The biological half-life of the Te-123m labelled fatty acids ranged from 26 to 583 hrs in the hearts of the test animals. These Te-123m fatty acids were retained in the heart longer than radioiodinated fatty acids and have acceptable absorbed doses to the various target organs. (U.K.)
A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks

KAUST Repository

Lautenschlä ger, Karin; Hwang, Chiachi; Liu, Wentso; Boon, Nico; Kö ster, Oliver; Vrouwenvelder, Johannes S.; Egli, Thomas; Hammes, Frederik A.

2013-01-01

Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (±0.6)×104cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, sofar for unknown reasons, recorded a slight but significantly higher TCC (1.3(±0.1)×105cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used provides a powerful
A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks.

Science.gov (United States)

Lautenschlager, Karin; Hwang, Chiachi; Liu, Wen-Tso; Boon, Nico; Köster, Oliver; Vrouwenvelder, Hans; Egli, Thomas; Hammes, Frederik

2013-06-01

Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52 h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (± 0.6) × 10(4) cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, so far for unknown reasons, recorded a slight but significantly higher TCC (1.3 (± 0.1) × 10(5) cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used
A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks

KAUST Repository

Lautenschläger, Karin

2013-06-01

Biological stability of drinking water implies that the concentration of bacterial cells and composition of the microbial community should not change during distribution. In this study, we used a multi-parametric approach that encompasses different aspects of microbial water quality including microbial growth potential, microbial abundance, and microbial community composition, to monitor biological stability in drinking water of the non-chlorinated distribution system of Zürich. Drinking water was collected directly after treatment from the reservoir and in the network at several locations with varied average hydraulic retention times (6-52h) over a period of four months, with a single repetition two years later. Total cell concentrations (TCC) measured with flow cytometry remained remarkably stable at 9.5 (±0.6)×104cells/ml from water in the reservoir throughout most of the distribution network, and during the whole time period. Conventional microbial methods like heterotrophic plate counts, the concentration of adenosine tri-phosphate, total organic carbon and assimilable organic carbon remained also constant. Samples taken two years apart showed more than 80% similarity for the microbial communities analysed with denaturing gradient gel electrophoresis and 454 pyrosequencing. Only the two sampling locations with the longest water retention times were the exceptions and, sofar for unknown reasons, recorded a slight but significantly higher TCC (1.3(±0.1)×105cells/ml) compared to the other locations. This small change in microbial abundance detected by flow cytometry was also clearly observed in a shift in the microbial community profiles to a higher abundance of members from the Comamonadaceae (60% vs. 2% at other locations). Conventional microbial detection methods were not able to detect changes as observed with flow cytometric cell counts and microbial community analysis. Our findings demonstrate that the multi-parametric approach used provides a powerful
Effects of the infectious period distribution on predicted transitions in childhood disease dynamics.

Science.gov (United States)

Krylova, Olga; Earn, David J D

2013-07-06

The population dynamics of infectious diseases occasionally undergo rapid qualitative changes, such as transitions from annual to biennial cycles or to irregular dynamics. Previous work, based on the standard seasonally forced 'susceptible-exposed-infectious-removed' (SEIR) model has found that transitions in the dynamics of many childhood diseases result from bifurcations induced by slow changes in birth and vaccination rates. However, the standard SEIR formulation assumes that the stage durations (latent and infectious periods) are exponentially distributed, whereas real distributions are narrower and centred around the mean. Much recent work has indicated that realistically distributed stage durations strongly affect the dynamical structure of seasonally forced epidemic models. We investigate whether inferences drawn from previous analyses of transitions in patterns of measles dynamics are robust to the shapes of the stage duration distributions. As an illustrative example, we analyse measles dynamics in New York City from 1928 to 1972. We find that with a fixed mean infectious period in the susceptible-infectious-removed (SIR) model, the dynamical structure and predicted transitions vary substantially as a function of the shape of the infectious period distribution. By contrast, with fixed mean latent and infectious periods in the SEIR model, the shapes of the stage duration distributions have a less dramatic effect on model dynamical structure and predicted transitions. All these results can be understood more easily by considering the distribution of the disease generation time as opposed to the distributions of individual disease stages. Numerical bifurcation analysis reveals that for a given mean generation time the dynamics of the SIR and SEIR models for measles are nearly equivalent and are insensitive to the shapes of the disease stage distributions.
Understanding the dynamics in distribution of invasive alien plant species under predicted climate change in Western Himalaya.

Science.gov (United States)

Thapa, Sunil; Chitale, Vishwas; Rijal, Srijana Joshi; Bisht, Neha; Shrestha, Bharat Babu

2018-01-01

Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively.
Multivariate models for prediction of rheological characteristics of filamentous fermentation broth from the size distribution

DEFF Research Database (Denmark)

Petersen, Nanna; Stocks, S.; Gernaey, Krist

2008-01-01

fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield...... in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining theological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (mu(app)), yield stress (tau......(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as mu...
Depressive symptoms predict head and neck cancer survival: Examining plausible behavioral and biological pathways.

Science.gov (United States)

Zimmaro, Lauren A; Sephton, Sandra E; Siwik, Chelsea J; Phillips, Kala M; Rebholz, Whitney N; Kraemer, Helena C; Giese-Davis, Janine; Wilson, Liz; Bumpous, Jeffrey M; Cash, Elizabeth D

2018-03-01

Head and neck cancers are associated with high rates of depression, which may increase the risk for poorer immediate and long-term outcomes. Here it was hypothesized that greater depressive symptoms would predict earlier mortality, and behavioral (treatment interruption) and biological (treatment response) mediators were examined. Patients (n = 134) reported depressive symptomatology at treatment planning. Clinical data were reviewed at the 2-year follow-up. Greater depressive symptoms were associated with significantly shorter survival (hazard ratio, 0.868; 95% confidence interval [CI], 0.819-0.921; P ratio, 0.865; 95% CI, 0.774-0.966; P = .010), and poorer treatment response (odds ratio, 0.879; 95% CI, 0.803-0.963; P = .005). The poorer treatment response partially explained the depression-survival relation. Other known prognostic indicators did not challenge these results. Depressive symptoms at the time of treatment planning predict overall 2-year mortality. Effects are partly influenced by the treatment response. Depression screening and intervention may be beneficial. Future studies should examine parallel biological pathways linking depression to cancer survival, including endocrine disruption and inflammation. Cancer 2018;124:1053-60. © 2018 American Cancer Society. © 2018 American Cancer Society.
Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Directory of Open Access Journals (Sweden)

Dandan Zhao

2018-03-01

Full Text Available Wetlands in the mid- and high-latitudes are particularly vulnerable to environmental changes and have declined dramatically in recent decades. Climate change and human activities are arguably the most important factors driving wetland distribution changes which will have important implications for wetland ecological functions and services. We analyzed the importance of driving variables for wetland distribution and investigated the relative importance of climatic factors and human activity factors in driving historical wetland distribution changes. We predicted wetland distribution changes under climate change and human activities over the 21st century using the Random Forest model in a mid- and high-latitude region of Northeast China. Climate change scenarios included three Representative Concentration Pathways (RCPs based on five general circulation models (GCMs downloaded from the Coupled Model Intercomparison Project, Phase 5 (CMIP5. The three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5 predicted radiative forcing to peak at 2.6, 4.5, and 8.5 W/m2 by the 2100s, respectively. Our results showed that the variables with high importance scores were agricultural population proportion, warmness index, distance to water body, coldness index, and annual mean precipitation; climatic variables were given higher importance scores than human activity variables on average. Average predicted wetland area among three emission scenarios were 340,000 ha, 123,000 ha, and 113,000 ha for the 2040s, 2070s, and 2100s, respectively. Average change percent in predicted wetland area among three periods was greatest under the RCP 8.5 emission scenario followed by RCP 4.5 and RCP 2.6 emission scenarios, which were 78%, 64%, and 55%, respectively. Losses in predicted wetland distribution were generally around agricultural lands and expanded continually from the north to the whole region over time, while the gains were mostly associated with grasslands and water in the
Predicting cycle time distributions for integrated processing workstations : an aggregate modeling approach

NARCIS (Netherlands)

Veeger, C.P.L.; Etman, L.F.P.; Lefeber, A.A.J.; Adan, I.J.B.F.; Herk, van J.; Rooda, J.E.

2011-01-01

To predict cycle time distributions of integrated processing workstations, detailed simulation models are almost exclusively used; these models require considerable development and maintenance effort. As an alternative, we propose an aggregate model that is a lumped-parameter representation of the
Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika; Tegner, Jesper; Tsamardinos, Ioannis

2017-01-01

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia

2017-03-31

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika; Tegner, Jesper; Tsamardinos, Ioannis

2017-01-01

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia

2017-09-29

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
Precision predictions for Higgs differential distributions at the LHC

Energy Technology Data Exchange (ETDEWEB)

Ebert, Markus

2017-08-15

After the discovery of a Standard-Model-like Higgs boson at the LHC a central aspect of the LHC physics program is to study the Higgs boson's couplings to Standard Model particles in detail in order to elucidate the nature of the Higgs mechanism and to search for hints of physics beyond the Standard Model. This requires precise theory predictions for both inclusive and differential Higgs cross sections. In this thesis we focus on the application of resummation techniques in the framework of Soft-Collinear Effective Theory (SCET) to obtain accurate predictions with reliable theory uncertainties for various observables. We first consider transverse momentum distributions, where the resummation of large logarithms in momentum (or distribution) space has been a long-standing open question. We show that its two-dimensional nature leads to additional difficulties not observed in one-dimensional observables such as thrust, and solving the associated renormalization group equations (RGEs) in momentum space thus requires a very careful scale setting. This is achieved using distributional scale setting, a new technique to solve differential equations such as RGEs directly in distribution space, as it allows one to treat logarithmic plus distributions like ordinary logarithms. We show that the momentum space solution fundamentally differs from the standard resummation in Fourier space by different boundary terms to all orders in perturbation theory and hence provides an interesting and complementary approach to obtain new insight into the all-order perturbative and nonperturbative structure of transverse momentum distributions. Our work lays the ground for a detailed numerical study of the momentum space resummation. We then show that in the case of a discovery of a new heavy color-singlet resonance such as a heavy Higgs boson, one can reliably and model-independently infer its production mechanism by dividing the data into two mutually exclusive jet bins. The method is
Methods for Prediction of Temperature Distribution in Flashover Caused by Backdraft Fire

Directory of Open Access Journals (Sweden)

Guowei Zhang

2014-01-01

Full Text Available Accurately predicting temperature distribution in flashover fire is a key issue for evacuation and fire-fighting. Now many good flashover fire experiments have be conducted, but most of these experiments are proceeded in enclosure with fixed openings; researches on fire development and temperature distribution in flashover caused by backdraft fire did not receive enough attention. In order to study flashover phenomenon caused by backdraft fire, a full-scale fire experiment was conducted in one abandoned office building. Process of fire development and temperature distribution in room and corridor were separately recorded during the experiment. The experiment shows that fire development in enclosure is closely affected by the room ventilation. Unlike existing temperature curves which have only one temperature peak, temperature in flashover caused by backdraft may have more than one peak value and that there is a linear relationship between maximum peak temperature and distance away from fire compartment. Based on BFD curve and experimental data, mathematical models are proposed to predict temperature curve in flashover fire caused by backdraft at last. These conclusions and experiment data obtained in this paper could provide valuable reference to fire simulation, hazard assessment, and fire protection design.
Bayesian Belief Networks for predicting drinking water distribution system pipe breaks

International Nuclear Information System (INIS)

Francis, Royce A.; Guikema, Seth D.; Henneman, Lucas

2014-01-01

In this paper, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. To the authors’ knowledge, this is the first attempt to model drinking water distribution system pipe breaks using BBNs. Development of expert systems such as BBNs for analyzing drinking water distribution system data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate data-based distribution system monitoring and asset management. Due to the difficulties in collecting, preparing, and managing drinking water distribution system data, most pipe break models can be classified as “statistical–physical” or “hypothesis-generating.” We develop the BBN with the hope of contributing to the “hypothesis-generating” class of models, while demonstrating the possibility that BBNs might also be used as “statistical–physical” models. Our model is learned from pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. BBN models are learned using a constraint-based method, a score-based method, and a hybrid method. Model evaluation is based on log-likelihood scoring. Sensitivity analysis using mutual information criterion is also reported. While our results indicate general agreement with prior results reported in pipe break modeling studies, they also suggest that it may be difficult to select among model alternatives. This model uncertainty may mean that more research is needed for understanding whether additional pipe break risk factors beyond age, break history, pipe material, and pipe diameter might be important for asset management planning. - Highlights: • We show Bayesian Networks for predictive and diagnostic management of water distribution systems. • Our model may enable system operators and managers to prioritize system
Robust distributed model predictive control of linear systems with structured time-varying uncertainties

Science.gov (United States)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.
Prediction of vertical distribution and ambient development temperature of Baltic cod, Gadus morhua L., eggs

DEFF Research Database (Denmark)

Wieland, Kai; Jarre, Astrid

1997-01-01

An artificial neural network (ANN) model was established to predict the vertical distribution of Baltic cod eggs. Data from vertical distribution sampling in the Bornholm Basin over the period 1986-1995 were used to train and test the network, while data sets from sampling in 1996 were used...... for validation. The model explained 82% of the variance between observed and predicted relative frequencies of occurrence of the eggs in relation to salinity, temperature and oxygen concentration; The ANN fitted all observations satisfactorily except for one sampling date, where an exceptional hydrographic...

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya.

Science.gov (United States)

Aryal, Achyut; Shrestha, Uttam Babu; Ji, Weihong; Ale, Som B; Shrestha, Sujata; Ingty, Tenzing; Maraseni, Tek; Cockfield, Geoff; Raubenheimer, David

2016-06-01

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator-prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy-deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate-only model shows that only 11.64% (17,190 km(2)) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km(2) (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate-only model. It is predicted that future climate may alter the predator-prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards - a species already facing energetic constraints due to the
Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors.

Science.gov (United States)

Marini, C; Fossa, F; Paoli, C; Bellingeri, M; Gnone, G; Vassallo, P

2015-03-01

Habitat modeling is an important tool to investigate the quality of the habitat for a species within a certain area, to predict species distribution and to understand the ecological processes behind it. Many species have been investigated by means of habitat modeling techniques mainly to address effective management and protection policies and cetaceans play an important role in this context. The bottlenose dolphin (Tursiops truncatus) has been investigated with habitat modeling techniques since 1997. The objectives of this work were to predict the distribution of bottlenose dolphin in a coastal area through the use of static morphological features and to compare the prediction performances of three different modeling techniques: Generalized Linear Model (GLM), Generalized Additive Model (GAM) and Random Forest (RF). Four static variables were tested: depth, bottom slope, distance from 100 m bathymetric contour and distance from coast. RF revealed itself both the most accurate and the most precise modeling technique with very high distribution probabilities predicted in presence cells (90.4% of mean predicted probabilities) and with 66.7% of presence cells with a predicted probability comprised between 90% and 100%. The bottlenose distribution obtained with RF allowed the identification of specific areas with particularly high presence probability along the coastal zone; the recognition of these core areas may be the starting point to develop effective management practices to improve T. truncatus protection. Copyright © 2014 Elsevier Ltd. All rights reserved.
Impacts of pesticide mixtures in European rivers as predicted by the Species Sensitivity Distribution (SSD) models and SPEAR bioindication

Science.gov (United States)

Jesenska, Sona; Liess, Mathias; Schäfer, Ralf; Beketov, Mikhail; Blaha, Ludek

2013-04-01

Species sensitivity distribution (SSD) is statistical method broadly used in the ecotoxicological risk assessment of chemicals. Originally it has been used for prospective risk assessment of single substances but nowadays it is becoming more important also in the retrospective risk assessment of mixtures, including the catchment scale. In the present work, SSD predictions (impacts of mixtures consisting of 25 pesticides; data from several catchments in Germany, France and Finland) were compared with SPEAR-pesticides, which a bioindicator index based on biological traits responsive to the effects of pesticides and post-contamination recovery. The results showed statistically significant correlations (Pearson's R, ppesticides (based on field biomonitoring observations). Comparisons of the thresholds established for the SSD and SPEAR approaches (SPEAR-pesticides=45%, i.e. LOEC level, and msPAF = 0.05 for SSD, i.e. HC5) showed that use of chronic toxicity data significantly improved the agreement between the two methods but the SPEAR-pesticides index was still more sensitive. Taken together, the validation study shows good potential of SSD models in predicting the real impacts of micropollutant mixtures on natural communities of aquatic biota.
Distributed predictive control of spiral wave in cardiac excitable media

International Nuclear Information System (INIS)

Zheng-Ning, Gan; Xin-Ming, Cheng

2010-01-01

In this paper, we propose the distributed predictive control strategies of spiral wave in cardiac excitable media. The modified FitzHugh–Nagumo model was used to express the cardiac excitable media approximately. Based on the control-Lyapunov theory, we obtained the distributed control equation, which consists of a positive control-Lyapunov function and a positive cost function. Using the equation, we investigate two kinds of robust control strategies: the time-dependent distributed control strategy and the space-time dependent distributed control strategy. The feasibility of the strategies was demonstrated via an illustrative example, in which the spiral wave was prevented to occur, and the possibility for inducing ventricular fibrillation was eliminated. The strategies are helpful in designing various cardiac devices. Since the second strategy is more efficient and robust than the first one, and the response time in the second strategy is far less than that in the first one, the former is suitable for the quick-response control systems. In addition, our spatiotemporal control strategies, especially the second strategy, can be applied to other cardiac models, even to other reaction-diffusion systems. (general)
Do abundance distributions and species aggregation correctly predict macroecological biodiversity patterns in tropical forests?

Science.gov (United States)

Wiegand, Thorsten; Lehmann, Sebastian; Huth, Andreas; Fortin, Marie‐Josée

2016-01-01

Abstract Aim It has been recently suggested that different ‘unified theories of biodiversity and biogeography’ can be characterized by three common ‘minimal sufficient rules’: (1) species abundance distributions follow a hollow curve, (2) species show intraspecific aggregation, and (3) species are independently placed with respect to other species. Here, we translate these qualitative rules into a quantitative framework and assess if these minimal rules are indeed sufficient to predict multiple macroecological biodiversity patterns simultaneously. Location Tropical forest plots in Barro Colorado Island (BCI), Panama, and in Sinharaja, Sri Lanka. Methods We assess the predictive power of the three rules using dynamic and spatial simulation models in combination with census data from the two forest plots. We use two different versions of the model: (1) a neutral model and (2) an extended model that allowed for species differences in dispersal distances. In a first step we derive model parameterizations that correctly represent the three minimal rules (i.e. the model quantitatively matches the observed species abundance distribution and the distribution of intraspecific aggregation). In a second step we applied the parameterized models to predict four additional spatial biodiversity patterns. Results Species‐specific dispersal was needed to quantitatively fulfil the three minimal rules. The model with species‐specific dispersal correctly predicted the species–area relationship, but failed to predict the distance decay, the relationship between species abundances and aggregations, and the distribution of a spatial co‐occurrence index of all abundant species pairs. These results were consistent over the two forest plots. Main conclusions The three ‘minimal sufficient’ rules only provide an incomplete approximation of the stochastic spatial geometry of biodiversity in tropical forests. The assumption of independent interspecific placements is most
Real-time distributed economic model predictive control for complete vehicle energy management

NARCIS (Netherlands)

Romijn, Constantijn; Donkers, Tijs; Kessels, John; Weiland, Siep

2017-01-01

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving
Predicting the spatial distribution of leaf litterfall in a mixed deciduous forest

NARCIS (Netherlands)

Staelens, Jeroen; Nachtergale, Lieven; Luyssaert, Sebastiaan

2004-01-01

An accurate prediction of the spatial distribution of litterfall can improve insight in the interaction between the canopy layer and forest floor characteristics, which is a key feature in forest nutrient cycling. Attempts to model the spatial variability of litterfall have been made across forest
Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA

Science.gov (United States)

Lowrey, Chris E.; Longshore, Kathleen M.; Riddle, Brett R.; Mantooth, Stacy

2016-01-01

Although montane sky islands surrounded by desert scrub and shrub steppe comprise a large part of the biological diversity of the Basin and Range Province of southwestern North America, comprehensive ecological and population demographic studies for high-elevation small mammals within these areas are rare. Here, we examine the ecology and population parameters of the Palmer’s chipmunk (Tamias palmeri) in the Spring Mountains of southern Nevada, and present a predictive GIS-based distribution and probability of occurrence model at both home range and geographic spatial scales. Logistic regression analyses and Akaike Information Criterion model selection found variables of forest type, slope, and distance to water sources as predictive of chipmunk occurrence at the geographic scale. At the home range scale, increasing population density, decreasing overstory canopy cover, and decreasing understory canopy cover contributed to increased survival rates.
Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Informing biological design by integration of systems and synthetic biology.

Science.gov (United States)

Smolke, Christina D; Silver, Pamela A

2011-03-18

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. Copyright © 2011 Elsevier Inc. All rights reserved.
Biological distribution of iodo-allyl Gabapentin and iodo-Gabapentin

International Nuclear Information System (INIS)

Akat, H.; Yildirim, Y.; Balcan, M.; Yurt Lambrecht, F.; Yilmaz, O.; Duman, Y.

2008-01-01

Gabapentin (GBP) is an anticonvulsant and is widely used in the treatment of epilepsy. In this study, GBP and an allyl derivative of GBP were radioiodinated with 131 I using the iodogen method; then their radiopharmaceutical potential in rats and rabbits was investigated. The radiochemical purity of 131 I-GBP and its derivatives was determined by RTLC. The labeling yield was 95±2%. Biological evaluation was performed in normal rats and rabbits. Labeled compounds were intravenously injected into two rabbits via the ear vein after anesthetizing. The dynamic and static scintigrams were obtained using a gamma camera at different time. Then the labeled compounds were administered intravenously into the rats. The distribution was studied by counting the radioactivity in the removed organs. The results of biodistribution in the rats showed the clearance of 131 IALGBP was faster than 131 I-GBP. On the other hand, the uptake of 131 I-ALGBP in the brain was higher than 131 I-GBP at 60 minutes. (author)
The role of biological fertility in predicting family size.

Science.gov (United States)

Joffe, M; Key, J; Best, N; Jensen, T K; Keiding, N

2009-08-01

It is plausible that a couple's ability to achieve the desired number of children is limited by biological fertility, especially if childbearing is postponed. Family size has declined and semen quality may have deteriorated in much of Europe, although studies have found an increase rather than a decrease in couple fertility. Using four high-quality European datasets, we took the reported time to pregnancy (TTP) as the predictor variable; births reported as following contraceptive failure were an additional category. The outcome variable was final or near-final family size. Potential confounders were maternal age when unprotected sex began prior to the first birth, and maternal smoking. Desired family size was available in only one of the datasets. Couples with a TTP of at least 12 months tended to have smaller families, with odds ratios for the risk of not having a second child approximately 1.8, and for the risk of not having a third child approximately 1.6. Below 12 months no association was observed. Findings were generally consistent across datasets. There was also a more than 2-fold risk of not achieving the desired family size if TTP was 12 months or more for the first child. Within the limits of the available data quality, family size appears to be predicted by biological fertility, even after adjustment for maternal age, if the woman was at least 20 years old when the couple's first attempt at conception started. The contribution of behavioural factors to this result also needs to be investigated.
Chromophoric Dissolved Organic Matter across a Marine Distributed Biological Observatory in the Pacific Arctic Region

Science.gov (United States)

Berman, S. L.; Frey, K. E.; Shake, K. L.; Cooper, L. W.; Grebmeier, J. M.

2014-12-01

Dissolved organic matter (DOM) plays an important role in marine ecosystems as both a carbon source for the microbial food web (and thus a source of CO2 to the atmosphere) and as a light inhibitor in marine environments. The presence of chromophoric dissolved organic matter (CDOM; the optically active portion of total DOM) can have significant controlling effects on transmittance of sunlight through the water column and therefore on primary production as well as the heat balance of the upper ocean. However, CDOM is also susceptible to photochemical degradation, which decreases the flux of solar radiation that is absorbed. Knowledge of the current spatial and temporal distribution of CDOM in marine environments is thus critical for understanding how ongoing and future changes in climate may impact these biological, biogeochemical, and physical processes. We describe the quantity and quality of CDOM along five key productive transects across a developing Distributed Biological Observatory (DBO) in the Pacific Arctic region. The samples were collected onboard the CCGS Sir Wilfred Laurier in July 2013 and 2014. Monitoring of the variability of CDOM along transects of high productivity can provide important insights into biological and biogeochemical cycling across the region. Our analyses include overall concentrations of CDOM, as well as proxy information such as molecular weight, lability, and source (i.e., autochthonous vs. allochthonous) of organic matter. We utilize these field observations to compare with satellite-derived CDOM concentrations determined from the Aqua MODIS satellite platform, which ultimately provides a spatially and temporally continuous synoptic view of CDOM concentrations throughout the region. Examining the current relationships among CDOM, sea ice variability, biological productivity, and biogeochemical cycling in the Pacific Arctic region will likely provide key insights for how ecosystems throughout the region will respond in future
Predictive modelling of grain size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Science.gov (United States)

Baasch, B.; M"uller, H.; von Dobeneck, T.

2018-04-01

In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.
Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

Directory of Open Access Journals (Sweden)

Nicola A. Wardrop

2014-11-01

Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.
Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

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Lemke Ney

2009-09-01

Full Text Available Abstract Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing
Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

Science.gov (United States)

Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among
Hierarchical Model Predictive Control for Resource Distribution

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

2010-01-01

units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...
Analysis and evaluation of forecasting methods and tools to predict future demand for secondary chemical-biological configuration items

OpenAIRE

Ritchey, Chris D.

2013-01-01

Approved for public release; distribution is unlimited As the Engineering Support Activity (ESA) for numerous consumable Chemical Biological items managed by the Defense Logistics Agency (DLA), Edgewood Chemical Biological Center (ECBC) must be able to complete reviews of all procurement packages within 15 calendar days. With such little lead time, it would be very beneficial if ECBC had the ability to forecast when DLA procurement actions will occur. This thesis presents an evaluation of ...
Contribution to biology and distribution studies on some ground beetles species (Coleoptera, Carabidae registered in the Red Data Book of Krasnodarsky Krai

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Alexander S. Bondarenko

2017-05-01

Full Text Available Some biological features and distributional data on seven species of the ground beetles, registered in the Red Data Book of Krasnodarsky Krai, are presented, namely Carabus obtusus, Carabus kaljuzhnyji, Carabus miroshnikovi, Carabus caucasicus, Leistus spinibarbis, Poecilus lyroderus, and Harpalus petri. The results of the field researches, carried out by the authors in 2010–2015, expanded considerably the knowledge of their biological features and regional distribution areas; furthermore, life cycles were reconstructed for four of the above listed species.

Biological effects of petroleum hydrocarbons: Predictions of long-term effects and recovery

International Nuclear Information System (INIS)

Capuzzo, J.M.

1990-01-01

Biological effects of petroleum hydrocarbons on marine organisms and ecosystems are dependent on the persistence and bioavailability of specific hydrocarbons, the ability of organisms to accumulate and metabolize various hydrocarbons, the fate of metabolized products, and the interference of specific hydrocarbons with normal metabolic processes that may alter an organism's chances for survival and reproduction in the environment. In considering the long-term effects of petroleum hydrocarbons on marine ecosystems it is important to ascertain what biological effects may result in subtle ecological changes, changes in community structure and function, and possible impairment of fisheries resources. It is also important to understand which hydrocarbons persist in benthic environments and the sublethal effects that lead to reduced growth, delayed development and reduced reproductive effort, population decline and the loss of that population's function in marine communities. Only through a multi-disciplinary approach to the study of the fate, transport and effects of petroleum hydrocarbons on marine ecosystems will there be a significant improvement in the ability to predict the long-term effects of oil spills and to elucidate the mechanisms of recovery
Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model

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Diaz-Rodriguez, Sebastian; Bozada, Samantha M.; Phifer, Jeremy R.; Paluch, Andrew S.

2016-11-01

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of 2.2± 0.2 log units (ranking 15 out of 62 entries), the correlation coefficient ( R) was 0.6± 0.1 (ranking 35), and 72± 6 % of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.
Predicting Translation Initiation Rates for Designing Synthetic Biology

International Nuclear Information System (INIS)

Reeve, Benjamin; Hargest, Thomas; Gilbert, Charlie; Ellis, Tom

2014-01-01

In synthetic biology, precise control over protein expression is required in order to construct functional biological systems. A core principle of the synthetic biology approach is a model-guided design and based on the biological understanding of the process, models of prokaryotic protein production have been described. Translation initiation rate is a rate-limiting step in protein production from mRNA and is dependent on the sequence of the 5′-untranslated region and the start of the coding sequence. Translation rate calculators are programs that estimate protein translation rates based on the sequence of these regions of an mRNA, and as protein expression is proportional to the rate of translation initiation, such calculators have been shown to give good approximations of protein expression levels. In this review, three currently available translation rate calculators developed for synthetic biology are considered, with limitations and possible future progress discussed.
Universally sloppy parameter sensitivities in systems biology models.

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Ryan N Gutenkunst

2007-10-01

Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
Universally sloppy parameter sensitivities in systems biology models.

Science.gov (United States)

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data

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Trifonov, Vladimir; Pasqualucci, Laura; Dalla-Favera, Riccardo; Rabadan, Raul

2011-12-01

Recent developments in extracting and processing biological and clinical data are allowing quantitative approaches to studying living systems. High-throughput sequencing (HTS), expression profiles, proteomics, and electronic health records (EHR) are some examples of such technologies. Extracting meaningful information from those technologies requires careful analysis of the large volumes of data they produce. In this note, we present a set of fractal-like distributions that commonly appear in the analysis of such data. The first set of examples are drawn from a HTS experiment. Here, the distributions appear as part of the evaluation of the error rate of the sequencing and the identification of tumorogenic genomic alterations. The other examples are obtained from risk factor evaluation and analysis of relative disease prevalence and co-mordbidity as these appear in EHR. The distributions are also relevant to identification of subclonal populations in tumors and the study of quasi-species and intrahost diversity of viral populations.
Institute for Genomics and Systems Biology

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Institute for Genomics and Systems Biology Discover. Predict. Improve. Advancing Human and , 2015 See all Research Papers Featured Video Introduction to Systems Biology Video: Introduction to Systems Biology News Jack Gilbert Heading UChicago Startup that Aims to Predict Behavior of Trillions of
Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

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Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

2017-06-01

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.
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
Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain.

Science.gov (United States)

Rosenbaum, Michael; Agurs-Collins, Tanya; Bray, Molly S; Hall, Kevin D; Hopkins, Mark; Laughlin, Maren; MacLean, Paul S; Maruvada, Padma; Savage, Cary R; Small, Dana M; Stoeckel, Luke

2018-04-01

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment. The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research. The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments. © 2018 The Obesity Society.
WE-B-304-00: Point/Counterpoint: Biological Dose Optimization

International Nuclear Information System (INIS)

2015-01-01

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations
Definition of the dose(tempo)-distribution in the biological irradiation-facility of the RIVM

International Nuclear Information System (INIS)

Bader, F.J.M.

1990-02-01

The RIVM biological irradiation facility (BBF) for the irradiation of biological samples and small animals is a self shielded device and can be safely operated in an existing laboratory environment. There are two 137 Cs sources (15TBq) in a bilateral geometry to give maximum dose uniformity. The easily accessible irradiation chamber is housed in a rotating lead shielding. The dosimetry of BBF was performed by the Dosimetry Section of the RIVM. Experiments were made to determine the absorbed dose in plastic tubes filled with water and the dose distribution over the tube-holder. Separate experiments were made to determine the absorbed dose during the rotation of the irradiation chamber and to check the irradiation timer. For the experiments LiF:Mg,Ti (TLD-100) extruded ribbons were used. The TLDs were calibrated in a collimated beam of 137 Cs gamma rays. The determination of the absorbed dose in water was based on a users biological irradiation set up. The TLDs were individually sealed in thin plastic foil and put in plastic tubes filled for 1/3 with water. The tubes were vertically placed in the tube-holder and placed in the centre of the irradiation chamber. The results show that the absorbed dose in water (determined on January 1, 1990) is equal to 0.97 Gy/timer-unit, with a total uncertainty of 7 percent (1σ). During the rotation of the irradiation chamber the absorbed dose (determined on January 1, 1990) is equal to 0.38 Gy, with a total uncertainty of 15 percent (1σ). The variation of the dose distribution was determined at 15 different measurement points distributed over the tube-holder. The dosis in the measurement point in the centre of the tube-holder was taken as reference value. The maximum observed deviation over the other 14 measurement points amounts to -16 percent of it. The BBF-timer was checked against a special timer. The results indicate that within a range from 2-11 'timer-units' no differences are present. (author). 6 refs.; 6 figs.; 3 fotos
Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

Science.gov (United States)

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.
Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.

Directory of Open Access Journals (Sweden)

Christopher Y Park

Full Text Available A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics
Diversity of Pyrrolizidine Alkaloids in the Boraginaceae Structures, Distribution, and Biological Properties

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Assem El-Shazly

2014-04-01

Full Text Available Among the diversity of secondary metabolites which are produced by plants as means of defence against herbivores and microbes, pyrrolizidine alkaloids (PAs are common in Boraginaceae, Asteraceae and some other plant families. Pyrrolizidine alkaloids are infamous as toxic compounds which can alkylate DNA und thus cause mutations and even cancer in herbivores and humans. Almost all genera of the family Boraginaceae synthesize and store this type of alkaloids. This review reports the available information on the present status (literature up to early 2014 of the pyrrolizidine alkaloids in the Boraginaceae and summarizes the topics structure, distribution, chemistry, chemotaxonomic significance, and biological properties.
Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

DEFF Research Database (Denmark)

Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.

2018-01-01

, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...
Biological variation of lipid constituents and distribution of tocopherols and astaxanthin in farmed Atlantic salmon (Salmo salar)

DEFF Research Database (Denmark)

Refsgaard, Hanne; Brockhoff, Per B; Jensen, Benny

1998-01-01

The contents of fat, astaxanthin, and tocogherols and the fatty acid composition of a homogeneous group of 145 farmed Atlantic salmon (Salmo salar) were determined. The analytical variation of the data was stastistically-separated from the biological variation. The fat content in the muscle near...... the head was 15.0% with a biological standard deviation of 3.0%. The astaxanthin concentration was 5.5 mg/kg of muscle with a biological standard deviation of 1.1 mg/kg of muscle, and the canthaxanthin concentration was 200 mu g/kg of muscle with a standard deviation of 47 mu g/kg of muscle....... The concentrations of alpha-, gamma-, and delta-tocopherols were approximately 32, 2.9, and 0.4 mg/kg of muscle, respectively, and the biological standard deviations were 4.5, 0.4, and 0.07 mg/kg (14, 14, and 20%), respectively. in another group of five salmon the distributions throughout the fillet were determined...
Introducing Biological Microdosimetry for Ionising Radiation

International Nuclear Information System (INIS)

Scott, B.R.; Schoellnberger, H.

2000-01-01

Microdosimetry is important for radiation protection, for understanding mechanisms of radiation action, and for radiation risk assessment. This article introduces a generic, Monte Carlo based approach to biological microdosimetry for ionising radiation. Our Monte Carlo analyses are carried out with a widely used Crystal Ball software. The approach to biological microdosimetry presented relates to quantal biological effects data (e.g. cell survival, mutagenesis, neoplastic transformation) for which there is an initial linear segment to the dose-response curve. The macroscopic dose data considered were selected such that is could be presumed that the vast majority of cells at risk have radiation dose delivered to their critical target. For cell killing, neoplastic transformation, and mutagenesis, the critical biological target for radiation is presumed to be DNA. Our approach to biological microdosimetry does not require detailed information about the mass, volume, and shape of the critical biological target. Further, one does not have to know what formal distribution function applies to the microdose distribution. However, formal distributions are required for the biological data used to derive the non-parametric microdose distributions. Here, we use the binomial distribution to characterise the variability in the number of cells affected by a fixed macroscopic dose. Assuming this variability to arise from variability in the microscopic dose to the critical biological target, a non-parametric microdose distribution is generated by the standard Monte Carlo method. The non-parametric distribution is then fitted using a set of formal distributions (beta, exponential, extreme value, gamma, logistic, log-normal, normal, Pareto, triangular, uniform, and Weibull). The best fit is then evaluated based on statistical criteria (chi-square test). To demonstrate the application of biological microdosimetry, the standard Monte Carlo method is used with radiobiological data for
Introducing Biological Microdosimetry for Ionising Radiation

Energy Technology Data Exchange (ETDEWEB)

Scott, B.R.; Schoellnberger, H

2000-07-01

Microdosimetry is important for radiation protection, for understanding mechanisms of radiation action, and for radiation risk assessment. This article introduces a generic, Monte Carlo based approach to biological microdosimetry for ionising radiation. Our Monte Carlo analyses are carried out with a widely used Crystal Ball software. The approach to biological microdosimetry presented relates to quantal biological effects data (e.g. cell survival, mutagenesis, neoplastic transformation) for which there is an initial linear segment to the dose-response curve. The macroscopic dose data considered were selected such that is could be presumed that the vast majority of cells at risk have radiation dose delivered to their critical target. For cell killing, neoplastic transformation, and mutagenesis, the critical biological target for radiation is presumed to be DNA. Our approach to biological microdosimetry does not require detailed information about the mass, volume, and shape of the critical biological target. Further, one does not have to know what formal distribution function applies to the microdose distribution. However, formal distributions are required for the biological data used to derive the non-parametric microdose distributions. Here, we use the binomial distribution to characterise the variability in the number of cells affected by a fixed macroscopic dose. Assuming this variability to arise from variability in the microscopic dose to the critical biological target, a non-parametric microdose distribution is generated by the standard Monte Carlo method. The non-parametric distribution is then fitted using a set of formal distributions (beta, exponential, extreme value, gamma, logistic, log-normal, normal, Pareto, triangular, uniform, and Weibull). The best fit is then evaluated based on statistical criteria (chi-square test). To demonstrate the application of biological microdosimetry, the standard Monte Carlo method is used with radiobiological data for
Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

Science.gov (United States)

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

Directory of Open Access Journals (Sweden)

Chunrong Mi

2017-01-01

Full Text Available Species distribution models (SDMs have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33, White-naped Crane (Grus vipio, n = 40, and Black-necked Crane (Grus nigricollis, n = 75 in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model, Random Forest, CART (Classification and Regression Tree and Maxent (Maximum Entropy Models. In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC and true skill statistic (TSS were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid
Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

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Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n = 33), White-naped Crane ( Grus vipio , n = 40), and Black-necked Crane ( Grus nigricollis , n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid
A Personalized Approach to Biological Therapy Using Prediction of Clinical Response Based on MRP8/14 Serum Complex Levels in Rheumatoid Arthritis Patients.

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S C Nair

Full Text Available Measurement of MRP8/14 serum levels has shown potential in predicting clinical response to different biological agents in rheumatoid arthritis (RA. We aimed to develop a treatment algorithm based on a prediction score using MRP8/14 measurements and clinical parameters predictive for response to different biological agents.Baseline serum levels of MRP8/14 were measured in 170 patients starting treatment with infliximab, adalimumab or rituximab. We used logistic regression analysis to develop a predictive score for clinical response at 16 weeks. MRP8/14 levels along with clinical variables at baseline were investigated. We also investigated how the predictive effect of MRP8/14 was modified by drug type. A treatment algorithm was developed based on categorizing the expected response per drug type as high, intermediate or low for each patient and optimal treatment was defined. Finally, we present the utility of using this treatment algorithm in clinical practice.The probability of response increased with higher baseline MRP8/14 complex levels (OR = 1.39, differentially between the TNF-blockers and rituximab (OR of interaction term = 0.78, and also increased with higher DAS28 at baseline (OR = 1.28. Rheumatoid factor positivity, functional disability (a higher HAQ, and previous use of a TNF-inhibitor decreased the probability of response. Based on the treatment algorithm 80 patients would have been recommended for anti-TNF treatment, 8 for rituximab, 13 for another biological treatment (other than TNFi or rituximab and for 69 no recommendation was made. The predicted response rates matched the observed response in the cohort well. On group level the predicted response based on the algorithm resulted in a modest 10% higher response rate in our cohort with much higher differences in response probability in individual patients treated contrary to treatment recommendation.Prediction of response using MRP8/14 levels along with clinical predictors has
The size distribution of marine atmospheric aerosol with regard to primary biological aerosol particles over the South Atlantic Ocean

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Matthias-Maser, Sabine; Brinkmann, Jutta; Schneider, Wilhelm

The marine atmosphere is characterized by particles which originate from the ocean and by those which reached the air by advection from the continent. The bubble-burst mechanism produces both sea salt as well as biological particles. The following article describes the determination of the size distribution of marine aerosol particles with special emphasis on the biological particles. Th data were obtained on three cruises with the German Research Vessel "METEOR" crossing the South Atlantic Ocean. The measurements showed that biological particles amount to 17% in number and 10% in volume concentration. Another type of particle became obvious in the marine atmosphere, the biologically contaminated particle, i.e. particles which consist partly (approximately up to one-third) of biological matter. Their concentration in the evaluated size class ( r>2 μm) is higher than the concentration of the pure biological particles. The concentrations vary over about one to two orders of magnitude during all cruises.
Study (Prediction of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods

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Massoud Tabesh

2011-07-01

Full Text Available Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models and Evolutionary polynomial Regression method (EPR pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods.
Visibility from roads predict the distribution of invasive fishes in agricultural ponds.

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Kizuka, Toshikazu; Akasaka, Munemitsu; Kadoya, Taku; Takamura, Noriko

2014-01-01

Propagule pressure and habitat characteristics are important factors used to predict the distribution of invasive alien species. For species exhibiting strong propagule pressure because of human-mediated introduction of species, indicators of introduction potential must represent the behavioral characteristics of humans. This study examined 64 agricultural ponds to assess the visibility of ponds from surrounding roads and its value as a surrogate of propagule pressure to explain the presence and absence of two invasive fish species. A three-dimensional viewshed analysis using a geographic information system quantified the visual exposure of respective ponds to humans. Binary classification trees were developed as a function of their visibility from roads, as well as five environmental factors: river density, connectivity with upstream dam reservoirs, pond area, chlorophyll a concentration, and pond drainage. Traditional indicators of human-mediated introduction (road density and proportion of urban land-use area) were alternatively included for comparison instead of visual exposure. The presence of Bluegill (Lepomis macrochirus) was predicted by the ponds' higher visibility from roads and pond connection with upstream dam reservoirs. Results suggest that fish stocking into ponds and their dispersal from upstream sources facilitated species establishment. Largemouth bass (Micropterus salmoides) distribution was constrained by chlorophyll a concentration, suggesting their lower adaptability to various environments than that of Bluegill. Based on misclassifications from classification trees for Bluegill, pond visual exposure to roads showed greater predictive capability than traditional indicators of human-mediated introduction. Pond visibility is an effective predictor of invasive species distribution. Its wider use might improve management and mitigate further invasion. The visual exposure of recipient ecosystems to humans is important for many invasive species that
Biokinetics of zinc oxide nanoparticles: toxicokinetics, biological fates, and protein interaction

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Choi SJ

2014-12-01

Full Text Available Soo-Jin Choi,1 Jin-Ho Choy2 1Department of Food Science and Technology, Seoul Women's University, 2Center for Intelligent Nano Bio Materials (CINBM, Department of Bioinspired Science and Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, South Korea Abstract: Biokinetic studies of zinc oxide (ZnO nanoparticles involve systematic and quantitative analyses of absorption, distribution, metabolism, and excretion in plasma and tissues of whole animals after exposure. A full understanding of the biokinetics provides basic information about nanoparticle entry into systemic circulation, target organs of accumulation and toxicity, and elimination time, which is important for predicting the long-term toxic potential of nanoparticles. Biokinetic behaviors can be dependent on physicochemical properties, dissolution property in biological fluids, and nanoparticle–protein interaction. Moreover, the determination of biological fates of ZnO nanoparticles in the systemic circulation and tissues is critical in interpreting biokinetic behaviors and predicting toxicity potential as well as mechanism. This review focuses on physicochemical factors affecting the biokinetics of ZnO nanoparticles, in concert with understanding bioavailable fates and their interaction with proteins. Keywords: ZnO nanoparticles, biokinetics, distribution, excretion, fate, interaction
Performance prediction of a synchronization link for distributed aerospace wireless systems.

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Wang, Wen-Qin; Shao, Huaizong

2013-01-01

For reasons of stealth and other operational advantages, distributed aerospace wireless systems have received much attention in recent years. In a distributed aerospace wireless system, since the transmitter and receiver placed on separated platforms which use independent master oscillators, there is no cancellation of low-frequency phase noise as in the monostatic cases. Thus, high accurate time and frequency synchronization techniques are required for distributed wireless systems. The use of a dedicated synchronization link to quantify and compensate oscillator frequency instability is investigated in this paper. With the mathematical statistical models of phase noise, closed-form analytic expressions for the synchronization link performance are derived. The possible error contributions including oscillator, phase-locked loop, and receiver noise are quantified. The link synchronization performance is predicted by utilizing the knowledge of the statistical models, system error contributions, and sampling considerations. Simulation results show that effective synchronization error compensation can be achieved by using this dedicated synchronization link.
Prediction of thermal coagulation from the instantaneous strain distribution induced by high-intensity focused ultrasound

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Iwasaki, Ryosuke; Takagi, Ryo; Tomiyasu, Kentaro; Yoshizawa, Shin; Umemura, Shin-ichiro

2017-07-01

The targeting of the ultrasound beam and the prediction of thermal lesion formation in advance are the requirements for monitoring high-intensity focused ultrasound (HIFU) treatment with safety and reproducibility. To visualize the HIFU focal zone, we utilized an acoustic radiation force impulse (ARFI) imaging-based method. After inducing displacements inside tissues with pulsed HIFU called the push pulse exposure, the distribution of axial displacements started expanding and moving. To acquire RF data immediately after and during the HIFU push pulse exposure to improve prediction accuracy, we attempted methods using extrapolation estimation and applying HIFU noise elimination. The distributions going back in the time domain from the end of push pulse exposure are in good agreement with tissue coagulation at the center. The results suggest that the proposed focal zone visualization employing pulsed HIFU entailing the high-speed ARFI imaging method is useful for the prediction of thermal coagulation in advance.
Emphysema Distribution and Diffusion Capacity Predict Emphysema Progression in Human Immunodeficiency Virus Infection

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Leung, Janice M; Malagoli, Andrea; Santoro, Antonella; Besutti, Giulia; Ligabue, Guido; Scaglioni, Riccardo; Dai, Darlene; Hague, Cameron; Leipsic, Jonathon; Sin, Don D.; Man, SF Paul; Guaraldi, Giovanni

2016-01-01

Background Chronic obstructive pulmonary disease (COPD) and emphysema are common amongst patients with human immunodeficiency virus (HIV). We sought to determine the clinical factors that are associated with emphysema progression in HIV. Methods 345 HIV-infected patients enrolled in an outpatient HIV metabolic clinic with ≥2 chest computed tomography scans made up the study cohort. Images were qualitatively scored for emphysema based on percentage involvement of the lung. Emphysema progression was defined as any increase in emphysema score over the study period. Univariate analyses of clinical, respiratory, and laboratory data, as well as multivariable logistic regression models, were performed to determine clinical features significantly associated with emphysema progression. Results 17.4% of the cohort were emphysema progressors. Emphysema progression was most strongly associated with having a low baseline diffusion capacity of carbon monoxide (DLCO) and having combination centrilobular and paraseptal emphysema distribution. In adjusted models, the odds ratio (OR) for emphysema progression for every 10% increase in DLCO percent predicted was 0.58 (95% confidence interval [CI] 0.41–0.81). The equivalent OR (95% CI) for centrilobular and paraseptal emphysema distribution was 10.60 (2.93–48.98). Together, these variables had an area under the curve (AUC) statistic of 0.85 for predicting emphysema progression. This was an improvement over the performance of spirometry (forced expiratory volume in 1 second to forced vital capacity ratio), which predicted emphysema progression with an AUC of only 0.65. Conclusion Combined paraseptal and centrilobular emphysema distribution and low DLCO could identify HIV patients who may experience emphysema progression. PMID:27902753
DNA methylation-based measures of biological age: meta-analysis predicting time to death

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Chen, Brian H.; Marioni, Riccardo E.; Colicino, Elena; Peters, Marjolein J.; Ward-Caviness, Cavin K.; Tsai, Pei-Chien; Roetker, Nicholas S.; Just, Allan C.; Demerath, Ellen W.; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R.; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P.; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L.; Murabito, Joanne M.; Bandinelli, Stefania; Hernandez, Dena G.; Melzer, David; Nalls, Michael; Pilling, Luke C.; Price, Timothy R.; Singleton, Andrew B.; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M.; Shah, Sonia; Wray, Naomi R.; McRae, Allan F.; Franco, Oscar H.; Hofman, Albert; Uitterlinden, André G.; Absher, Devin; Assimes, Themistocles; Levine, Morgan E.; Lu, Ake T.; Tsao, Philip S.; Hou, Lifang; Manson, JoAnn E.; Carty, Cara L.; LaCroix, Andrea Z.; Reiner, Alexander P.; Spector, Tim D.; Feinberg, Andrew P.; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T.; Peters, Annette; Deary, Ian J.; Pankow, James S.; Ferrucci, Luigi; Horvath, Steve

2016-01-01

Estimates of biological age based on DNA methylation patterns, often referred to as “epigenetic age”, “DNAm age”, have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2×10−9), independent of chronological age, even after adjusting for additional risk factors (p<5.4×10−4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5×10−43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality. PMID:27690265
Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

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Piana, Chiara; Danhof, Meindert; Della Pasqua, Oscar

2014-01-01

Aims The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. Methods Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10–15 kg and 30–40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. Results Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. Conclusions In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. PMID:24433411
Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

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Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that
Spatiotemporal trends in Canadian domestic wild boar production and habitat predict wild pig distribution

DEFF Research Database (Denmark)

Michel, Nicole; Laforge, Michel; van Beest, Floris

2017-01-01

eradication of wild pigs is rarely feasible after establishment over large areas, effective management will depend on strengthening regulations and enforcement of containment practices for Canadian domestic wild boar farms. Initiation of coordinated provincial and federal efforts to implement population...... wild boar and test the propagule pressure hypothesis to improve predictive ability of an existing habitat-based model of wild pigs. We reviewed spatiotemporal patterns in domestic wild boar production across ten Canadian provinces during 1991–2011 and evaluated the ability of wild boar farm...... distribution to improve predictive models of wild pig occurrence using a resource selection probability function for wild pigs in Saskatchewan. Domestic wild boar production in Canada increased from 1991 to 2001 followed by sharp declines in all provinces. The distribution of domestic wild boar farms in 2006...
Prediction of the low-velocity distribution from the pore structure in simple porous media

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de Anna, Pietro; Quaife, Bryan; Biros, George; Juanes, Ruben

2017-12-01

The macroscopic properties of fluid flow and transport through porous media are a direct consequence of the underlying pore structure. However, precise relations that characterize flow and transport from the statistics of pore-scale disorder have remained elusive. Here we investigate the relationship between pore structure and the resulting fluid flow and asymptotic transport behavior in two-dimensional geometries of nonoverlapping circular posts. We derive an analytical relationship between the pore throat size distribution fλ˜λ-β and the distribution of the low fluid velocities fu˜u-β /2 , based on a conceptual model of porelets (the flow established within each pore throat, here a Hagen-Poiseuille flow). Our model allows us to make predictions, within a continuous-time random-walk framework, for the asymptotic statistics of the spreading of fluid particles along their own trajectories. These predictions are confirmed by high-fidelity simulations of Stokes flow and advective transport. The proposed framework can be extended to other configurations which can be represented as a collection of known flow distributions.
Regional climate model downscaling may improve the prediction of alien plant species distributions

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Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

2014-12-01

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.
Biology-inspired Microphysiological System Approaches to Solve the Prediction Dilemma of Substance Testing

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Marx, Uwe; Andersson, Tommy B.; Bahinski, Anthony; Beilmann, Mario; Beken, Sonja; Cassee, Flemming R.; Cirit, Murat; Daneshian, Mardas; Fitzpatrick, Susan; Frey, Olivier; Gaertner, Claudia; Giese, Christoph; Griffith, Linda; Hartung, Thomas; Heringa, Minne B.; Hoeng, Julia; de Jong, Wim H.; Kojima, Hajime; Kuehnl, Jochen; Luch, Andreas; Maschmeyer, Ilka; Sakharov, Dmitry; Sips, Adrienne J. A. M.; Steger-Hartmann, Thomas; Tagle, Danilo A.; Tonevitsky, Alexander; Tralau, Tewes; Tsyb, Sergej; van de Stolpe, Anja; Vandebriel, Rob; Vulto, Paul; Wang, Jufeng; Wiest, Joachim; Rodenburg, Marleen; Roth, Adrian

2017-01-01

Summary The recent advent of microphysiological systems – microfluidic biomimetic devices that aspire to emulate the biology of human tissues, organs and circulation in vitro – is envisaged to enable a global paradigm shift in drug development. An extraordinary US governmental initiative and various dedicated research programs in Europe and Asia have led recently to the first cutting-edge achievements of human single-organ and multi-organ engineering based on microphysiological systems. The expectation is that test systems established on this basis would model various disease stages, and predict toxicity, immunogenicity, ADME profiles and treatment efficacy prior to clinical testing. Consequently, this technology could significantly affect the way drug substances are developed in the future. Furthermore, microphysiological system-based assays may revolutionize our current global programs of prioritization of hazard characterization for any new substances to be used, for example, in agriculture, food, ecosystems or cosmetics, thus, replacing laboratory animal models used currently. Thirty-five experts from academia, industry and regulatory bodies present here the results of an intensive workshop (held in June 2015, Berlin, Germany). They review the status quo of microphysiological systems available today against industry needs, and assess the broad variety of approaches with fit-for-purpose potential in the drug development cycle. Feasible technical solutions to reach the next levels of human biology in vitro are proposed. Furthermore, key organ-on-a-chip case studies, as well as various national and international programs are highlighted. Finally, a roadmap into the future is outlined, to allow for more predictive and regulatory-accepted substance testing on a global scale. PMID:27180100
Fractal scaling of particle size distribution and relationships with topsoil properties affected by biological soil crusts.

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Guang-Lei Gao

Full Text Available BACKGROUND: Biological soil crusts are common components of desert ecosystem; they cover ground surface and interact with topsoil that contribute to desertification control and degraded land restoration in arid and semiarid regions. METHODOLOGY/PRINCIPAL FINDINGS: To distinguish the changes in topsoil affected by biological soil crusts, we compared topsoil properties across three types of successional biological soil crusts (algae, lichens, and mosses crust, as well as the referenced sandland in the Mu Us Desert, Northern China. Relationships between fractal dimensions of soil particle size distribution and selected soil properties were discussed as well. The results indicated that biological soil crusts had significant positive effects on soil physical structure (P<0.05; and soil organic carbon and nutrients showed an upward trend across the successional stages of biological soil crusts. Fractal dimensions ranged from 2.1477 to 2.3032, and significantly linear correlated with selected soil properties (R(2 = 0.494∼0.955, P<0.01. CONCLUSIONS/SIGNIFICANCE: Biological soil crusts cause an important increase in soil fertility, and are beneficial to sand fixation, although the process is rather slow. Fractal dimension proves to be a sensitive and useful index for quantifying changes in soil properties that additionally implies desertification. This study will be essential to provide a firm basis for future policy-making on optimal solutions regarding desertification control and assessment, as well as degraded ecosystem restoration in arid and semiarid regions.
Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

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Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.
Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

2013-01-01

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

IQ Predicts Biological Motion Perception in Autism Spectrum Disorders

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Rutherford, M. D.; Troje, Nikolaus F.

2012-01-01

Biological motion is easily perceived by neurotypical observers when encoded in point-light displays. Some but not all relevant research shows significant deficits in biological motion perception among those with ASD, especially with respect to emotional displays. We tested adults with and without ASD on the perception of masked biological motion…
Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

DEFF Research Database (Denmark)

Hickey, John M.; Chiurugwi, Tinashe; Mackay, Ian

2017-01-01

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human...... that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying...... use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform...
Direct Comparison of Biologically Optimized Spread-out Bragg Peaks for Protons and Carbon Ions

International Nuclear Information System (INIS)

Wilkens, Jan J.; Oelfke, Uwe

2008-01-01

Purpose: In radiotherapy with hadrons, it is anticipated that carbon ions are superior to protons, mainly because of their biological properties: the relative biological effectiveness (RBE) for carbon ions is supposedly higher in the target than in the surrounding normal tissue, leading to a therapeutic advantage over protons. The purpose of this report is to investigate this effect by using biological model calculations. Methods and Materials: We compared spread-out Bragg peaks for protons and carbon ions by using physical and biological optimization. The RBE for protons and carbon ions was calculated according to published biological models. These models predict increased RBE values in regions of high linear energy transfer (LET) and an inverse dependency of the RBE on dose. Results: For pure physical optimization, protons yield a better dose distribution along the central axis. In biologically optimized plans, RBE variations for protons were relatively small. For carbon ions, high RBE values were found in the high-LET target region, as well as in the low-dose region outside the target. This means that the LET dependency and dose dependency of the RBE can cancel each other. We show this for radioresistant tissues treated with two opposing beams, for which the predicted carbon RBE within the target volume was lower than outside. Conclusions: For tissue parameters used in this study, the model used does not predict a biologic advantage of carbon ions. More reliable model parameters and clinical trials are necessary to explore the true potential of radiotherapy with carbon ions
Interpretation of proton relative biological effectiveness using lesion induction, lesion repair, and cellular dose distribution

International Nuclear Information System (INIS)

Paganetti, H.

2005-01-01

Phenomenological biophysical models have been successfully used to estimate the relative biological effectiveness (RBE) of ions. The predictive power of these models is limited because they require measured dose-response data that are not necessarily available for all clinically relevant end points. Furthermore, input parameters often lack mechanistic interpretation. In order to link RBE to more fundamental biological parameters we combine the concepts of two well-established biophysical models, i.e., the phenomenological 'track structure' model and the more mechanistic 'lethal lesion/potentially lethal lesion' (LPL) model. We parametrize a relation between RBE, dose homogeneity in the cell nucleus and induction rates for different lesion types. The macroscopic dose-response relationship is described in the LPL model and the microscopic, subcellular, relationship is determined by the local dose deposition pattern. The formalism provides a framework for a mechanistic interpretation of RBE values
Multivariate models for prediction of rheological characteristics of filamentous fermentation broth from the size distribution.

Science.gov (United States)

Petersen, Nanna; Stocks, Stuart; Gernaey, Krist V

2008-05-01

The main purpose of this article is to demonstrate that principal component analysis (PCA) and partial least squares regression (PLSR) can be used to extract information from particle size distribution data and predict rheological properties. Samples from commercially relevant Aspergillus oryzae fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield stress (tau(y)), consistency index (K), and flow behavior index (n)) resulted in a large standard deviation of the parameter estimates. The flow behavior index was not found to be correlated with any of the other measured variables and previous studies have suggested a constant value of the flow behavior index in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining rheological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (micro(app)), yield stress (tau(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as micro(app). Validation on an independent test set yielded a root mean square error of 1.21 Pa for tau(y), 0.209 Pa s(n) for K, and 0.0288 Pa s for micro(app), corresponding to R(2) = 0.95, R(2) = 0.94, and R(2) = 0.95 respectively. Copyright 2007 Wiley Periodicals, Inc.
Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

Science.gov (United States)

Ahmed, Shiek S S J; Ramakrishnan, V

2012-01-01

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy
Correlation of microdosimetric measurements with relative biological effectiveness from clinical experience for two neutron therapy beams

International Nuclear Information System (INIS)

Stinchcomb, T.G.; Kuchnir, F.T.; Myrianthopoulos, L.C.; Horton, J.L. Jr.; Roberts, W.K.

1986-01-01

Microdosimetric measurements were made for the neutron therapy beams at the University of Chicago and at the Cleveland Clinic with the same geometry and phantom material using the same tissue-equivalent spherical proportional counter and standard techniques. The energy deposition spectra (dose distributions in lineal energy) are compared for these beams and for their scattered components (direct beam blocked). The model of dual radiation action (DRA) of Kellerer and Rossi is employed to interpret these data in terms of biological effectiveness over this limited range of radiation qualities. The site-diameter parameter of the DRA theory is determined for the Cleveland beam by setting the biological effectiveness (relative to 60 Co gamma radiation) equal to the relative biological effectiveness value deduced from radiobiology experiments and clinical experience. The resulting value of this site-diameter parameter is then used to predict the biological effectiveness of the Chicago beam. The prediction agrees with the value deduced from radiobiology and clinical experience. The biological effectiveness of the scattered components of both beams is also estimated using the model
A nodal model to predict vertical temperature distribution in a room with floor heating and displacement ventilation

DEFF Research Database (Denmark)

Wu, Xiaozhou; Olesen, Bjarne W.; Fang, Lei

2013-01-01

In this paper, the development of a nodal model that predicts vertical temperature distribution in a typical office room with floor heating and displacement ventilation (FHDV) is described. The vertical air flow distribution is first determined according to the principle of displacement ventilati...
Optimization of labeling conditions of n-isopropyl-p-iodoamphetamine chloridate (IMP) with radioiodine. Biological distribution studies

International Nuclear Information System (INIS)

Colturato, Maria Tereza

2000-01-01

The development of this work was based on a great interest from the medical community in the utilization of N-isopropyl-p-iodoamphetamine chloridate (IMP) labeled with 123 l, for brain perfusion evaluation. The IMP was initially characterized by: Melting Point (MP), Infrared Spectrophotometry (IR), Nuclear Magnetic Resonance Spectrometry (NMR), Elemental Analysis and High Performance Liquid Chromatography (HPLC). After having chosen the ideal method (nucleophilic substitution) to label IMP with that used Cu(I) as reducing agent and ascorbic acid as catalyzing of Cu(II), studies were performed to optimize the labeling parameters of 123 l-IMP: temperature reaction, time reaction, ascorbic acid mass, pH and molar ratio, and stability of the final product. The quality control method (ascending paper chromatographic) used to determine the radiochemistry purity showed to be efficient, fast and of easily handling for routine production. Biological distribution studies were performed with laboratory animals (mice) to determine the percent administered dose in the blood, different organs and whole body after intravenous administration of the radiopharmaceutical. Toxicological evaluation and in vitro study to determine the plasmatic protein binding were also done. The data of the biological distribution in mice have shown that the product crossed the intact blood brain barrier, for a enough time to obtain brain scintigraphic image, thus, allowing a follow up of further studies after the intravenous administration of the radiopharmaceutical. The 123 l-IMP showed a blood clearance and then the principal elimination route was the urinary. The kinetic study of 123 l-IMP, submitting blood samples data to BIEXP.BAS program, showed a biexponential pattern which allowed demonstrating that the compound presents a first phase of quick distribution and a second one slower corresponding to the equilibrium and elimination. Based on the results from radiochemical purity, stability and
Logic-based models in systems biology: a predictive and parameter-free network analysis method.

Science.gov (United States)

Wynn, Michelle L; Consul, Nikita; Merajver, Sofia D; Schnell, Santiago

2012-11-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.
Three-dimensional distributions of elements in biological samples by energy-filtered electron tomography

Energy Technology Data Exchange (ETDEWEB)

Leapman, R.D.; Kocsis, E.; Zhang, G.; Talbot, T.L.; Laquerriere, P

2004-07-15

By combining electron tomography with energy-filtered electron microscopy, we have shown the feasibility of determining the three-dimensional distributions of phosphorus in biological specimens. Thin sections of the nematode, Caenorhabditis elegans were prepared by high-pressure freezing, freeze-substitution and plastic embedding. Images were recorded at energy losses above and below the phosphorus L{sub 2,3} edge using a post-column imaging filter operating at a beam energy of 120 keV. The unstained specimens exhibited minimal contrast in bright-field images. After it was determined that the specimen was sufficiently thin to allow two-window ratio imaging of phosphorus, pairs of pre-edge and post-edge images were acquired in series over a tilt range of {+-}55 deg. at 5 deg. increments for two orthogonal tilt axes. The projected phosphorus distributions were aligned using the pre-edge images that contained inelastic contrast from colloidal gold particles deposited on the specimen surface. A reconstruction and surface rendering of the phosphorus distribution clearly revealed features 15-20 nm in diameter, which were identified as ribosomes distributed along the stacked membranes of endoplasmic reticulum and in the cytoplasm. The sensitivity of the technique was estimated at <35 phosphorus atoms per voxel based on the known total ribosomal phosphorus content of approximately 7000 atoms. Although a high electron dose of approximately 10{sup 7} e/nm{sup 2} was required to record two-axis tilt series, specimens were sufficiently stable to allow image alignment and tomographic reconstruction.
Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

Science.gov (United States)

Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

2017-01-01

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

Science.gov (United States)

Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

2017-03-01

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Using a topographic index to distribute variable source area runoff predicted with the SCS curve-number equation

Science.gov (United States)

Lyon, Steve W.; Walter, M. Todd; Gérard-Marchant, Pierre; Steenhuis, Tammo S.

2004-10-01

Because the traditional Soil Conservation Service curve-number (SCS-CN) approach continues to be used ubiquitously in water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed and tested a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Predicting the location of source areas is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point-source pollution. The method presented here used the traditional SCS-CN approach to predict runoff volume and spatial extent of saturated areas and a topographic index, like that used in TOPMODEL, to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was applied to two subwatersheds of the Delaware basin in the Catskill Mountains region of New York State and one watershed in south-eastern Australia to produce runoff-probability maps. Observed saturated area locations in the watersheds agreed with the distributed CN-VSA method. Results showed good agreement with those obtained from the previously validated soil moisture routing (SMR) model. When compared with the traditional SCS-CN method, the distributed CN-VSA method predicted a similar total volume of runoff, but vastly different locations of runoff generation. Thus, the distributed CN-VSA approach provides a physically based method that is simple enough to be incorporated into water quality models, and other tools that currently use the traditional SCS-CN method, while still adhering to the principles of VSA hydrology.
CFD prediction of flow and phase distribution in fuel assemblies with spacers

Energy Technology Data Exchange (ETDEWEB)

Anglart, H.; Nylund, O. [ABB Atom AB, Vasteras (Switzerland); Kurul, N. [Rensselaer Polytechnic Institute, Troy, NY (United States)] [and others

1995-09-01

This paper is concerned with the modeling and computation of multi-dimensional two-phase flows in BWR fuel assemblies. The modeling principles are presented based on using a two-fluid model in which lateral interfacial effects are accounted for. This model has been used to evaluate the velocity fields of both vapor and liquid phases, as well as phase distribution, between fuel elements in geometries similar to BWR fuel bundles. Furthermore, this model has been used to predict, in a detailed mechanistic manner, the effects of spacers on flow and phase distribution between, and pressure drop along, fuel elements. The related numerical simulations have been performed using a CFD computer code, CFDS-FLOW3D.
Mathematical model of heat transfer to predict distribution of hardness through the Jominy bar

International Nuclear Information System (INIS)

Lopez, E.; Hernandez, J. B.; Solorio, G.; Vergara, H. J.; Vazquez, O.; Garnica, F.

2013-01-01

The heat transfer coefficient was estimated at the bottom surface at Jominy bar end quench specimen by solution of the heat inverse conduction problem. A mathematical model based on the finite-difference method was developed to predict thermal paths and volume fraction of transformed phases. The mathematical model was codified in the commercial package Microsoft Visual Basic v. 6. The calculated thermal path and final phase distribution were used to evaluate the hardness distribution along the AISI 4140 Jominy bar. (Author)
Working memory capacity of biological movements predicts empathy traits.

Science.gov (United States)

Gao, Zaifeng; Ye, Tian; Shen, Mowei; Perry, Anat

2016-04-01

Working memory (WM) and empathy are core issues in cognitive and social science, respectively. However, no study so far has explored the relationship between these two constructs. Considering that empathy takes place based on the others' observed experiences, which requires extracting the observed dynamic scene into WM and forming a coherent representation, we hypothesized that a sub-type of WM capacity, i.e., WM for biological movements (BM), should predict one's empathy level. Therefore, WM capacity was measured for three distinct types of stimuli in a change detection task: BM of human beings (BM; Experiment 1), movements of rectangles (Experiment 2), and static colors (Experiment 3). The first two stimuli were dynamic and shared one WM buffer which differed from the WM buffer for colors; yet only the BM conveyed social information. We found that BM-WM capacity was positively correlated with both cognitive and emotional empathy, with no such correlations for WM capacity of movements of rectangles or of colors. Thus, the current study is the first to provide evidence linking a specific buffer of WM and empathy, and highlights the necessity for considering different WM capacities in future social and clinical research.
Feature selection for splice site prediction: A new method using EDA-based feature ranking

Directory of Open Access Journals (Sweden)

Rouzé Pierre

2004-05-01

Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.
Predicting spatial and temporal distribution of Indo-Pacific lionfish (Pterois volitans) in Biscayne Bay through habitat suitability modeling

Science.gov (United States)

Bernal, Nicholas A.; DeAngelis, Donald L.; Schofield, Pamela J.; Sullivan Sealey, Kathleen

2014-01-01

Invasive species may exhibit higher levels of growth and reproduction when environmental conditions are most suitable, and thus their effects on native fauna may be intensified. Understanding potential impacts of these species, especially in the nascent stages of a biological invasion, requires critical information concerning spatial and temporal distributions of habitat suitability. Using empirically supported environmental variables (e.g., temperature, salinity, dissolved oxygen, rugosity, and benthic substrate), our models predicted habitat suitability for the invasive lionfish (Pterois volitans) in Biscayne Bay, Florida. The use of Geographic Information Systems (GIS) as a platform for the modeling process allowed us to quantify correlations between temporal (seasonal) fluctuations in the above variables and the spatial distribution of five discrete habitat quality classes, whose ranges are supported by statistical deviations from the apparent best conditions described in prior studies. Analysis of the resulting models revealed little fluctuation in spatial extent of the five habitat classes on a monthly basis. Class 5, which represented the area with environmental variables closest to the best conditions for lionfish, occupied approximately one-third of Biscayne Bay, with subsequent habitats declining in area. A key finding from this study was that habitat suitability increased eastward from the coastline, where higher quality habitats were adjacent to the Atlantic Ocean and displayed marine levels of ambient water quality. Corroboration of the models with sightings from the USGS-NAS database appeared to support our findings by nesting 79 % of values within habitat class 5; however, field testing (i.e., lionfish surveys) is necessary to confirm the relationship between habitat classes and lionfish distribution.
Predicted altitudinal shifts and reduced spatial distribution of Leishmania infantum vector species under climate change scenarios in Colombia.

Science.gov (United States)

González, Camila; Paz, Andrea; Ferro, Cristina

2014-01-01

Visceral leishmaniasis (VL) is caused by the trypanosomatid parasite Leishmania infantum (=Leishmania chagasi), and is epidemiologically relevant due to its wide geographic distribution, the number of annual cases reported and the increase in its co-infection with HIV. Two vector species have been incriminated in the Americas: Lutzomyia longipalpis and Lutzomyia evansi. In Colombia, L. longipalpis is distributed along the Magdalena River Valley while L. evansi is only found in the northern part of the Country. Regarding the epidemiology of the disease, in Colombia the incidence of VL has decreased over the last few years without any intervention being implemented. Additionally, changes in transmission cycles have been reported with urban transmission occurring in the Caribbean Coast. In Europe and North America climate change seems to be driving a latitudinal shift of leishmaniasis transmission. Here, we explored the spatial distribution of the two known vector species of L. infantum in Colombia and projected its future distribution into climate change scenarios to establish the expansion potential of the disease. An updated database including L. longipalpis and L. evansi collection records from Colombia was compiled. Ecological niche models were performed for each species using the Maxent software and 13 Worldclim bioclimatic coverages. Projections were made for the pessimistic CSIRO A2 scenario, which predicts the higher increase in temperature due to non-emission reduction, and the optimistic Hadley B2 Scenario predicting the minimum increase in temperature. The database contained 23 records for L. evansi and 39 records for L. longipalpis, distributed along the Magdalena River Valley and the Caribbean Coast, where the potential distribution areas of both species were also predicted by Maxent. Climate change projections showed a general overall reduction in the spatial distribution of the two vector species, promoting a shift in altitudinal distribution for L

Radionuclide distributions and sorption behavior in the Susquehanna--Chesapeake Bay System

International Nuclear Information System (INIS)

Olsen, C.R.; Larsen, I.L.; Lowry, P.D.; McLean, R.I.; Domotor, S.L.

1989-01-01

Radionuclides released into the Susquehanna--Chesapeake System from the Three Mile Island, Peach Bottom, and Calvert Cliffs nuclear power plants are partitioned among dissolved, particulate, and biological phases and may thus exist in a number of physical and chemical forms. In this project, we have measured the dissolved and particulate distributions of fallout 137 Cs; reactor-released 137 Cs, 134 Cs, 65 Zn, 60 Co, and 58 Co; and naturally occurring 7 Be and 210 Pb in the lower Susquehanna River and Upper Chesapeake Bay. In addition, we chemically leached suspended particles and bottom sediments in the laboratory to determine radionuclide partitioning among different particulate-sorbing phases to complement the site-specific field data. This information has been used to document the important geochemical processes that affect the transport, sorption, distribution, and fate of reactor-released radionuclides (and by analogy, other trace contaminants) in this river-estuarine system. Knowledge of the mechanisms, kinetic factors, and processes that affect radionuclide distributions is crucial for predicting their biological availability, toxicity, chemical behavior, physical transport, and accumulation in aquatic systems. The results from this project provide the information necessary for developing accurate radionuclide-transport and biological-uptake models. 76 refs., 12 figs
Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology.

Science.gov (United States)

Di Marco, Moreno; Buchanan, Graeme M; Szantoi, Zoltan; Holmgren, Milena; Grottolo Marasini, Gabriele; Gross, Dorit; Tranquilli, Sandra; Boitani, Luigi; Rondinini, Carlo

2014-01-01

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.
A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys).

Science.gov (United States)

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive
A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys.

Directory of Open Access Journals (Sweden)

Magdalena Năpăruş

Full Text Available BACKGROUND: Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. METHODOLOGY/PRINCIPAL FINDINGS: We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World, N. livida (Madagascar, N. malabarensis (S-SE Asia, and N. papuana (Australasia. For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range, a large part of Brazil and the Guianas (area of synanthropic spread, and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (subtropics. CONCLUSIONS: Our model is a customizable GIS tool intended to predict current and future potential
Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

Directory of Open Access Journals (Sweden)

Adam M Wilson

2016-03-01

Full Text Available Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.
Biological basis of detoxication

National Research Council Canada - National Science Library

Caldwell, John; Jakoby, William B

1983-01-01

This volume considers that premise that most of the major patterns of biological conversion of foreign compounds are known and may have predictive value in assessing the biological course for novel compounds...
Prediction of residual stress distribution in multi-stacked thin film by curvature measurement and iterative FEA

International Nuclear Information System (INIS)

Choi, Hyeon Chang; Park, Jun Hyub

2005-01-01

In this study, residual stress distribution in multi-stacked film by MEMS (Micro-Electro Mechanical System) process is predicted using Finite Element Method (FEM). We develop a finite element program for REsidual Stress Analysis (RESA) in multi-stacked film. The RESA predicts the distribution of residual stress field in multi-stacked film. Curvatures of multi-stacked film and single layers which consist of the multi-stacked film are used as the input to the RESA. To measure those curvatures is easier than to measure a distribution of residual stress. To verify the RESA, mean stresses and stress gradients of single and multilayers are measured. The mean stresses are calculated from curvatures of deposited wafer by using Stoney's equation. The stress gradients are calculated from the vertical deflection at the end of cantilever beam. To measure the mean stress of each layer in multi-stacked film, we measure the curvature of wafer with the film after etching layer by layer in multi-stacked film
How the Assumed Size Distribution of Dust Minerals Affects the Predicted Ice Forming Nuclei

Science.gov (United States)

Perlwitz, Jan P.; Fridlind, Ann M.; Garcia-Pando, Carlos Perez; Miller, Ron L.; Knopf, Daniel A.

2015-01-01

The formation of ice in clouds depends on the availability of ice forming nuclei (IFN). Dust aerosol particles are considered the most important source of IFN at a global scale. Recent laboratory studies have demonstrated that the mineral feldspar provides the most efficient dust IFN for immersion freezing and together with kaolinite for deposition ice nucleation, and that the phyllosilicates illite and montmorillonite (a member of the smectite group) are of secondary importance.A few studies have applied global models that simulate mineral specific dust to predict the number and geographical distribution of IFN. These studies have been based on the simple assumption that the mineral composition of soil as provided in data sets from the literature translates directly into the mineral composition of the dust aerosols. However, these tables are based on measurements of wet-sieved soil where dust aggregates are destroyed to a large degree. In consequence, the size distribution of dust is shifted to smaller sizes, and phyllosilicates like illite, kaolinite, and smectite are only found in the size range 2 m. In contrast, in measurements of the mineral composition of dust aerosols, the largest mass fraction of these phyllosilicates is found in the size range 2 m as part of dust aggregates. Conversely, the mass fraction of feldspar is smaller in this size range, varying with the geographical location. This may have a significant effect on the predicted IFN number and its geographical distribution.An improved mineral specific dust aerosol module has been recently implemented in the NASA GISS Earth System ModelE2. The dust module takes into consideration the disaggregated state of wet-sieved soil, on which the tables of soil mineral fractions are based. To simulate the atmospheric cycle of the minerals, the mass size distribution of each mineral in aggregates that are emitted from undispersed parent soil is reconstructed. In the current study, we test the null
Circulating Biologically Active Adrenomedullin (bio-ADM) Predicts Hemodynamic Support Requirement and Mortality During Sepsis.

Science.gov (United States)

Caironi, Pietro; Latini, Roberto; Struck, Joachim; Hartmann, Oliver; Bergmann, Andreas; Maggio, Giuseppe; Cavana, Marco; Tognoni, Gianni; Pesenti, Antonio; Gattinoni, Luciano; Masson, Serge

2017-08-01

The biological role of adrenomedullin (ADM), a hormone involved in hemodynamic homeostasis, is controversial in sepsis because administration of either the peptide or an antibody against it may be beneficial. Plasma biologically active ADM (bio-ADM) was assessed on days 1, 2, and 7 after randomization of 956 patients with sepsis or septic shock to albumin or crystalloids for fluid resuscitation in the multicenter Albumin Italian Outcome Sepsis trial. We tested the association of bio-ADM and its time-dependent variation with fluid therapy, vasopressor administration, organ failures, and mortality. Plasma bio-ADM on day 1 (median [Q1-Q3], 110 [59-198] pg/mL) was higher in patients with septic shock, associated with 90-day mortality, multiple organ failures and the average extent of hemodynamic support therapy (fluids and vasopressors), and serum lactate time course over the first week. Moreover, it predicted incident cardiovascular dysfunction in patients without shock at enrollment (OR [95% CI], 1.9 [1.4-2.5]; P sepsis, the circulating, biologically active form of ADM may help individualizing hemodynamic support therapy, while avoiding harmful effects. Its possible pathophysiologic role makes bio-ADM a potential candidate for future targeted therapies. ClinicalTrials.gov; No.: NCT00707122. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
Technical note: Combining quantile forecasts and predictive distributions of streamflows

Science.gov (United States)

Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

2017-11-01

The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.
Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy.

Science.gov (United States)

Nazeri, Mona; Jusoff, Kamaruzaman; Madani, Nima; Mahmud, Ahmad Rodzi; Bahman, Abdul Rani; Kumar, Lalit

2012-01-01

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.
Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus distribution using maximum entropy.

Directory of Open Access Journals (Sweden)

Mona Nazeri

Full Text Available One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.
High-Lift Propeller Noise Prediction for a Distributed Electric Propulsion Flight Demonstrator

Science.gov (United States)

Nark, Douglas M.; Buning, Pieter G.; Jones, William T.; Derlaga, Joseph M.

2017-01-01

Over the past several years, the use of electric propulsion technologies within aircraft design has received increased attention. The characteristics of electric propulsion systems open up new areas of the aircraft design space, such as the use of distributed electric propulsion (DEP). In this approach, electric motors are placed in many different locations to achieve increased efficiency through integration of the propulsion system with the airframe. Under a project called Scalable Convergent Electric Propulsion Technology Operations Research (SCEPTOR), NASA is designing a flight demonstrator aircraft that employs many "high-lift propellers" distributed upstream of the wing leading edge and two cruise propellers (one at each wingtip). As the high-lift propellers are operational at low flight speeds (take-off/approach flight conditions), the impact of the DEP configuration on the aircraft noise signature is also an important design consideration. This paper describes efforts toward the development of a mulit-fidelity aerodynamic and acoustic methodology for DEP high-lift propeller aeroacoustic modeling. Specifically, the PAS, OVERFLOW 2, and FUN3D codes are used to predict the aerodynamic performance of a baseline high-lift propeller blade set. Blade surface pressure results from the aerodynamic predictions are then used with PSU-WOPWOP and the F1A module of the NASA second generation Aircraft NOise Prediction Program to predict the isolated high-lift propeller noise source. Comparisons of predictions indicate that general trends related to angle of attack effects at the blade passage frequency are captured well with the various codes. Results for higher harmonics of the blade passage frequency appear consistent for the CFD based methods. Conversely, evidence of the need for a study of the effects of increased azimuthal grid resolution on the PAS based results is indicated and will be pursued in future work. Overall, the results indicate that the computational
Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

Science.gov (United States)

Hickey, John M; Chiurugwi, Tinashe; Mackay, Ian; Powell, Wayne

2017-08-30

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
Surgery confounds biology: the predictive value of stage-, grade- and prostate-specific antigen for recurrence after radical prostatectomy as a function of surgeon experience.

Science.gov (United States)

Vickers, Andrew J; Savage, Caroline J; Bianco, Fernando J; Klein, Eric A; Kattan, Michael W; Secin, Fernando P; Guilloneau, Bertrand D; Scardino, Peter T

2011-04-01

Statistical models predicting cancer recurrence after surgery are based on biologic variables. We have shown previously that prostate cancer recurrence is related to both tumor biology and to surgical technique. Here, we evaluate the association between several biological predictors and biochemical recurrence across varying surgical experience. The study included two separate cohorts: 6,091 patients treated by open radical prostatectomy and an independent replication set of 2,298 patients treated laparoscopically. We calculated the odds ratios for biological predictors of biochemical recurrence-stage, Gleason grade and prostate-specific antigen (PSA)-and also the predictive accuracy (area under the curve, AUC) of a multivariable model, for subgroups of patients defined by the experience of their surgeon. In the open cohort, the odds ratio for Gleason score 8+ and advanced pathologic stage, though not PSA or Gleason score 7, increased dramatically when patients treated by surgeons with lower levels of experience were excluded (Gleason 8+: odds ratios 5.6 overall vs. 13.0 for patients treated by surgeons with 1,000+ prior cases; locally advanced disease: odds ratios of 6.6 vs. 12.2, respectively). The AUC of the multivariable model was 0.750 for patients treated by surgeons with 50 or fewer cases compared to 0.849 for patients treated by surgeons with 500 or more. Although predictiveness was lower overall for the independent replication set cohort, the main findings were replicated. Surgery confounds biology. Although our findings have no direct clinical implications, studies investigating biological variables as predictors of outcome after curative resection of cancer should consider the impact of surgeon-specific factors. Copyright © 2010 UICC.
Distributed Sensor Network for meteorological observations and numerical weather Prediction Calculations

Directory of Open Access Journals (Sweden)

Á. Vas

2013-06-01

Full Text Available The prediction of weather generally means the solution of differential equations on the base of the measured initial conditions where the data of close and distant neighboring points are used for the calculations. It requires the maintenance of expensive weather stations and supercomputers. However, if weather stations are not only capable of measuring but can also communicate with each other, then these smart sensors can also be applied to run forecasting calculations. This applies the highest possible level of parallelization without the collection of measured data into one place. Furthermore, if more nodes are involved, the result becomes more accurate, but the computing power required from one node does not increase. Our Distributed Sensor Network for meteorological sensing and numerical weather Prediction Calculations (DSN-PC can be applied in several different areas where sensing and numerical calculations, even the solution of differential equations, are needed.
Predicting the distribution of commercially important invertebrate stocks under future climate.

Directory of Open Access Journals (Sweden)

Bayden D Russell

Full Text Available The future management of commercially exploited species is challenging because techniques used to predict the future distribution of stocks under climate change are currently inadequate. We projected the future distribution and abundance of two commercially harvested abalone species (blacklip abalone, Haliotis rubra and greenlip abalone, H. laevigata inhabiting coastal South Australia, using multiple species distribution models (SDM and for decadal time slices through to 2100. Projections are based on two contrasting global greenhouse gas emissions scenarios. The SDMs identified August (winter Sea Surface Temperature (SST as the best descriptor of abundance and forecast that warming of winter temperatures under both scenarios may be beneficial to both species by allowing increased abundance and expansion into previously uninhabited coasts. This range expansion is unlikely to be realised, however, as projected warming of March SST is projected to exceed temperatures which cause up to 10-fold increases in juvenile mortality. By linking fine-resolution forecasts of sea surface temperature under different climate change scenarios to SDMs and physiological experiments, we provide a practical first approximation of the potential impact of climate-induced change on two species of marine invertebrates in the same fishery.
Evaluation of Airborne Remote Sensing Techniques for Predicting the Distribution of Energetic Compounds on Impact Areas

National Research Council Canada - National Science Library

Graves, Mark R; Dove, Linda P; Jenkins, Thomas F; Bigl, Susan; Walsh, Marianne E; Hewitt, Alan D; Lambert, Dennis; Perron, Nancy; Ramsey, Charles; Gamey, Jeff; Beard, Les; Doll, William E; Magoun, Dale

2007-01-01

.... Remote sensing and geographic information system (GIS) technologies were utilized to assist in the development of enhanced sampling strategies to better predict the landscape-scale distribution of energetic compounds...
Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores.

Science.gov (United States)

Bastien, Olivier; Maréchal, Eric

2008-08-07

Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2) following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory) is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure). Homologous sequences were considered as systems 1) having a high redundancy of information reflected by the magnitude of their alignment scores, 2) which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a constant rate, corresponding to the
Model predictions and analysis of enhanced biological effectiveness at low dose rates

International Nuclear Information System (INIS)

Watt, D.E.; Sykes, C.E.; Younis, A.-R.S.

1988-01-01

A severe challenge to all models purporting to describe the biological effects of ionizing radiation has arisen with the discovery of two phenomena: the anomalous trend with dose rate of the frequency of neoplastic transformation of mammalian cells and the apparent excessive damaging power of electron-capture radionuclides when incorporated into cell nuclei. A new model is proposed which predicts and enables interpretation of these phenomena. Radiation effectiveness is found to be expressible absolutely in terms of the geometrical cross-sectional area of the radiosensitive sites. The duration of the irradiation, the mean free path for ionization, the influence of particles in the slowing-down spectrum perrtaining in the medium and two collective time factors determining the mean repair rate and the mean lifetime of unidentified reactive chemical species [pt

DemQSAR: predicting human volume of distribution and clearance of drugs.

Science.gov (United States)

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is
Artificial neural network application for predicting soil distribution coefficient of nickel

International Nuclear Information System (INIS)

Falamaki, Amin

2013-01-01

The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in
Logic-based models in systems biology: a predictive and parameter-free network analysis method†

Science.gov (United States)

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820
The predictive skill of species distribution models for plankton in a changing climate

DEFF Research Database (Denmark)

Brun, Philipp Georg; Kiørboe, Thomas; Licandro, Priscilla

2016-01-01

Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real-world are rare. Here, we use data from the Continuous Plankton Recorder program, one...... null models, is essential to assess the robustness of projections of marine planktonic species under climate change...
An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

Science.gov (United States)

De Vore, Karl W; Fatahi, Nadia M; Sass, John E

2016-08-01

Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.
Two Dogmas of Biology

Directory of Open Access Journals (Sweden)

Leonore Fleming

2017-01-01

Full Text Available The problem with reductionism in biology is not the reduction, but the implicit attitude of determinism that usually accompanies it. Methodological reductionism is supported by deterministic beliefs, but making such a connection is problematic when it is based on an idea of determinism as fixed predictability. Conflating determinism with predictability gives rise to inaccurate models that overlook the dynamic complexity of our world, as well as ignore our epistemic limitations when we try to model it. Furthermore, the assumption of a strictly deterministic framework is unnecessarily hindering to biology. By removing the dogma of determinism, biological methods, including reductive methods, can be expanded to include stochastic models and probabilistic interpretations. Thus, the dogma of reductionism can be saved once its ties with determinism are severed. In this paper, I analyze two problems that have faced molecular biology for the last 50 years—protein folding and cancer. Both cases demonstrate the long influence of reductionism and determinism on molecular biology, as well as how abandoning determinism has opened the door to more probabilistic and unconstrained reductive methods in biology.
Mathematical biology

CERN Document Server

Murray, James D

1993-01-01

The book is a textbook (with many exercises) giving an in-depth account of the practical use of mathematical modelling in the biomedical sciences. The mathematical level required is generally not high and the emphasis is on what is required to solve the real biological problem. The subject matter is drawn, e.g. from population biology, reaction kinetics, biological oscillators and switches, Belousov-Zhabotinskii reaction, reaction-diffusion theory, biological wave phenomena, central pattern generators, neural models, spread of epidemics, mechanochemical theory of biological pattern formation and importance in evolution. Most of the models are based on real biological problems and the predictions and explanations offered as a direct result of mathematical analysis of the models are important aspects of the book. The aim is to provide a thorough training in practical mathematical biology and to show how exciting and novel mathematical challenges arise from a genuine interdisciplinary involvement with the biosci...
Use of ecological niche modeling as a tool for predicting the potential distribution of Microcystis sp (cyanobacteria in the Aguamilpa Dam, Nayarit, Mexico

Directory of Open Access Journals (Sweden)

Enrique Martinez-Meyer

2012-04-01

Full Text Available Ecological niche modeling is an important tool to evaluate the spatial distribution of terrestrial species, however, its applicability has been little explored in the aquatic environment. Microcystis sp., a species of cyanobacteria, is widely recognized for its ability to produce a group of toxins known as microcystins, which can cause death of animals as fish, birds and mammals depending on the amount of toxin absorbed. Like any taxonomic group, cyanobacteria has environmental thresholds, therefore, a suitable ecological niche will define their distribution. This study was conducted in Aguamilpa Hydroelectric Reservoir, an artificial ecosystem that started operations in 1994. In this system we evaluated the potential distribution of Microcystis sp., by generating a prediction model based on the concept of ecological niche MAXENT, using a Digital Elevation Model in cells of 100 m x 100 m (1 ha spatial resolution and monitoring eleven physicochemical and biological variables and nutrients in water. The distribution maps were developed using ArcMap 9.2®. The results indicated that Microcystis sp., is distributed mainly in the upper tributary basin (Huaynamota basin during the dry season. There was less chance to find cyanobacteria in the entire system during the cold dry season, while during the warm dry season cyanobacteria was recognized at the confluence of two rivers. During the rainfall season there were no reports of cyanobacteria presence. This species is often associated with arising trophic processes of anthropogenic origin; therefore, attention is required in specific areas that have been identified in this work to improve Aguamilpa’s watershed management and restoration. It was also recognized the importance of phosphorus and nitrogen interaction, which determines the distribution of Microcystis sp., in the Aguamilpa Reservoir. The results of this study demonstrated that ecological niche modeling was a suitable tool to assess the
Distributed Model Predictive Control for Active Power Control of Wind Farm

DEFF Research Database (Denmark)

Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard

2014-01-01

This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....
Biosecurity and Open-Source Biology: The Promise and Peril of Distributed Synthetic Biological Technologies.

Science.gov (United States)

Evans, Nicholas G; Selgelid, Michael J

2015-08-01

In this article, we raise ethical concerns about the potential misuse of open-source biology (OSB): biological research and development that progresses through an organisational model of radical openness, deskilling, and innovation. We compare this organisational structure to that of the open-source software model, and detail salient ethical implications of this model. We demonstrate that OSB, in virtue of its commitment to openness, may be resistant to governance attempts.
Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy

International Nuclear Information System (INIS)

Shiraishi, Satomi; Moore, Kevin L.

2016-01-01

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D clin − D pred . The mean (〈δD r 〉), standard deviation (σ δD r ), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r PTV ) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r PTV ∈ [ − 6, 30] mm. The average prediction error was less
Detailed analysis of inversions predicted between two human genomes: errors, real polymorphisms, and their origin and population distribution.

Science.gov (United States)

Vicente-Salvador, David; Puig, Marta; Gayà-Vidal, Magdalena; Pacheco, Sarai; Giner-Delgado, Carla; Noguera, Isaac; Izquierdo, David; Martínez-Fundichely, Alexander; Ruiz-Herrera, Aurora; Estivill, Xavier; Aguado, Cristina; Lucas-Lledó, José Ignacio; Cáceres, Mario

2017-02-01

The growing catalogue of structural variants in humans often overlooks inversions as one of the most difficult types of variation to study, even though they affect phenotypic traits in diverse organisms. Here, we have analysed in detail 90 inversions predicted from the comparison of two independently assembled human genomes: the reference genome (NCBI36/HG18) and HuRef. Surprisingly, we found that two thirds of these predictions (62) represent errors either in assembly comparison or in one of the assemblies, including 27 misassembled regions in HG18. Next, we validated 22 of the remaining 28 potential polymorphic inversions using different PCR techniques and characterized their breakpoints and ancestral state. In addition, we determined experimentally the derived allele frequency in Europeans for 17 inversions (DAF = 0.01-0.80), as well as the distribution in 14 worldwide populations for 12 of them based on the 1000 Genomes Project data. Among the validated inversions, nine have inverted repeats (IRs) at their breakpoints, and two show nucleotide variation patterns consistent with a recurrent origin. Conversely, inversions without IRs have a unique origin and almost all of them show deletions or insertions at the breakpoints in the derived allele mediated by microhomology sequences, which highlights the importance of mechanisms like FoSTeS/MMBIR in the generation of complex rearrangements in the human genome. Finally, we found several inversions located within genes and at least one candidate to be positively selected in Africa. Thus, our study emphasizes the importance of careful analysis and validation of large-scale genomic predictions to extract reliable biological conclusions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Study on the biological half-life and organ-distribution of tritiated lysine-vasopressin in Brattleboro rats

International Nuclear Information System (INIS)

Laczi, F.; Laszlo, F.A.; Keri, Gy.; Teplan, I.

1980-01-01

The biological half-life and organ-distribution of tritiated lysine-vasopressin were determined in R-Amsterdam rats, and in homozygous and heterozygous Brattleboro rats with hereditary central diabetes insipidus. It was found that the biological half-life of the tritiated lysin-vasopressin in the Brattleboro rats did not differ significantly from that found in the R-Amsterdam rats. The highest radioactivities were observed in the neuro- and adenohypophyses and in the kidneys of both the R-Amsterdam and the Brattleboro rats. The accumulation of tritiated LVP was higher in the small intestine of the Brattleboro rats than in that of the R-Amsterdam animals. The results have led to the conclusion that the accelerated elimination of vasopressin and its pathologic organ-accumulation are probably not involved in the water metabolism disturbance of Brattleboro rats with hereditary hypothalamic diabetes insipidus. (author)
The StreamCat Dataset: Accumulated Attributes for NHDPlusV2 Catchments (Version 2.1) for the Conterminous United States: Predicted Biological Condition

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset consists of predicted probabilities of good biological condition based in the US EPA 2008/2009 National Rivers and Streams Assessment (NRSA). NRSA...
Quantifying evenly distributed states in exclusion and nonexclusion processes

Science.gov (United States)

Binder, Benjamin J.; Landman, Kerry A.

2011-04-01

Spatial-point data sets, generated from a wide range of physical systems and mathematical models, can be analyzed by counting the number of objects in equally sized bins. We find that the bin counts are related to the Pólya distribution. New measures are developed which indicate whether or not a spatial data set, generated from an exclusion process, is at its most evenly distributed state, the complete spatial randomness (CSR) state. To this end, we define an index in terms of the variance between the bin counts. Limiting values of the index are determined when objects have access to the entire domain and when there are subregions of the domain that are inaccessible to objects. Using three case studies (Lagrangian fluid particles in chaotic laminar flows, cellular automata agents in discrete models, and biological cells within colonies), we calculate the indexes and verify that our theoretical CSR limit accurately predicts the state of the system. These measures should prove useful in many biological applications.
Predicting statistical properties of open reading frames in bacterial genomes.

Directory of Open Access Journals (Sweden)

Katharina Mir

Full Text Available An analytical model based on the statistical properties of Open Reading Frames (ORFs of eubacterial genomes such as codon composition and sequence length of all reading frames was developed. This new model predicts the average length, maximum length as well as the length distribution of the ORFs of 70 species with GC contents varying between 21% and 74%. Furthermore, the number of annotated genes is predicted with high accordance. However, the ORF length distribution in the five alternative reading frames shows interesting deviations from the predicted distribution. In particular, long ORFs appear more often than expected statistically. The unexpected depletion of stop codons in these alternative open reading frames cannot completely be explained by a biased codon usage in the +1 frame. While it is unknown if the stop codon depletion has a biological function, it could be due to a protein coding capacity of alternative ORFs exerting a selection pressure which prevents the fixation of stop codon mutations. The comparison of the analytical model with bacterial genomes, therefore, leads to a hypothesis suggesting novel gene candidates which can now be investigated in subsequent wet lab experiments.
Predictions of malaria vector distribution in Belize based on multispectral satellite data.

Science.gov (United States)

Roberts, D R; Paris, J F; Manguin, S; Harbach, R E; Woodruff, R; Rejmankova, E; Polanco, J; Wullschleger, B; Legters, L J

1996-03-01

Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.
Michael Levitt and Computational Biology

Science.gov (United States)

dropdown arrow Site Map A-Z Index Menu Synopsis Michael Levitt and Computational Biology Resources with Michael Levitt, PhD, professor of structural biology at the Stanford University School of Medicine, has function. ... Levitt's early work pioneered computational structural biology, which helped to predict
Imprecision in estimates of dose from ingested 137Cs due to variability in human biological characteristics

International Nuclear Information System (INIS)

Schwarz, G.; Dunning, D.E. Jr.

1982-01-01

An attempt has been made to quantify the variability in human biological parameters determining dose to man from ingestion of a unit activity of soluble 137 Cs and the resulting imprecision in the predicted total-body dose commitment. The analysis is based on an extensive review of the literature along with the application of statistical methods to determine parameter variability, correlations between parameters, and predictive imprecision. The variability in the principal biological parameters (biological half-time and total-body mass) involved can be described by a geometric standard deviation of 1.2-1.5 for adults and 1.6-1.9 for children/ adolescents of age 0.1-18 yr. The estimated predictive imprecision (using a Monte Carlo technique) in the total-body dose commitment from ingested 137 Cs can be described by a geometric standard deviation on the order of 1.3-1.4, meaning that the 99th percentile of the predicted distribution of dose is within approximately 2.1 times the mean value. The mean dose estimate is 0.009 Sv/MBq (34 mrem/μ Ci) for children/adolescents and 0.01 Sv/MBq (38 mrem/μ Ci) for adults. Little evidence of age dependence in the total-body dose from ingested 137 Cs is observed. (author)
The application of predictive modelling for determining bio-environmental factors affecting the distribution of blackflies (Diptera: Simuliidae) in the Gilgel Gibe watershed in Southwest Ethiopia.

Science.gov (United States)

Ambelu, Argaw; Mekonen, Seblework; Koch, Magaly; Addis, Taffere; Boets, Pieter; Everaert, Gert; Goethals, Peter

2014-01-01

Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies.

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

Science.gov (United States)

Stoll, Gautier; Viara, Eric; Barillot, Emmanuel; Calzone, Laurence

2012-08-29

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential
Prediction of fission mass-yield distributions based on cross section calculations

International Nuclear Information System (INIS)

Hambsch, F.-J.; G.Vladuca; Tudora, Anabella; Oberstedt, S.; Ruskov, I.

2005-01-01

For the first time, fission mass-yield distributions have been predicted based on an extended statistical model for fission cross section calculations. In this model, the concept of the multi-modality of the fission process has been incorporated. The three most dominant fission modes, the two asymmetric standard I (S1) and standard II (S2) modes and the symmetric superlong (SL) mode are taken into account. De-convoluted fission cross sections for S1, S2 and SL modes for 235,238 U(n, f) and 237 Np(n, f), based on experimental branching ratios, were calculated for the first time in the incident neutron energy range from 0.01 to 5.5 MeV providing good agreement with the experimental fission cross section data. The branching ratios obtained from the modal fission cross section calculations have been used to deduce the corresponding fission yield distributions, including mean values also for incident neutron energies hitherto not accessible to experiment
Maximizing the biological effect of proton dose delivered with scanned beams via inhomogeneous daily dose distributions

Energy Technology Data Exchange (ETDEWEB)

Zeng Chuan; Giantsoudi, Drosoula; Grassberger, Clemens; Goldberg, Saveli; Niemierko, Andrzej; Paganetti, Harald; Efstathiou, Jason A.; Trofimov, Alexei [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114 (United States)

2013-05-15

Purpose: Biological effect of radiation can be enhanced with hypofractionation, localized dose escalation, and, in particle therapy, with optimized distribution of linear energy transfer (LET). The authors describe a method to construct inhomogeneous fractional dose (IFD) distributions, and evaluate the potential gain in the therapeutic effect from their delivery in proton therapy delivered by pencil beam scanning. Methods: For 13 cases of prostate cancer, the authors considered hypofractionated courses of 60 Gy delivered in 20 fractions. (All doses denoted in Gy include the proton's mean relative biological effectiveness (RBE) of 1.1.) Two types of plans were optimized using two opposed lateral beams to deliver a uniform dose of 3 Gy per fraction to the target by scanning: (1) in conventional full-target plans (FTP), each beam irradiated the entire gland, (2) in split-target plans (STP), beams irradiated only the respective proximal hemispheres (prostate split sagittally). Inverse planning yielded intensity maps, in which discrete position control points of the scanned beam (spots) were assigned optimized intensity values. FTP plans preferentially required a higher intensity of spots in the distal part of the target, while STP, by design, employed proximal spots. To evaluate the utility of IFD delivery, IFD plans were generated by rearranging the spot intensities from FTP or STP intensity maps, separately as well as combined using a variety of mixing weights. IFD courses were designed so that, in alternating fractions, one of the hemispheres of the prostate would receive a dose boost and the other receive a lower dose, while the total physical dose from the IFD course was roughly uniform across the prostate. IFD plans were normalized so that the equivalent uniform dose (EUD) of rectum and bladder did not increase, compared to the baseline FTP plan, which irradiated the prostate uniformly in every fraction. An EUD-based model was then applied to estimate tumor
Maximizing the biological effect of proton dose delivered with scanned beams via inhomogeneous daily dose distributions

International Nuclear Information System (INIS)

Zeng Chuan; Giantsoudi, Drosoula; Grassberger, Clemens; Goldberg, Saveli; Niemierko, Andrzej; Paganetti, Harald; Efstathiou, Jason A.; Trofimov, Alexei

2013-01-01

Purpose: Biological effect of radiation can be enhanced with hypofractionation, localized dose escalation, and, in particle therapy, with optimized distribution of linear energy transfer (LET). The authors describe a method to construct inhomogeneous fractional dose (IFD) distributions, and evaluate the potential gain in the therapeutic effect from their delivery in proton therapy delivered by pencil beam scanning. Methods: For 13 cases of prostate cancer, the authors considered hypofractionated courses of 60 Gy delivered in 20 fractions. (All doses denoted in Gy include the proton's mean relative biological effectiveness (RBE) of 1.1.) Two types of plans were optimized using two opposed lateral beams to deliver a uniform dose of 3 Gy per fraction to the target by scanning: (1) in conventional full-target plans (FTP), each beam irradiated the entire gland, (2) in split-target plans (STP), beams irradiated only the respective proximal hemispheres (prostate split sagittally). Inverse planning yielded intensity maps, in which discrete position control points of the scanned beam (spots) were assigned optimized intensity values. FTP plans preferentially required a higher intensity of spots in the distal part of the target, while STP, by design, employed proximal spots. To evaluate the utility of IFD delivery, IFD plans were generated by rearranging the spot intensities from FTP or STP intensity maps, separately as well as combined using a variety of mixing weights. IFD courses were designed so that, in alternating fractions, one of the hemispheres of the prostate would receive a dose boost and the other receive a lower dose, while the total physical dose from the IFD course was roughly uniform across the prostate. IFD plans were normalized so that the equivalent uniform dose (EUD) of rectum and bladder did not increase, compared to the baseline FTP plan, which irradiated the prostate uniformly in every fraction. An EUD-based model was then applied to estimate tumor
Maximizing the biological effect of proton dose delivered with scanned beams via inhomogeneous daily dose distributions.

Science.gov (United States)

Zeng, Chuan; Giantsoudi, Drosoula; Grassberger, Clemens; Goldberg, Saveli; Niemierko, Andrzej; Paganetti, Harald; Efstathiou, Jason A; Trofimov, Alexei

2013-05-01

Biological effect of radiation can be enhanced with hypofractionation, localized dose escalation, and, in particle therapy, with optimized distribution of linear energy transfer (LET). The authors describe a method to construct inhomogeneous fractional dose (IFD) distributions, and evaluate the potential gain in the therapeutic effect from their delivery in proton therapy delivered by pencil beam scanning. For 13 cases of prostate cancer, the authors considered hypofractionated courses of 60 Gy delivered in 20 fractions. (All doses denoted in Gy include the proton's mean relative biological effectiveness (RBE) of 1.1.) Two types of plans were optimized using two opposed lateral beams to deliver a uniform dose of 3 Gy per fraction to the target by scanning: (1) in conventional full-target plans (FTP), each beam irradiated the entire gland, (2) in split-target plans (STP), beams irradiated only the respective proximal hemispheres (prostate split sagittally). Inverse planning yielded intensity maps, in which discrete position control points of the scanned beam (spots) were assigned optimized intensity values. FTP plans preferentially required a higher intensity of spots in the distal part of the target, while STP, by design, employed proximal spots. To evaluate the utility of IFD delivery, IFD plans were generated by rearranging the spot intensities from FTP or STP intensity maps, separately as well as combined using a variety of mixing weights. IFD courses were designed so that, in alternating fractions, one of the hemispheres of the prostate would receive a dose boost and the other receive a lower dose, while the total physical dose from the IFD course was roughly uniform across the prostate. IFD plans were normalized so that the equivalent uniform dose (EUD) of rectum and bladder did not increase, compared to the baseline FTP plan, which irradiated the prostate uniformly in every fraction. An EUD-based model was then applied to estimate tumor control probability
Development of a semi-automated method for subspecialty case distribution and prediction of intraoperative consultations in surgical pathology

Directory of Open Access Journals (Sweden)

Raul S Gonzalez

2015-01-01

Full Text Available Background: In many surgical pathology laboratories, operating room schedules are prospectively reviewed to determine specimen distribution to different subspecialty services and to predict the number and nature of potential intraoperative consultations for which prior medical records and slides require review. At our institution, such schedules were manually converted into easily interpretable, surgical pathology-friendly reports to facilitate these activities. This conversion, however, was time-consuming and arguably a non-value-added activity. Objective: Our goal was to develop a semi-automated method of generating these reports that improved their readability while taking less time to perform than the manual method. Materials and Methods: A dynamic Microsoft Excel workbook was developed to automatically convert published operating room schedules into different tabular formats. Based on the surgical procedure descriptions in the schedule, a list of linked keywords and phrases was utilized to sort cases by subspecialty and to predict potential intraoperative consultations. After two trial-and-optimization cycles, the method was incorporated into standard practice. Results: The workbook distributed cases to appropriate subspecialties and accurately predicted intraoperative requests. Users indicated that they spent 1-2 h fewer per day on this activity than before, and team members preferred the formatting of the newer reports. Comparison of the manual and semi-automatic predictions showed that the mean daily difference in predicted versus actual intraoperative consultations underwent no statistically significant changes before and after implementation for most subspecialties. Conclusions: A well-designed, lean, and simple information technology solution to determine subspecialty case distribution and prediction of intraoperative consultations in surgical pathology is approximately as accurate as the gold standard manual method and requires less
Predictable and Predictive Emotions:Explaining Cheap Signals and Trust Re-Extension

Directory of Open Access Journals (Sweden)

Eric eSchniter

2014-11-01

Full Text Available Despite normative predictions from economics and biology, unrelated strangers will often develop the trust necessary to reap gains from one-shot economic exchange opportunities. This appears to be especially true when declared intentions and emotions can be cheaply communicated. Perhaps even more puzzling to economists and biologists is the observation that anonymous and unrelated individuals, known to have breached trust, often make effective use of cheap signals, such as promises and apologies, to encourage trust re-extension. We used a pair of trust games with one-way communication and emotion surveys to investigate the role of emotions in regulating the propensity to message, apologize, re-extend trust, and demonstrate trustworthiness. This design allowed us to observe the endogenous emergence and natural distribution of trust-relevant behaviors, remedial strategies used by promise-breakers, their effects on behavior, and subsequent outcomes. We found that emotions triggered by interaction outcomes are predictable and also predict subsequent apology and trust re-extension. The role of emotions in behavioral regulation helps explain why messages are produced, when they can be trusted, and when trust will be re-extended.
Predictable and predictive emotions: explaining cheap signals and trust re-extension.

Science.gov (United States)

Schniter, Eric; Sheremeta, Roman M

2014-01-01

Despite normative predictions from economics and biology, unrelated strangers will often develop the trust necessary to reap gains from one-shot economic exchange opportunities. This appears to be especially true when declared intentions and emotions can be cheaply communicated. Perhaps even more puzzling to economists and biologists is the observation that anonymous and unrelated individuals, known to have breached trust, often make effective use of cheap signals, such as promises and apologies, to encourage trust re-extension. We used a pair of trust games with one-way communication and an emotion survey to investigate the role of emotions in regulating the propensity to message, apologize, re-extend trust, and demonstrate trustworthiness. This design allowed us to observe the endogenous emergence and natural distribution of trust-relevant behaviors, remedial strategies used by promise-breakers, their effects on behavior, and subsequent outcomes. We found that emotions triggered by interaction outcomes are predictable and also predict subsequent apology and trust re-extension. The role of emotions in behavioral regulation helps explain why messages are produced, when they can be trusted, and when trust will be re-extended.
Prediction of clearance, volume of distribution and half-life by allometric scaling and by use of plasma concentrations predicted from pharmacokinetic constants: a comparative study.

Science.gov (United States)

Mahmood, I

1999-08-01

Pharmacokinetic parameters (clearance, CL, volume of distribution in the central compartment, VdC, and elimination half-life, t1/2beta) predicted by an empirical allometric approach have been compared with parameters predicted from plasma concentrations calculated by use of the pharmacokinetic constants A, B, alpha and beta, where A and B are the intercepts on the Y axis of the plot of plasma concentration against time and alpha and beta are the rate constants, both pairs of constants being for the distribution and elimination phases, respectively. The pharmacokinetic parameters of cefpiramide, actisomide, troglitazone, procaterol, moxalactam and ciprofloxacin were scaled from animal data obtained from the literature. Three methods were used to generate plots for the prediction of clearance in man: dependence of clearance on body weight (simple allometric equation); dependence of the product of clearance and maximum life-span potential (MLP) on body weight; and dependence of the product of clearance and brain weight on body weight. Plasma concentrations of the drugs were predicted in man by use of A, B, alpha and beta obtained from animal data. The predicted plasma concentrations were then used to calculate CL, VdC and t1/2beta. The pharmacokinetic parameters predicted by use of both approaches were compared with measured values. The results indicate that simple allometry did not predict clearance satisfactorily for actisomide, troglitazone, procaterol and ciprofloxacin. Use of MLP or the product of clearance and brain weight improved the prediction of clearance for these four drugs. Except for troglitazone, VdC and t1/2beta predicted for man by use of the allometric approach were comparable with measured values for the drugs studied. CL, VdC and t1/2beta predicted by use of pharmacokinetic constants were comparable with values predicted by simple allometry. Thus, if simple allometry failed to predict clearance of a drug, so did the pharmacokinetic constant
Predicting plant invasions under climate change: are species distribution models validated by field trials?

Science.gov (United States)

Sheppard, Christine S; Burns, Bruce R; Stanley, Margaret C

2014-09-01

Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced
Human Papilloma Virus: Prevalence, distribution and predictive value to lymphatic metastasis in penile carcinoma

Directory of Open Access Journals (Sweden)

Aluizio Goncalves da Fonseca

2013-07-01

Full Text Available Objectives To evaluate the prevalence, distribution and association of HPV with histological pattern of worse prognosis of penile cancer, in order to evaluate its predictive value of inguinal metastasis, as well as evaluation of other previous reported prognostic factors. Material and Methods Tumor samples of 82 patients with penile carcinoma were tested in order to establish the prevalence and distribution of genotypic HPV using PCR. HPV status was correlated to histopathological factors and the presence of inguinal mestastasis. The influence of several histological characteristics was also correlated to inguinal disease-free survival. Results Follow-up varied from 1 to 71 months (median 22 months. HPV DNA was identified in 60.9% of sample, with higher prevalence of types 11 and 6 (64% and 32%, respectively. There was no significant correlation of the histological characteristics of worse prognosis of penile cancer with HPV status. Inguinal disease-free survival in 5 years did also not show HPV status influence (p = 0.45. The only independent pathologic factors of inguinal metastasis were: stage T ≥ T1b-T4 (p = 0.02, lymphovascular invasion (p = 0.04 and infiltrative invasion (p = 0.03. conclusions HPV status and distribution had shown no correlation with worse prognosis of histological aspects, or predictive value for lymphatic metastasis in penile carcinoma.
Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms.

Science.gov (United States)

Ripszam, M; Gallampois, C M J; Berglund, Å; Larsson, H; Andersson, A; Tysklind, M; Haglund, P

2015-06-01

Predicted consequences of future climate change in the northern Baltic Sea include increases in sea surface temperatures and terrestrial dissolved organic carbon (DOC) runoff. These changes are expected to alter environmental distribution of anthropogenic organic contaminants (OCs). To assess likely shifts in their distributions, outdoor mesocosms were employed to mimic pelagic ecosystems at two temperatures and two DOC concentrations, current: 15°C and 4 mg DOCL(-1) and, within ranges of predicted increases, 18°C and 6 mg DOCL(-1), respectively. Selected organic contaminants were added to the mesocosms to monitor changes in their distribution induced by the treatments. OC partitioning to particulate matter and sedimentation were enhanced at the higher DOC concentration, at both temperatures, while higher losses and lower partitioning of OCs to DOC were observed at the higher temperature. No combined effects of higher temperature and DOC on partitioning were observed, possibly because of the balancing nature of these processes. Therefore, changes in OCs' fates may largely depend on whether they are most sensitive to temperature or DOC concentration rises. Bromoanilines, phenanthrene, biphenyl and naphthalene were sensitive to the rise in DOC concentration, whereas organophosphates, chlorobenzenes (PCBz) and polychlorinated biphenyls (PCBs) were more sensitive to temperature. Mitotane and diflufenican were sensitive to both temperature and DOC concentration rises individually, but not in combination. Copyright © 2015 Elsevier B.V. All rights reserved.
Biological half-life and distribution of radiocesium in a contaminated population of green treefrogs Hyla cinerea

International Nuclear Information System (INIS)

Dapson, R.W.; Kaplan, L.

1975-01-01

Radiocesium content of adult male green treefrogs Hyla cinerea from a contaminated habitat is adequately described by a log normal distribution with mean 2.277 log 10 pCi g -1 dry wt (189.2 pCi g -1 ) and variance of 0.031. There was significant negative correlation of body burden with body length and weight (p 2 = 0.10). Biological half-life of radiocesium in unfed, captive frogs held at 20 deg - 30 deg C averaged 30.1 d. (author)
Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Shiraishi, Satomi; Moore, Kevin L., E-mail: kevinmoore@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California 92093 (United States)

2016-01-15

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D{sub clin} − D{sub pred}. The mean (〈δD{sub r}〉), standard deviation (σ{sub δD{sub r}}), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r{sub PTV}) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r{sub PTV} ∈ [ − 6, 30] mm. The
pH prediction by artificial neural networks for the drinking water of the distribution system of Hyderabad city

International Nuclear Information System (INIS)

Memon, N.A.; Unar, M.A.; Ansari, A.K.

2012-01-01

In this research, feed forward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm. The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%. Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results. (author)
Experimental Validation of Energy Resources Integration in Microgrids via Distributed Predictive Control

DEFF Research Database (Denmark)

Mantovani, Giancarlo; Costanzo, Giuseppe Tommaso; Marinelli, Mattia

2014-01-01

This paper presents an innovative control scheme for the management of energy consumption in commercial build- ings with local energy production, such as photovoltaic panels or wind turbine and an energy storage unit. The presented scheme is based on distributed model predictive controllers, which...... sources, a vanadium redox battery system, resistive load, and a point of common coupling to the national grid. Several experiments are carried to assess the performance of the control scheme in managing local energy pro- duction and consumption....
Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

Directory of Open Access Journals (Sweden)

Ching-Tai Chen

Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted
Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Science.gov (United States)

Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with
Robust Distributed Model Predictive Load Frequency Control of Interconnected Power System

Directory of Open Access Journals (Sweden)

Xiangjie Liu

2013-01-01

Full Text Available Considering the load frequency control (LFC of large-scale power system, a robust distributed model predictive control (RDMPC is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results show good dynamic response and robustness in the presence of power system dynamic uncertainties.
Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

Science.gov (United States)

Zhao, Meng; Ding, Baocang

2015-03-01

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data

Science.gov (United States)

Feng, Shou; Fu, Ping; Zheng, Wenbin

2018-03-01

Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is usually needed. This paper proposed a novel preliminary results processing method called the nodes interaction method. The nodes interaction method revises the preliminary results and guarantees that the predictions are consistent with the hierarchy constraint. This method exploits the label dependency and considers the hierarchical interaction between nodes when making decisions based on the Bayesian network in its first phase. In the second phase, this method further adjusts the results according to the hierarchy constraint. Implementing the nodes interaction method in the HMC framework also enhances the HMC performance for solving the gene function prediction problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph that is more difficult to tackle. The experimental results validate the promising performance of the proposed method compared to state-of-the-art methods on eight benchmark yeast data sets annotated by the GO.
Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

Science.gov (United States)

Germaine, Stephen S.; Ignizio, Drew; Keinath, Doug; Copeland, Holly

2014-01-01

Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an
Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores

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Maréchal Eric

2008-08-01

Full Text Available Abstract Background Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2 following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. Results We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure. Homologous sequences were considered as systems 1 having a high redundancy of information reflected by the magnitude of their alignment scores, 2 which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a
Predictive analytics for truck arrival time estimation : a field study at a European distribution center

NARCIS (Netherlands)

van der Spoel, Sjoerd; Amrit, Chintan Amrit; van Hillegersberg, Jos

2017-01-01

Distribution centres (DCs) are the hubs connecting transport streams in the supply chain. The synchronisation of coming and going cargo at a DC requires reliable arrival times. To achieve this, a reliable method to predict arrival times is needed. A literature review was performed to find the
Predicting potential global distributions of two Miscanthus grasses: implications for horticulture, biofuel production, and biological invasions.

Science.gov (United States)

Hager, Heather A; Sinasac, Sarah E; Gedalof, Ze'ev; Newman, Jonathan A

2014-01-01

In many regions, large proportions of the naturalized and invasive non-native floras were originally introduced deliberately by humans. Pest risk assessments are now used in many jurisdictions to regulate the importation of species and usually include an estimation of the potential distribution in the import area. Two species of Asian grass (Miscanthus sacchariflorus and M. sinensis) that were originally introduced to North America as ornamental plants have since escaped cultivation. These species and their hybrid offspring are now receiving attention for large-scale production as biofuel crops in North America and elsewhere. We evaluated their potential global climate suitability for cultivation and potential invasion using the niche model CLIMEX and evaluated the models' sensitivity to the parameter values. We then compared the sensitivity of projections of future climatically suitable area under two climate models and two emissions scenarios. The models indicate that the species have been introduced to most of the potential global climatically suitable areas in the northern but not the southern hemisphere. The more narrowly distributed species (M. sacchariflorus) is more sensitive to changes in model parameters, which could have implications for modelling species of conservation concern. Climate projections indicate likely contractions in potential range in the south, but expansions in the north, particularly in introduced areas where biomass production trials are under way. Climate sensitivity analysis shows that projections differ more between the selected climate change models than between the selected emissions scenarios. Local-scale assessments are required to overlay suitable habitat with climate projections to estimate areas of cultivation potential and invasion risk.
Ventilation versus biology: What is the controlling mechanism of nitrous oxide distribution in the North Atlantic?

Science.gov (United States)

Paz, Mercedes; García-Ibáñez, Maribel I.; Steinfeldt, Reiner; Ríos, Aida F.; Pérez, Fiz F.

2017-04-01

The extent to which water mass mixing and ocean ventilation contribute to nitrous oxide (N2O) distribution at the scale of oceanic basins is poorly constrained. We used novel N2O and chlorofluorocarbon measurements along with multiparameter water mass analysis to evaluate the impact of water mass mixing and Atlantic Meridional Overturning Circulation (AMOC) on N2O distribution along the Observatoire de la variabilité interannuelle et décennale en Atlantique Nord (OVIDE) section, extending from Portugal to Greenland. The biological N2O production has a stronger impact on the observed N2O concentrations in the water masses traveling northward in the upper limb of the AMOC than those in recently ventilated cold water masses in the lower limb, where N2O concentrations reflect the colder temperatures. The high N2O tongue, with concentrations as high as 16 nmol kg-1, propagates above the isopycnal surface delimiting the upper and lower AMOC limbs, which extends from the eastern North Atlantic Basin to the Iceland Basin and coincides with the maximum N2O production rates. Water mixing and basin-scale remineralization account for 72% of variation in the observed distribution of N2O. The mixing-corrected stoichiometric ratio N2O:O2 for the North Atlantic Basin of 0.06 nmol/μmol is in agreement with ratios of N2O:O2 for local N2O anomalies, suggesting than up to 28% of N2O production occurs in the temperate and subpolar Atlantic, an overlooked region for N2O cycling. Overall, our results highlight the importance of taking into account mixing, O2 undersaturation when water masses are formed and the increasing atmospheric N2O concentrations when parameterizing N2O:O2 and biological N2O production in the global oceans.
Combining public participatory surveillance and occupancy modelling to predict the distributional response of Ixodes scapularis to climate change.

Science.gov (United States)

Lieske, David J; Lloyd, Vett K

2018-03-01

Ixodes scapularis, a known vector of Borrelia burgdorferi sensu stricto (Bbss), is undergoing range expansion in many parts of Canada. The province of New Brunswick, which borders jurisdictions with established populations of I. scapularis, constitutes a range expansion zone for this species. To better understand the current and potential future distribution of this tick under climate change projections, this study applied occupancy modelling to distributional records of adult ticks that successfully overwintered, obtained through passive surveillance. This study indicates that I. scapularis occurs throughout the southern-most portion of the province, in close proximity to coastlines and major waterways. Milder winter conditions, as indicated by the number of degree days model with a predictive accuracy of 0.845 (range: 0.828-0.893). Both RCP 4.5 and RCP 8.5 climate projections predict that a significant proportion of the province (roughly a quarter to a third) will be highly suitable for I. scapularis by the 2080s. Comparison with cases of canine infection show good spatial agreement with baseline model predictions, but the presence of canine Borrelia infections beyond the climate envelope, defined by the highest probabilities of tick occurrence, suggest the presence of Bbss-carrying ticks distributed by long-range dispersal events. This research demonstrates that predictive statistical modelling of multi-year surveillance information is an efficient way to identify areas where I. scapularis is most likely to occur, and can be used to guide subsequent active sampling efforts in order to better understand fine scale species distributional patterns. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.
Historical freshwater fish ecology: a long-term view of distribution changes and biological invasions

Directory of Open Access Journals (Sweden)

Miguel Clavero

2015-12-01

Full Text Available Past processes and events may have an important influence on contemporaneous ecological patterns, including current human impacts on landscapes and organisms. In spite of that, most of the ecological knowledge has been built upon short-term studies, which very rarely exceed one decade. Ecology and Conservation Biology have an important lack of historical approaches, a deficiency that may become a hindrance for the management of natural systems. In this talk I will present examples of how historical information on the distribution of freshwater fish and other aquatic organisms can be used to address ecological questions. Most analyses are based on two important Spanish historical written sources: the Relaciones de Felipe II (16th century and the Madoz Dictionary (19th century. The examples considered include the European eel (Anguilla anguilla, the brown trout (Salmo trutta, the common carp (Cyprinus carpio and the white clawed crayfish (Austropotamobius italicus, among other species, as well as questions related to biological invasions, habitat loss and the impacts of global warming. The outputs of ecological research based on historical data often become useful tools for present-day biodiversity conservation planning and actions.
Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table.

Science.gov (United States)

Koseki, Shigenobu; Isobe, Seiichiro

2005-10-25

The growth of pathogenic bacteria Escherichia coli O157:H7, Salmonella spp., and Listeria monocytogenes on iceberg lettuce under constant and fluctuating temperatures was modelled in order to estimate the microbial safety of this vegetable during distribution from the farm to the table. Firstly, we examined pathogen growth on lettuce at constant temperatures, ranging from 5 to 25 degrees C, and then we obtained the growth kinetic parameters (lag time, maximum growth rate (micro(max)), and maximum population density (MPD)) using the Baranyi primary growth model. The parameters were similar to those predicted by the pathogen modelling program (PMP), with the exception of MPD. The MPD of each pathogen on lettuce was 2-4 log(10) CFU/g lower than that predicted by PMP. Furthermore, the MPD of pathogens decreased with decreasing temperature. The relationship between mu(max) and temperature was linear in accordance with Ratkowsky secondary model as was the relationship between the MPD and temperature. Predictions of pathogen growth under fluctuating temperature used the Baranyi primary microbial growth model along with the Ratkowsky secondary model and MPD equation. The fluctuating temperature profile used in this study was the real temperature history measured during distribution from the field at harvesting to the retail store. Overall predictions for each pathogen agreed well with observed viable counts in most cases. The bias and root mean square error (RMSE) of the prediction were small. The prediction in which mu(max) was based on PMP showed a trend of overestimation relative to prediction based on lettuce. However, the prediction concerning E. coli O157:H7 and Salmonella spp. on lettuce greatly overestimated growth in the case of a temperature history starting relatively high, such as 25 degrees C for 5 h. In contrast, the overall prediction of L. monocytogenes under the same circumstances agreed with the observed data.
Predictive geochemical mapping using environmental correlation

International Nuclear Information System (INIS)

Wilford, John; Caritat, Patrice de; Bui, Elisabeth

2016-01-01

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time. Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km 2 ), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations. Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation
Trait-based representation of biological nitrification: Model development, testing, and predicted community composition

Directory of Open Access Journals (Sweden)

Nick eBouskill

2012-10-01

Full Text Available Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an ‘organism’ in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait focused on nitrification (MicroTrait-N that represents the ammonia-oxidizing bacteria (AOB and ammonia-oxidizing archaea (AOA and nitrite oxidizing bacteria (NOB using traits related to enzyme kinetics and physiological properties. We used this model to predict nitrifier diversity, ammonia (NH3 oxidation rates and nitrous oxide (N2O production across pH, temperature and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over six month simulations is maximized in a system where the relationship between AOB and NOB is maintained. When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models.
Fostering synergy between cell biology and systems biology.

Science.gov (United States)

Eddy, James A; Funk, Cory C; Price, Nathan D

2015-08-01

In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding. In turn, incorporating domain expertise and experimental data is crucial for building towards whole cell models. An iterative cycle of interaction between cell and systems biologists advances the goals of both fields and establishes a framework for mechanistic understanding of the genome-to-phenome relationship. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.
Predicting Brain Metastasis in Breast Cancer Patients: Stage Versus Biology.

Science.gov (United States)

Azim, Hamdy A; Abdel-Malek, Raafat; Kassem, Loay

2018-04-01

Brain metastasis (BM) is a life-threatening event in breast cancer patients. Identifying patients at a high risk for BM can help to adopt screening programs and test preventive interventions. We tried to identify the incidence of BM in different stages and subtypes of breast cancer. We reviewed the clinical records of 2193 consecutive breast cancer patients who presented between January 1999 and December 2010. We explored the incidence of BM in relation to standard clinicopathological factors, and determined the cumulative risk of BM according to the disease stage and phenotype. Of the 2193 included women, 160 (7.3%) developed BM at a median follow-up of 5.8 years. Age younger than 60 years (P = .015), larger tumors (P = .004), lymph node (LN) positivity (P < .001), high tumor grade (P = .012), and HER2 positivity (P < .001) were associated with higher incidence of BM in the whole population. In patients who presented with locoregional disease, 3 factors independently predicted BM: large tumors (hazard ratio [HR], 3.60; 95% confidence interval [CI], 1.54-8.38; P = .003), axillary LN metastasis (HR, 4.03; 95% CI, 1.91-8.52; P < .001), and HER2 positivity (HR, 1.89; 95% CI, 1.0-3.41; P = .049). A Brain Relapse Index was formulated using those 3 factors, with 5-year cumulative incidence of BM of 19.2% in those having the 2 or 3 risk factors versus 2.5% in those with no or 1 risk factor (P < .001). In metastatic patients, 3 factors were associated with higher risk of BM: HER2 positivity (P = .007), shorter relapse-free interval (P < .001), and lung metastasis (P < .001). Disease stage and biological subtypes predict the risk for BM and subsequent treatment outcome. Copyright © 2017 Elsevier Inc. All rights reserved.
[Predictive distribution and planting GAP of Cyathula officinalis in China based on 3S technology and MaxEnt modelling].

Science.gov (United States)

Wu, Ming-Yan; He, Lan; Chen, Jia-Li; Dong, Guang; Cheng, Wu-Xue

2017-11-01

Research on predictive distribution and planting GAP of Cyathula officinalis in China is helpful to provide scientific basis for its protection and planting popularization. According to the data in 63 distribution sites and 49 ecological variables, using MaxEnt ecological niche model and 3S technology, we performed a quantitative analysis of suitable distribution and planting GAP of C. officinalis in China. Our results show that: ① the area of suitable distribution of C. officinalis is about 634 385.80 km² in total, and mainly in Northeastern and Southeastern Sichuan, Northern and Southeastern Yunnan, Western and Southwestern Guizhou, Southwestern and Northeastern Chongqing, Southwestern Shaanxi, Southeastern Gansu, Western Guangxi, Southeastern Tibet. ② The main ecological factors determining the potential distribution are precipitation, altitude, minimum temperature of coldest month, soil type, monthly mean temperature. ③ The planting GAP region are mainly in Guangyuan, Mianyang, Ya'an, Leshan, Liangshan, Panzhihua of Sichuan province, Hanzhong of Shaanxi province, Dali, Nujiang, Chuxiong, Baoshan, Qujing, Wenshan of Yunnan province, southwestern autonomous prefecture in Guizhou province. The results are of great significance for realizing the growth environment, predicting the potential distribution and promoting planting popularization for C. officinalis. Copyright© by the Chinese Pharmaceutical Association.
Biological couplings: Classification and characteristic rules

Institute of Scientific and Technical Information of China (English)

无

2009-01-01

The phenomena that biological functions originate from biological coupling are the important biological foundation of multiple bionics and the significant discoveries in the bionic fields. In this paper, the basic concepts related to biological coupling are introduced from the bionic viewpoint. Constitution, classification and characteristic rules of biological coupling are illuminated, the general modes of biological coupling studies are analyzed, and the prospects of multi-coupling bionics are predicted.
Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different

Directory of Open Access Journals (Sweden)

Keisuke Yano

2014-05-01

Full Text Available We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the Fisher information matrix for the data and the Fisher information matrix for the target variables. We assume that the trace has a unique maximum point with respect to the parameter. We construct asymptotically constant-risk Bayesian predictive densities using a prior depending on the sample size. Further, we apply the theory to the subminimax estimator problem and the prediction based on the binary regression model.
Comparison of experimental pulse-height distributions in germanium detectors with integrated-tiger-series-code predictions

International Nuclear Information System (INIS)

Beutler, D.E.; Halbleib, J.A.; Knott, D.P.

1989-01-01

This paper reports pulse-height distributions in two different types of Ge detectors measured for a variety of medium-energy x-ray bremsstrahlung spectra. These measurements have been compared to predictions using the integrated tiger series (ITS) Monte Carlo electron/photon transport code. In general, the authors find excellent agreement between experiments and predictions using no free parameters. These results demonstrate that the ITS codes can predict the combined bremsstrahlung production and energy deposition with good precision (within measurement uncertainties). The one region of disagreement observed occurs for low-energy (<50 keV) photons using low-energy bremsstrahlung spectra. In this case the ITS codes appear to underestimate the produced and/or absorbed radiation by almost an order of magnitude
Chloridrate of N-isopropyl-p-iodoamphetamine labeled with Iodine-131. Biological distribution in laboratory animals

International Nuclear Information System (INIS)

Colturato, Maria Tereza; Muramoto, Emiko; Carvalho, Olga Goncalves de

2000-01-01

The development of this work was based on a great interest from the medical class in the utilization of chloridrate of N-isopropyl-p-iodoamphetamine (IMP) labeled with 123 I, for brain perfusion evaluation. Studies were performed to optimize the labeling parameters of IMP with 131 I using nucleophilic substitution: temperature and, time reaction, ascorbic acid mass, pH and relation IMP mass/radioiodo activity, and stability of the final product. Radiochemistry purity method used showed to be efficient, quick and of easily handling for routine production. Biological distribution studies were performed in mice to determine the percent administered dose in the blood, different organs and whole body after intravenous administration of the radiopharmaceutical. The product crossed the intact blood brain barrier, allowing a follow up of further studies after the intravenous administration of the radiopharmaceutical. The principal elimination route 131 I-IMP was the urinary. Based on the results from radiochemical purity, stability and biological behavior in laboratory animals, we concluded that the studied radiopharmaceutical presents all ideal characteristics for clinical use in brain studies in nuclear medicine. (author)
Characterizing short-term stability for Boolean networks over any distribution of transfer functions

International Nuclear Information System (INIS)

Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.

2016-01-01

Here we present a characterization of short-term stability of random Boolean networks under arbitrary distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. Finally, it has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.
Single amino acid substitution in important hemoglobinopathies does not disturb molecular function and biological process

Directory of Open Access Journals (Sweden)

Viroj Wiwanitkit

2008-06-01

Full Text Available Viroj WiwanitkitDepartment of Laboratory Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandAbstract: Hemoglobin is an important protein found in the red cells of many animals. In humans, the hemoglobin is mainly distributed in the red blood cell. Single amino acid substitution is the main pathogenesis of most hemoglobin disorders. Here, the author used a new gene ontology technology to predict the molecular function and biological process of four important hemoglobin disorders with single substitution. The four studied important abnormal hemoglobins (Hb with single substitution included Hb S, Hb E, Hb C, and Hb J-Baltimore. Using the GoFigure server, the molecular function and biological process in normal and abnormal hemoglobins was predicted. Compared with normal hemoglobin, all studied abnormal hemoglobins had the same function and biological process. This indicated that the overall function of oxygen transportation is not disturbed in the studied hemoglobin disorders. Clinical findings of oxygen depletion in abnormal hemoglobin should therefore be due to the other processes rather than genomics, proteomics, and expression levels.Keywords: hemoglobin, amino acid, substitution, function

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

Science.gov (United States)

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.
A Random Forest Approach to Predict the Spatial Distribution of Sediment Pollution in an Estuarine System

Science.gov (United States)

Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment cont...
Light-flavor sea-quark distributions in the nucleon in the SU(3) chiral quark soliton model. I. Phenomenological predictions

International Nuclear Information System (INIS)

Wakamatsu, M.

2003-01-01

Theoretical predictions are given for the light-flavor sea-quark distributions in the nucleon including the strange quark ones on the basis of the flavor SU(3) version of the chiral quark soliton model. Careful account is taken of the SU(3) symmetry breaking effects due to the mass difference Δm s between the strange and nonstrange quarks, which is the only one parameter necessary for the flavor SU(3) generalization of the model. A particular emphasis of study is put on the light-flavor sea-quark asymmetry as exemplified by the observables d-bar(x)-u-bar(x),d-bar(x)/u-bar(x),Δu-bar(x)-Δd-bar(x) as well as on the particle-antiparticle asymmetry of the strange quark distributions represented by s(x)-s-bar(x),s(x)/s-bar(x),Δs(x)-Δs-bar(x) etc. As for the unpolarized sea-quark distributions, the predictions of the model seem qualitatively consistent with the available phenomenological information provided by the NMC data for d-bar(x)-u-bar(x), the E866 data for d-bar(x)/u-bar(x), the CCFR data and the fit of Barone et al. for s(x)/s-bar(x), etc. The model is shown to give several unique predictions also for the spin-dependent sea-quark distribution, such that Δs(x)<<Δs-bar(x) < or approx. 0 and Δd-bar(x)<0<Δu-bar(x), although the verification of these predictions must await more elaborate experimental investigations in the near future
A two-stage predictive model to simultaneous control of trihalomethanes in water treatment plants and distribution systems: adaptability to treatment processes.

Science.gov (United States)

Domínguez-Tello, Antonio; Arias-Borrego, Ana; García-Barrera, Tamara; Gómez-Ariza, José Luis

2017-10-01

The trihalomethanes (TTHMs) and others disinfection by-products (DBPs) are formed in drinking water by the reaction of chlorine with organic precursors contained in the source water, in two consecutive and linked stages, that starts at the treatment plant and continues in second stage along the distribution system (DS) by reaction of residual chlorine with organic precursors not removed. Following this approach, this study aimed at developing a two-stage empirical model for predicting the formation of TTHMs in the water treatment plant and subsequently their evolution along the water distribution system (WDS). The aim of the two-stage model was to improve the predictive capability for a wide range of scenarios of water treatments and distribution systems. The two-stage model was developed using multiple regression analysis from a database (January 2007 to July 2012) using three different treatment processes (conventional and advanced) in the water supply system of Aljaraque area (southwest of Spain). Then, the new model was validated using a recent database from the same water supply system (January 2011 to May 2015). The validation results indicated no significant difference in the predictive and observed values of TTHM (R 2 0.874, analytical variance distribution systems studied, proving the adaptability of the new model to the boundary conditions. Finally the predictive capability of the new model was compared with 17 other models selected from the literature, showing satisfactory results prediction and excellent adaptability to treatment processes.
Relations between intuitive biological thinking and biological misconceptions in biology majors and nonmajors.

Science.gov (United States)

Coley, John D; Tanner, Kimberly

2015-03-02

Research and theory development in cognitive psychology and science education research remain largely isolated. Biology education researchers have documented persistent scientifically inaccurate ideas, often termed misconceptions, among biology students across biological domains. In parallel, cognitive and developmental psychologists have described intuitive conceptual systems--teleological, essentialist, and anthropocentric thinking--that humans use to reason about biology. We hypothesize that seemingly unrelated biological misconceptions may have common origins in these intuitive ways of knowing, termed cognitive construals. We presented 137 undergraduate biology majors and nonmajors with six biological misconceptions. They indicated their agreement with each statement, and explained their rationale for their response. Results indicate frequent agreement with misconceptions, and frequent use of construal-based reasoning among both biology majors and nonmajors in their written explanations. Moreover, results also show associations between specific construals and the misconceptions hypothesized to arise from those construals. Strikingly, such associations were stronger among biology majors than nonmajors. These results demonstrate important linkages between intuitive ways of thinking and misconceptions in discipline-based reasoning, and raise questions about the origins, persistence, and generality of relations between intuitive reasoning and biological misconceptions. © 2015 J. D. Coley and K. Tanner. CBE—Life Sciences Education © 2015 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models

Science.gov (United States)

Chunco, Amanda J.; Phimmachak, Somphouthone; Sivongxay, Niane; Stuart, Bryan L.

2013-01-01

The Lao newt (Laotriton laoensis) is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC) values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation) in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed. PMID:23555808
A model of litter size distribution in cattle.

Science.gov (United States)

Bennett, G L; Echternkamp, S E; Gregory, K E

1998-07-01

Genetic increases in twinning of cattle could result in increased frequency of triplet or higher-order births. There are no estimates of the incidence of triplets in populations with genetic levels of twinning over 40% because these populations either have not existed or have not been documented. A model of the distribution of litter size in cattle is proposed. Empirical estimates of ovulation rate distribution in sheep were combined with biological hypotheses about the fate of embryos in cattle. Two phases of embryo loss were hypothesized. The first phase is considered to be preimplantation. Losses in this phase occur independently (i.e., the loss of one embryo does not affect the loss of the remaining embryos). The second phase occurs after implantation. The loss of one embryo in this stage results in the loss of all embryos. Fewer than 5% triplet births are predicted when 50% of births are twins and triplets. Above 60% multiple births, increased triplets accounted for most of the increase in litter size. Predictions were compared with data from 5,142 calvings by 14 groups of heifers and cows with average litter sizes ranging from 1.14 to 1.36 calves. The predicted number of triplets was not significantly different (chi2 = 16.85, df = 14) from the observed number. The model also predicted differences in conception rates. A cow ovulating two ova was predicted to have the highest conception rate in a single breeding cycle. As mean ovulation rate increased, predicted conception to one breeding cycle increased. Conception to two or three breeding cycles decreased as mean ovulation increased because late-pregnancy failures increased. An alternative model of the fate of ova in cattle based on embryo and uterine competency predicts very similar proportions of singles, twins, and triplets but different conception rates. The proposed model of litter size distribution in cattle accurately predicts the proportion of triplets found in cattle with genetically high twinning
Measurements and predictions of the air distribution systems in high compute density (Internet) data centers

Energy Technology Data Exchange (ETDEWEB)

Cho, Jinkyun [HIMEC (Hanil Mechanical Electrical Consultants) Ltd., Seoul 150-103 (Korea); Department of Architectural Engineering, Yonsei University, Seoul 120-749 (Korea); Lim, Taesub; Kim, Byungseon Sean [Department of Architectural Engineering, Yonsei University, Seoul 120-749 (Korea)

2009-10-15

When equipment power density increases, a critical goal of a data center cooling system is to separate the equipment exhaust air from the equipment intake air in order to prevent the IT server from overheating. Cooling systems for data centers are primarily differentiated according to the way they distribute air. The six combinations of flooded and locally ducted air distribution make up the vast majority of all installations, except fully ducted air distribution methods. Once the air distribution system (ADS) is selected, there are other elements that must be integrated into the system design. In this research, the design parameters and IT environmental aspects of the cooling system were studied with a high heat density data center. CFD simulation analysis was carried out in order to compare the heat removal efficiencies of various air distribution systems. The IT environment of an actual operating data center is measured to validate a model for predicting the effect of different air distribution systems. A method for planning and design of the appropriate air distribution system is described. IT professionals versed in precision air distribution mechanisms, components, and configurations can work more effectively with mechanical engineers to ensure the specification and design of optimized cooling solutions. (author)
Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)

KAUST Repository

Chefaoui, Rosa M.

2015-05-05

Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. Here, we predict the realized and potential distribution for the species Cymodocea nodosa modelling its environmental niche in the Mediterranean and adjacent Atlantic coastlines. We use a combination of environmental variables and landscape metrics to perform a suite of predictive algorithms which enables examination of the niche and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 °C to 26.4 °C and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We found potential suitable areas not occupied by the seagrass mainly in coastal regions of North Africa and the Adriatic coast of Italy. The present study describes the realized and potential distribution of a seagrass species, providing the first global model of the factors that can be shaping the environmental niche of C. nodosa throughout its range. We identified the variables constraining its distribution as well as thresholds delineating its environmental niche. Landscape metrics showed promising prospects for the prediction of coastal species dependent on the shape of the coast. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not
Large-Scale Prediction of Seagrass Distribution Integrating Landscape Metrics and Environmental Factors: The Case of Cymodocea nodosa (Mediterranean–Atlantic)

KAUST Repository

Chefaoui, Rosa M.; Assis, Jorge; Duarte, Carlos M.; Serrã o, Ester A.

2015-01-01

Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. Here, we predict the realized and potential distribution for the species Cymodocea nodosa modelling its environmental niche in the Mediterranean and adjacent Atlantic coastlines. We use a combination of environmental variables and landscape metrics to perform a suite of predictive algorithms which enables examination of the niche and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 °C to 26.4 °C and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We found potential suitable areas not occupied by the seagrass mainly in coastal regions of North Africa and the Adriatic coast of Italy. The present study describes the realized and potential distribution of a seagrass species, providing the first global model of the factors that can be shaping the environmental niche of C. nodosa throughout its range. We identified the variables constraining its distribution as well as thresholds delineating its environmental niche. Landscape metrics showed promising prospects for the prediction of coastal species dependent on the shape of the coast. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not
Northward shifts of the distributions of Spanish reptiles in association with climate change.

Science.gov (United States)

Moreno-Rueda, Gregorio; Pleguezuelos, Juan M; Pizarro, Manuel; Montori, Albert

2012-04-01

It is predicted that climate change will drive extinctions of some reptiles and that the number of these extinctions will depend on whether reptiles are able to change their distribution. Whether the latitudinal distribution of reptiles may change in response to increases in temperature is unknown. We used data on reptile distributions collected during the 20th century to analyze whether changes in the distributions of reptiles in Spain are associated with increases in temperature. We controlled for biases in sampling effort and found a mean, statistically significant, northward shift of the northern extent of reptile distributions of about 15.2 km from 1940-1975 to 1991-2005. The southern extent of the distributions did not change significantly. Thus, our results suggest that the latitudinal distributions of reptiles may be changing in response to climate change. ©2011 Society for Conservation Biology.
Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

Directory of Open Access Journals (Sweden)

Hyun-Seob Song

Full Text Available Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.
Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra

Energy Technology Data Exchange (ETDEWEB)

Kamp, Florian [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States); Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, München (Germany); Physik-Department, Technische Universität München, Garching (Germany); Cabal, Gonzalo [Experimental Physics–Medical Physics, Ludwig Maximilians University Munich, Garching (Germany); Mairani, Andrea [Medical Physics Unit, Centro Nazionale Adroterapia Oncologica (CNAO), Pavia (Italy); Heidelberg Ion-Beam Therapy Center, Heidelberg (Germany); Parodi, Katia [Experimental Physics–Medical Physics, Ludwig Maximilians University Munich, Garching (Germany); Wilkens, Jan J. [Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, München (Germany); Physik-Department, Technische Universität München, Garching (Germany); Carlson, David J., E-mail: david.j.carlson@yale.edu [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States)

2015-11-01

Purpose: The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Methods and Materials: Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damage simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. Results: We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β){sub X} = 2 Gy. Conclusions: These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from
Biology and air–sea gas exchange controls on the distribution of carbon isotope ratios (δ13C in the ocean

Directory of Open Access Journals (Sweden)

A. Schmittner

2013-09-01

Full Text Available Analysis of observations and sensitivity experiments with a new three-dimensional global model of stable carbon isotope cycling elucidate processes that control the distribution of δ13C of dissolved inorganic carbon (DIC in the contemporary and preindustrial ocean. Biological fractionation and the sinking of isotopically light δ13C organic matter from the surface into the interior ocean leads to low δ13CDIC values at depths and in high latitude surface waters and high values in the upper ocean at low latitudes with maxima in the subtropics. Air–sea gas exchange has two effects. First, it acts to reduce the spatial gradients created by biology. Second, the associated temperature-dependent fractionation tends to increase (decrease δ13CDIC values of colder (warmer water, which generates gradients that oppose those arising from biology. Our model results suggest that both effects are similarly important in influencing surface and interior δ13CDIC distributions. However, since air–sea gas exchange is slow in the modern ocean, the biological effect dominates spatial δ13CDIC gradients both in the interior and at the surface, in contrast to conclusions from some previous studies. Calcium carbonate cycling, pH dependency of fractionation during air–sea gas exchange, and kinetic fractionation have minor effects on δ13CDIC. Accumulation of isotopically light carbon from anthropogenic fossil fuel burning has decreased the spatial variability of surface and deep δ13CDIC since the industrial revolution in our model simulations. Analysis of a new synthesis of δ13CDIC measurements from years 1990 to 2005 is used to quantify preformed and remineralized contributions as well as the effects of biology and air–sea gas exchange. The model reproduces major features of the observed large-scale distribution of δ13CDIC as well as the individual contributions and effects. Residual misfits are documented and analyzed. Simulated surface and subsurface �
Prediction of druggable proteins using machine learning and systems biology: a mini-review

Directory of Open Access Journals (Sweden)

Gaurav eKandoi

2015-12-01

Full Text Available The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.
Comments on the Redshift Distribution of 44,200 SDSS Quasars: Evidence for Predicted Preferred Redshifts?

OpenAIRE

Bell, M. B.

2004-01-01

A Sloan Digital Sky Survey (SDSS) source sample containing 44,200 quasar redshifts is examined. Although arguments have been put forth to explain some of the structure observed in the redshift distribution, it is argued here that this structure may just as easily be explained by the presence of previously predicted preferred redshifts.
Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Directory of Open Access Journals (Sweden)

Guisan Antoine

2009-04-01

Full Text Available Abstract Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a real absences b pseudo-absences selected randomly from the background and c two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97, and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have
Hardware-in-the-Loop Simulation of a Distribution System with Air Conditioners under Model Predictive Control: Preprint

Energy Technology Data Exchange (ETDEWEB)

Sparn, Bethany F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ruth, Mark F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnamurthy, Dheepak [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Pratt, Annabelle [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lunacek, Monte S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jones, Wesley B [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Wu, Hongyu [Kansas State University; Mittal, Saurabh [Mitre Corporation; Marks, Jesse [University of Missouri

2017-08-01

Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.
Elemental distribution and sample integrity comparison of freeze-dried and frozen-hydrated biological tissue samples with nuclear microprobe

Energy Technology Data Exchange (ETDEWEB)

Vavpetič, P., E-mail: primoz.vavpetic@ijs.si [Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana (Slovenia); Vogel-Mikuš, K. [Biotechnical Faculty, Department of Biology, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana (Slovenia); Jeromel, L. [Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana (Slovenia); Ogrinc Potočnik, N. [Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana (Slovenia); FOM-Institute AMOLF, Science Park 104, 1098 XG Amsterdam (Netherlands); Pongrac, P. [Biotechnical Faculty, Department of Biology, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana (Slovenia); Department of Plant Physiology, University of Bayreuth, Universitätstr. 30, 95447 Bayreuth (Germany); Drobne, D.; Pipan Tkalec, Ž.; Novak, S.; Kos, M.; Koren, Š.; Regvar, M. [Biotechnical Faculty, Department of Biology, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana (Slovenia); Pelicon, P. [Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana (Slovenia)

2015-04-01

The analysis of biological samples in frozen-hydrated state with micro-PIXE technique at Jožef Stefan Institute (JSI) nuclear microprobe has matured to a point that enables us to measure and examine frozen tissue samples routinely as a standard research method. Cryotome-cut slice of frozen-hydrated biological sample is mounted between two thin foils and positioned on the sample holder. The temperature of the cold stage in the measuring chamber is kept below 130 K throughout the insertion of the samples and the proton beam exposure. Matrix composition of frozen-hydrated tissue is consisted mostly of ice. Sample deterioration during proton beam exposure is monitored during the experiment, as both Elastic Backscattering Spectrometry (EBS) and Scanning Transmission Ion Microscopy (STIM) in on–off axis geometry are recorded together with the events in two PIXE detectors and backscattered ions from the chopper in a single list-mode file. The aim of this experiment was to determine differences and similarities between two kinds of biological sample preparation techniques for micro-PIXE analysis, namely freeze-drying and frozen-hydrated sample preparation in order to evaluate the improvements in the elemental localisation of the latter technique if any. In the presented work, a standard micro-PIXE configuration for tissue mapping at JSI was used with five detection systems operating in parallel, with proton beam cross section of 1.0 × 1.0 μm{sup 2} and a beam current of 100 pA. The comparison of the resulting elemental distributions measured at the biological tissue prepared in the frozen-hydrated and in the freeze-dried state revealed differences in elemental distribution of particular elements at the cellular level due to the morphology alteration in particular tissue compartments induced either by water removal in the lyophilisation process or by unsatisfactory preparation of samples for cutting and mounting during the shock-freezing phase of sample preparation.
Resource selection models are useful in predicting fine-scale distributions of black-footed ferrets in prairie dog colonies

Science.gov (United States)

Eads, David A.; Jachowski, David S.; Biggins, Dean E.; Livieri, Travis M.; Matchett, Marc R.; Millspaugh, Joshua J.

2012-01-01

Wildlife-habitat relationships are often conceptualized as resource selection functions (RSFs)—models increasingly used to estimate species distributions and prioritize habitat conservation. We evaluated the predictive capabilities of 2 black-footed ferret (Mustela nigripes) RSFs developed on a 452-ha colony of black-tailed prairie dogs (Cynomys ludovicianus) in the Conata Basin, South Dakota. We used the RSFs to project the relative probability of occurrence of ferrets throughout an adjacent 227-ha colony. We evaluated performance of the RSFs using ferret space use data collected via postbreeding spotlight surveys June–October 2005–2006. In home ranges and core areas, ferrets selected the predicted "very high" and "high" occurrence categories of both RSFs. Count metrics also suggested selection of these categories; for each model in each year, approximately 81% of ferret locations occurred in areas of very high or high predicted occurrence. These results suggest usefulness of the RSFs in estimating the distribution of ferrets throughout a black-tailed prairie dog colony. The RSFs provide a fine-scale habitat assessment for ferrets that can be used to prioritize releases of ferrets and habitat restoration for prairie dogs and ferrets. A method to quickly inventory the distribution of prairie dog burrow openings would greatly facilitate application of the RSFs.

The global distribution of deep-water Antipatharia habitat

Science.gov (United States)

Yesson, Chris; Bedford, Faye; Rogers, Alex D.; Taylor, Michelle L.

2017-11-01

Antipatharia are a diverse group of corals with many species found in deep water. Many Antipatharia are habitat for associates, have extreme longevity and some species can occur beyond 8500 m depth. As they are major constituents of'coral gardens', which are Vulnerable Marine Ecosystems (VMEs), knowledge of their distribution and environmental requirements is an important pre-requisite for informed conservation planning particularly where the expense and difficulty of deep-sea sampling prohibits comprehensive surveys. This study uses a global database of Antipatharia distribution data to perform habitat suitability modelling using the Maxent methodology to estimate the global extent of black coral habitat suitability. The model of habitat suitability is driven by temperature but there is notable influence from other variables of topography, surface productivity and oxygen levels. This model can be used to predict areas of suitable habitat, which can be useful for conservation planning. The global distribution of Antipatharia habitat suitability shows a marked contrast with the distribution of specimen observations, indicating that many potentially suitable areas have not been sampled, and that sampling effort has been disproportionate to shallow, accessible areas inside marine protected areas (MPAs). Although 25% of Antipatharia observations are located in MPAs, only 7-8% of predicted suitable habitat is protected, which is short of the Convention on Biological Diversity target to protect 10% of ocean habitats by 2020.
Three-dimensional fuel pin model validation by prediction of hydrogen distribution in cladding and comparison with experiment

Energy Technology Data Exchange (ETDEWEB)

Aly, A. [North Carolina State Univ., Raleigh, NC (United States); Avramova, Maria [North Carolina State Univ., Raleigh, NC (United States); Ivanov, Kostadin [Pennsylvania State Univ., University Park, PA (United States); Motta, Arthur [Pennsylvania State Univ., University Park, PA (United States); Lacroix, E. [Pennsylvania State Univ., University Park, PA (United States); Manera, Annalisa [Univ. of Michigan, Ann Arbor, MI (United States); Walter, D. [Univ. of Michigan, Ann Arbor, MI (United States); Williamson, R. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Gamble, K. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2017-10-29

To correctly describe and predict this hydrogen distribution there is a need for multi-physics coupling to provide accurate three-dimensional azimuthal, radial, and axial temperature distributions in the cladding. Coupled high-fidelity reactor-physics codes with a sub-channel code as well as with a computational fluid dynamics (CFD) tool have been used to calculate detailed temperature distributions. These high-fidelity coupled neutronics/thermal-hydraulics code systems are coupled further with the fuel-performance BISON code with a kernel (module) for hydrogen. Both hydrogen migration and precipitation/dissolution are included in the model. Results from this multi-physics analysis is validated utilizing calculations of hydrogen distribution using models informed by data from hydrogen experiments and PIE data.
Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

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Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.
submitter Variable RBE in proton therapy: comparison of different model predictions and their influence on clinical-like scenarios

CERN Document Server

Giovannini, Giulia; Cabal, Gonzalo; Bauer, Julia; Tessonnier, Thomas; Frey, Kathrin; Debus, Jürgen; Mairani, Andrea; Parodi, Katia

2016-01-01

Background: In proton radiation therapy a constant relative biological effectiveness (RBE) of 1.1 is usually assumed. However, biological experiments have evidenced RBE dependencies on dose level, proton linear energy transfer (LET) and tissue type. This work compares the predictions of three of the main radio-biological models proposed in the literature by Carabe-Fernandez, Wedenberg, Scholz and coworkers. Methods: Using the chosen models, a spread-out Bragg peak (SOBP) as well as two exemplary clinical cases (single field and two fields) for cranial proton irradiation, all delivered with state-of-the-art pencil-beam scanning, have been analyzed in terms of absorbed dose, dose-averaged LET $(LET_D)$, RBE-weighted dose $(D_{RBE})$ and biological range shift distributions. Results: In the systematic comparison of RBE predictions by the three models we could show different levels of agreement depending on $(α/β) x$ and LET values. The SOBP study emphasizes the variation of LET D and RBE not only as a functi...
Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

International Nuclear Information System (INIS)

Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-01-01

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society
Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

Energy Technology Data Exchange (ETDEWEB)

Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-09-18

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.
Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores

Science.gov (United States)

Thompson, E. David; Bowling, Bethany V.; Markle, Ross E.

2018-02-01

Studies over the last 30 years have considered various factors related to student success in introductory biology courses. While much of the available literature suggests that the best predictors of success in a college course are prior college grade point average (GPA) and class attendance, faculty often require a valuable predictor of success in those courses wherein the majority of students are in the first semester and have no previous record of college GPA or attendance. In this study, we evaluated the efficacy of the ACT Mathematics subject exam and Lawson's Classroom Test of Scientific Reasoning in predicting success in a major's introductory biology course. A logistic regression was utilized to determine the effectiveness of a combination of scientific reasoning (SR) scores and ACT math (ACT-M) scores to predict student success. In summary, we found that the model—with both SR and ACT-M as significant predictors—could be an effective predictor of student success and thus could potentially be useful in practical decision making for the course, such as directing students to support services at an early point in the semester.
2012 best practices for repositories collection, storage, retrieval, and distribution of biological materials for research international society for biological and environmental repositories.

Science.gov (United States)

2012-04-01

Third Edition [Formula: see text] [Box: see text] Printed with permission from the International Society for Biological and Environmental Repositories (ISBER) © 2011 ISBER All Rights Reserved Editor-in-Chief Lori D. Campbell, PhD Associate Editors Fay Betsou, PhD Debra Leiolani Garcia, MPA Judith G. Giri, PhD Karen E. Pitt, PhD Rebecca S. Pugh, MS Katherine C. Sexton, MBA Amy P.N. Skubitz, PhD Stella B. Somiari, PhD Individual Contributors to the Third Edition Jonas Astrin, Susan Baker, Thomas J. Barr, Erica Benson, Mark Cada, Lori Campbell, Antonio Hugo Jose Froes Marques Campos, David Carpentieri, Omoshile Clement, Domenico Coppola, Yvonne De Souza, Paul Fearn, Kelly Feil, Debra Garcia, Judith Giri, William E. Grizzle, Kathleen Groover, Keith Harding, Edward Kaercher, Joseph Kessler, Sarah Loud, Hannah Maynor, Kevin McCluskey, Kevin Meagher, Cheryl Michels, Lisa Miranda, Judy Muller-Cohn, Rolf Muller, James O'Sullivan, Karen Pitt, Rebecca Pugh, Rivka Ravid, Katherine Sexton, Ricardo Luis A. Silva, Frank Simione, Amy Skubitz, Stella Somiari, Frans van der Horst, Gavin Welch, Andy Zaayenga 2012 Best Practices for Repositories: Collection, Storage, Retrieval and Distribution of Biological Materials for Research INTERNATIONAL SOCIETY FOR BIOLOGICAL AND ENVIRONMENTAL REPOSITORIES (ISBER) INTRODUCTION T he availability of high quality biological and environmental specimens for research purposes requires the development of standardized methods for collection, long-term storage, retrieval and distribution of specimens that will enable their future use. Sharing successful strategies for accomplishing this goal is one of the driving forces for the International Society for Biological and Environmental Repositories (ISBER). For more information about ISBER see www.isber.org . ISBER's Best Practices for Repositories (Best Practices) reflect the collective experience of its members and has received broad input from other repository professionals. Throughout this document
Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India

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Malay Pramanik

Full Text Available In recent years, climate change has become a major threat and has been widely documented in the geographic distribution of many plant species. However, the impacts of climate change on the distribution of ecologically vulnerable medicinal species remain largely unknown. The identification of a suitable habitat for a species under climate change scenario is a significant step towards the mitigation of biodiversity decline. The study, therefore, aims to predict the impact of current, and future climatic scenarios on the distribution of the threatened Garcinia indica across the northern Western Ghats using Maximum Entropy (MaxEnt modelling. The future projections were made for the year 2050 and 2070 with all Representative Concentration Pathways (RCPs scenario (2.6, 4.5, 6.0, and 8.5 using 56 species occurrence data, and 19 bioclimatic predictors from the BCC-CSM1.1 model of the Intergovernmental Panel for Climate Change’s (IPCC 5th assessment. The bioclimatic variables were minimised to a smaller number of variables after a multicollinearity test, and their contributions were assessed using jackknife test. The AUC value of 0.956 ± 0.023 indicates that the model performs with excellent accuracy. The study identified that temperature seasonality (39.5 ± 3.1%, isothermality (19.2 ± 1.6%, and annual precipitation (12.7 ± 1.7% would be the major influencing variables in the current and future distribution. The model predicted 10.50% (19318.7 sq. km of the study area as moderately to very highly suitable, while 82.60% (151904 sq. km of the study area was identified as ‘unsuitable’ or ‘very low suitable’. Our predictions of Climate change impact on habitat suitability suggest that there will be a drastic reduction in the suitability by 5.29% and 5.69% under RCP 8.5 for 2050 and 2070, respectively. Objective and Significance: Primary objective of this study is to identify the potential distribution of medicinally and
Past, present and future distributions of an Iberian Endemic, Lepus granatensis: ecological and evolutionary clues from species distribution models.

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Pelayo Acevedo

Full Text Available The application of species distribution models (SDMs in ecology and conservation biology is increasing and assuming an important role, mainly because they can be used to hindcast past and predict current and future species distributions. However, the accuracy of SDMs depends on the quality of the data and on appropriate theoretical frameworks. In this study, comprehensive data on the current distribution of the Iberian hare (Lepus granatensis were used to i determine the species' ecogeographical constraints, ii hindcast a climatic model for the last glacial maximum (LGM, relating it to inferences derived from molecular studies, and iii calibrate a model to assess the species future distribution trends (up to 2080. Our results showed that the climatic factor (in its pure effect and when it is combined with the land-cover factor is the most important descriptor of the current distribution of the Iberian hare. In addition, the model's output was a reliable index of the local probability of species occurrence, which is a valuable tool to guide species management decisions and conservation planning. Climatic potential obtained for the LGM was combined with molecular data and the results suggest that several glacial refugia may have existed for the species within the major Iberian refugium. Finally, a high probability of occurrence of the Iberian hare in the current species range and a northward expansion were predicted for future. Given its current environmental envelope and evolutionary history, we discuss the macroecology of the Iberian hare and its sensitivity to climate change.
Past, present and future distributions of an Iberian Endemic, Lepus granatensis: ecological and evolutionary clues from species distribution models.

Science.gov (United States)

Acevedo, Pelayo; Melo-Ferreira, José; Real, Raimundo; Alves, Paulo Célio

2012-01-01

The application of species distribution models (SDMs) in ecology and conservation biology is increasing and assuming an important role, mainly because they can be used to hindcast past and predict current and future species distributions. However, the accuracy of SDMs depends on the quality of the data and on appropriate theoretical frameworks. In this study, comprehensive data on the current distribution of the Iberian hare (Lepus granatensis) were used to i) determine the species' ecogeographical constraints, ii) hindcast a climatic model for the last glacial maximum (LGM), relating it to inferences derived from molecular studies, and iii) calibrate a model to assess the species future distribution trends (up to 2080). Our results showed that the climatic factor (in its pure effect and when it is combined with the land-cover factor) is the most important descriptor of the current distribution of the Iberian hare. In addition, the model's output was a reliable index of the local probability of species occurrence, which is a valuable tool to guide species management decisions and conservation planning. Climatic potential obtained for the LGM was combined with molecular data and the results suggest that several glacial refugia may have existed for the species within the major Iberian refugium. Finally, a high probability of occurrence of the Iberian hare in the current species range and a northward expansion were predicted for future. Given its current environmental envelope and evolutionary history, we discuss the macroecology of the Iberian hare and its sensitivity to climate change.
Uncertainties of predictions from parton distributions 1, experimental errors

CERN Document Server

Martin, A D; Stirling, William James; Thorne, R S; CERN. Geneva

2003-01-01

We determine the uncertainties on observables arising from the errors on the experimental data that are fitted in the global MRST2001 parton analysis. By diagonalizing the error matrix we produce sets of partons suitable for use within the framework of linear propagation of errors, which is the most convenient method for calculating the uncertainties. Despite the potential limitations of this approach we find that it can be made to work well in practice. This is confirmed by our alternative approach of using the more rigorous Lagrange multiplier method to determine the errors on physical quantities directly. As particular examples we determine the uncertainties on the predictions of the charged-current deep-inelastic structure functions, on the cross-sections for W production and for Higgs boson production via gluon--gluon fusion at the Tevatron and the LHC, on the ratio of W-minus to W-plus production at the LHC and on the moments of the non-singlet quark distributions. We discuss the corresponding uncertain...
Topographic Metric Predictions of Soil redistribution and Organic Carbon Distribution in Croplands

Science.gov (United States)

Mccarty, G.; Li, X.

2017-12-01

Landscape topography is a key factor controlling soil redistribution and soil organic carbon (SOC) distribution in Iowa croplands (USA). In this study, we adopted a combined approach based on carbon () and cesium (137Cs) isotope tracers, and digital terrain analysis to understand patterns of SOC redistribution and carbon sequestration dynamics as influenced by landscape topography in tilled cropland under long term corn/soybean management. The fallout radionuclide 137Cs was used to estimate soil redistribution rates and a Lidar-derived DEM was used to obtain a set of topographic metrics for digital terrain analysis. Soil redistribution rates and patterns of SOC distribution were examined across 560 sampling locations at two field sites as well as at larger scale within the watershed. We used δ13C content in SOC to partition C3 and C4 plant derived C density at 127 locations in one of the two field sites with corn being the primary source of C4 C. Topography-based models were developed to simulate SOC distribution and soil redistribution using stepwise ordinary least square regression (SOLSR) and stepwise principal component regression (SPCR). All topography-based models developed through SPCR and SOLSR demonstrated good simulation performance, explaining more than 62% variability in SOC density and soil redistribution rates across two field sites with intensive samplings. However, the SOLSR models showed lower reliability than the SPCR models in predicting SOC density at the watershed scale. Spatial patterns of C3-derived SOC density were highly related to those of SOC density. Topographic metrics exerted substantial influence on C3-derived SOC density with the SPCR model accounting for 76.5% of the spatial variance. In contrast C4 derived SOC density had poor spatial structure likely reflecting the substantial contribution of corn vegetation to recently sequestered SOC density. Results of this study highlighted the utility of topographic SPCR models for scaling
Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling.

Directory of Open Access Journals (Sweden)

Hannah Slater

Full Text Available Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF, in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.
Effect of sub-pore scale morphology of biological deposits on porous media flow properties

Science.gov (United States)

Ghezzehei, T. A.

2012-12-01

Biological deposits often influence fluid flow by altering the pore space morphology and related hydrologic properties such as porosity, water retention characteristics, and permeability. In most coupled-processes models changes in porosity are inferred from biological process models using mass-balance. The corresponding evolution of permeability is estimated using (semi-) empirical porosity-permeability functions such as the Kozeny-Carman equation or power-law functions. These equations typically do not account for the heterogeneous spatial distribution and morphological irregularities of the deposits. As a result, predictions of permeability evolution are generally unsatisfactory. In this presentation, we demonstrate the significance of pore-scale deposit distribution on porosity-permeability relations using high resolution simulations of fluid flow through a single pore interspersed with deposits of varying morphologies. Based on these simulations, we present a modification to the Kozeny-Carman model that accounts for the shape of the deposits. Limited comparison with published experimental data suggests the plausibility of the proposed conceptual model.
Computer prediction of biological activity of 2-methyl(phenyl-6,9-epoksybenzo[g]quinoline-4,5,10-Trion and 5-methyl-(1,2,4-triazolo[4,3-a] quinoline

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Yu. V. Karpenko

2015-04-01

Full Text Available , One of the priority measures, which are evaluated in the creation of new effective medicines, is their high selective effect and lack of side effects. A considerable interest is the possibility of combining several structures of heterocycles in one molecule, such as quinoline and furan, which may cause an increase in biological activity of these combined compounds or an emergence of new properties. It is known that derivatives of 1,2,4-triazolo[4,3-a]quinoline have anticonvulsant effect and treat the syndrome of disorders with the nervous system. The aim of research. The main purpose of this work was an establishment of combinatorial library of bioregulators, which combines structures 5,8-dioksoquinoline and furan (1-8, quinoline and triazole (9-12, using computer program PASS (Prediction Activitity Spectra for Substances. Virtual screening of heterocycles derivatives was conducted to determine the direction of their bioactivity researches. Materials and methods. The virtual screening of compounds was performed by using the computer program PASS (Prediction Activity Spectra for Substances. For the specific activity the increase of Pa quantity and the decrease of the Pi quantity, helps to get the greater chance to detect this activity in the experiment. Predicting the probability of substance manifestation of specific types of biological activity determines which tests are the most appropriate for studying the biological activity of specific chemical substances and which substances of those that are available to the researcher likely will show the desired effect. With theoretical prediction the most likely basic structures of new compounds with desired biological effect, which best suits the task will be selected. Results. Analysis of computer prediction demonstrates the promising search of antineoplastic, antibiotic, analgesic and other types of activity in some of these compounds. An important instant of prediction of these substances is
Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain.

Science.gov (United States)

Gálvez, Rosa; Musella, Vicenzo; Descalzo, Miguel A; Montoya, Ana; Checa, Rocío; Marino, Valentina; Martín, Oihane; Cringoli, Giuseppe; Rinaldi, Laura; Miró, Guadalupe

2017-09-19

The cat flea, Ctenocephalides felis, is the most prevalent flea species detected on dogs and cats in Europe and other world regions. The status of flea infestation today is an evident public health concern because of their cosmopolitan distribution and the flea-borne diseases transmission. This study determines the spatial distribution of the cat flea C. felis infesting dogs in Spain. Using geospatial tools, models were constructed based on entomological data collected from dogs during the period 2013-2015. Bioclimatic zones, covering broad climate and vegetation ranges, were surveyed in relation to their size. The models builded were obtained by negative binomial regression of several environmental variables to show impacts on C. felis infestation prevalence: land cover, bioclimatic zone, mean summer and autumn temperature, mean summer rainfall, distance to urban settlement and normalized difference vegetation index. In the face of climate change, we also simulated the future distributions of C. felis for the global climate model (GCM) "GFDL-CM3" and for the representative concentration pathway RCP45, which predicts their spread in the country. Predictive models for current climate conditions indicated the widespread distribution of C. felis throughout Spain, mainly across the central northernmost zone of the mainland. Under predicted conditions of climate change, the risk of spread was slightly greater, especially in the north and central peninsula, than for the current situation. The data provided will be useful for local veterinarians to design effective strategies against flea infestation and the pathogens transmitted by these arthropods.
A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon's blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology.

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Haiyu Xu

Full Text Available Traditional Chinese medicine (TCM is one of the oldest East Asian medical systems. The present study adopted a systems biology-based approach to provide new insights relating to the active constituents and molecular mechanisms underlying the effects of dragon's blood (DB tablets for the treatment of colitis. This study integrated chemical analysis, prediction of absorption, distribution, metabolism, and excretion (ADME, and network pharmacology. Firstly, a rapid, reliable, and accurate ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was employed to identify 48 components of DB tablets. In silico prediction of the passive absorption of these compounds, based on Caco-2 cell permeability, and their P450 metabolism enabled the identification of 22 potentially absorbed components and 8 metabolites. Finally, networks were constructed to analyze interactions between these DB components/metabolites absorbed and their putative targets, and between the putative DB targets and known therapeutic targets for colitis. This study provided a great opportunity to deepen the understanding of the complex pharmacological mechanisms underlying the effects of DB in colitis treatment.
Gauss-Kronrod-Trapezoidal Integration Scheme for Modeling Biological Tissues with Continuous Fiber Distributions

Science.gov (United States)

Hou, Chieh; Ateshian, Gerard A.

2015-01-01

Fibrous biological tissues may be modeled using a continuous fiber distribution (CFD) to capture tension-compression nonlinearity, anisotropic fiber distributions, and load-induced anisotropy. The CFD framework requires spherical integration of weighted individual fiber responses, with fibers contributing to the stress response only when they are in tension. The common method for performing this integration employs the discretization of the unit sphere into a polyhedron with nearly uniform triangular faces (finite element integration or FEI scheme). Although FEI has proven to be more accurate and efficient than integration using spherical coordinates, it presents three major drawbacks: First, the number of elements on the unit sphere needed to achieve satisfactory accuracy becomes a significant computational cost in a finite element analysis. Second, fibers may not be in tension in some regions on the unit sphere, where the integration becomes a waste. Third, if tensed fiber bundles span a small region compared to the area of the elements on the sphere, a significant discretization error arises. This study presents an integration scheme specialized to the CFD framework, which significantly mitigates the first drawback of the FEI scheme, while eliminating the second and third completely. Here, integration is performed only over the regions of the unit sphere where fibers are in tension. Gauss-Kronrod quadrature is used across latitudes and the trapezoidal scheme across longitudes. Over a wide range of strain states, fiber material properties, and fiber angular distributions, results demonstrate that this new scheme always outperforms FEI, sometimes by orders of magnitude in the number of computational steps and relative accuracy of the stress calculation. PMID:26291492
Predicting tensorial electrophoretic effects in asymmetric colloids

Science.gov (United States)

Mowitz, Aaron J.; Witten, T. A.

2017-12-01

We formulate a numerical method for predicting the tensorial linear response of a rigid, asymmetrically charged body to an applied electric field. This prediction requires calculating the response of the fluid to the Stokes drag forces on the moving body and on the countercharges near its surface. To determine the fluid's motion, we represent both the body and the countercharges using many point sources of drag known as Stokeslets. Finding the correct flow field amounts to finding the set of drag forces on the Stokeslets that is consistent with the relative velocities experienced by each Stokeslet. The method rigorously satisfies the condition that the object moves with no transfer of momentum to the fluid. We demonstrate that a sphere represented by 1999 well-separated Stokeslets on its surface produces flow and drag force like a solid sphere to 1% accuracy. We show that a uniformly charged sphere with 3998 body and countercharge Stokeslets obeys the Smoluchowski prediction [F. Morrison, J. Colloid Interface Sci. 34, 210 (1970), 10.1016/0021-9797(70)90171-2] for electrophoretic mobility when the countercharges lie close to the sphere. Spheres with dipolar and quadrupolar charge distributions rotate and translate as predicted analytically to 4% accuracy or better. We describe how the method can treat general asymmetric shapes and charge distributions. This method offers promise as a way to characterize and manipulate asymmetrically charged colloid-scale objects from biology (e.g., viruses) and technology (e.g., self-assembled clusters).

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

Science.gov (United States)

Wisz, Mary Susanne; Pottier, Julien; Kissling, W Daniel; Pellissier, Loïc; Lenoir, Jonathan; Damgaard, Christian F; Dormann, Carsten F; Forchhammer, Mads C; Grytnes, John-Arvid; Guisan, Antoine; Heikkinen, Risto K; Høye, Toke T; Kühn, Ingolf; Luoto, Miska; Maiorano, Luigi; Nilsson, Marie-Charlotte; Normand, Signe; Öckinger, Erik; Schmidt, Niels M; Termansen, Mette; Timmermann, Allan; Wardle, David A; Aastrup, Peter; Svenning, Jens-Christian

2013-02-01

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally
Evaluating prediction uncertainty

International Nuclear Information System (INIS)

McKay, M.D.

1995-03-01

The probability distribution of a model prediction is presented as a proper basis for evaluating the uncertainty in a model prediction that arises from uncertainty in input values. Determination of important model inputs and subsets of inputs is made through comparison of the prediction distribution with conditional prediction probability distributions. Replicated Latin hypercube sampling and variance ratios are used in estimation of the distributions and in construction of importance indicators. The assumption of a linear relation between model output and inputs is not necessary for the indicators to be effective. A sequential methodology which includes an independent validation step is applied in two analysis applications to select subsets of input variables which are the dominant causes of uncertainty in the model predictions. Comparison with results from methods which assume linearity shows how those methods may fail. Finally, suggestions for treating structural uncertainty for submodels are presented
Updating known distribution models for forecasting climate change impact on endangered species.

Science.gov (United States)

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2013-01-01

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only.
Single-mode biological distributed feedback laser

DEFF Research Database (Denmark)

Vannahme, Christoph; Maier-Flaig, Florian; Lemmer, Uli

2013-01-01

Single-mode second order distributed feedback (DFB) lasers of riboflavin (vitamin B2) doped gelatine films on nanostructured low refractive index material are demonstrated. Manufacturing is based on a simple UV nanoimprint and spin-coating. Emission wavelengths of 543 nm and 562 nm for two...
Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

Science.gov (United States)

Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

2013-01-01

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224
Melatonin Distribution Reveals Clues to Its Biological Significance in Basal Metazoans

Science.gov (United States)

Roopin, Modi; Levy, Oren

2012-01-01

Although nearly ubiquitous in nature, the precise biological significance of endogenous melatonin is poorly understood in phylogenetically basal taxa. In the present work, we describe insights into the functional role of melatonin at the most “basal” level of metazoan evolution. Hitherto unknown morphological determinants of melatonin distribution were evaluated in Nematostella vectensis by detecting melatonin immunoreactivity and examining the spatial gene expression patterns of putative melatonin biosynthetic and receptor elements that are located at opposing ends of the melatonin signaling pathway. Immuno-melatonin profiling indicated an elaborate interaction with reproductive tissues, reinforcing previous conjectures of a melatonin-responsive component in anthozoan reproduction. In situ hybridization (ISH) to putative melatonin receptor elements highlighted the possibility that the bioregulatory effects of melatonin in anthozoan reproduction may be mediated by interactions with membrane receptors, as in higher vertebrates. Another intriguing finding of the present study pertains to the prevalence of melatonin in centralized nervous structures. This pattern may be of great significance given that it 1) identifies an ancestral association between melatonin and key neuronal components and 2) potentially implies that certain effects of melatonin in basal species may be spread widely by regionalized nerve centers. PMID:23300630
Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

Science.gov (United States)

Bettina Ohse; Falk Huettmann; Stefanie M. Ickert-Bond; Glenn P. Juday

2009-01-01

Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca...
Hydrological-niche models predict water plant functional group distributions in diverse wetland types.

Science.gov (United States)

Deane, David C; Nicol, Jason M; Gehrig, Susan L; Harding, Claire; Aldridge, Kane T; Goodman, Abigail M; Brookes, Justin D

2017-06-01

Human use of water resources threatens environmental water supplies. If resource managers are to develop policies that avoid unacceptable ecological impacts, some means to predict ecosystem response to changes in water availability is necessary. This is difficult to achieve at spatial scales relevant for water resource management because of the high natural variability in ecosystem hydrology and ecology. Water plant functional groups classify species with similar hydrological niche preferences together, allowing a qualitative means to generalize community responses to changes in hydrology. We tested the potential for functional groups in making quantitative prediction of water plant functional group distributions across diverse wetland types over a large geographical extent. We sampled wetlands covering a broad range of hydrogeomorphic and salinity conditions in South Australia, collecting both hydrological and floristic data from 687 quadrats across 28 wetland hydrological gradients. We built hydrological-niche models for eight water plant functional groups using a range of candidate models combining different surface inundation metrics. We then tested the predictive performance of top-ranked individual and averaged models for each functional group. Cross validation showed that models achieved acceptable predictive performance, with correct classification rates in the range 0.68-0.95. Model predictions can be made at any spatial scale that hydrological data are available and could be implemented in a geographical information system. We show the response of water plant functional groups to inundation is consistent enough across diverse wetland types to quantify the probability of hydrological impacts over regional spatial scales. © 2017 by the Ecological Society of America.
Effects of life-history requirements on the distribution of a threatened reptile.

Science.gov (United States)

Thompson, Denise M; Ligon, Day B; Patton, Jason C; Papeş, Monica

2017-04-01

Survival and reproduction are the two primary life-history traits essential for species' persistence; however, the environmental conditions that support each of these traits may not be the same. Despite this, reproductive requirements are seldom considered when estimating species' potential distributions. We sought to examine potentially limiting environmental factors influencing the distribution of an oviparous reptile of conservation concern with respect to the species' survival and reproduction and to assess the implications of the species' predicted climatic constraints on current conservation practices. We used ecological niche modeling to predict the probability of environmental suitability for the alligator snapping turtle (Macrochelys temminckii). We built an annual climate model to examine survival and a nesting climate model to examine reproduction. We combined incubation temperature requirements, products of modeled soil temperature data, and our estimated distributions to determine whether embryonic development constrained the northern distribution of the species. Low annual precipitation constrained the western distribution of alligator snapping turtles, whereas the northern distribution was constrained by thermal requirements during embryonic development. Only a portion of the geographic range predicted to have a high probability of suitability for alligator snapping turtle survival was estimated to be capable of supporting successful embryonic development. Historic occurrence records suggest adult alligator snapping turtles can survive in regions with colder climes than those associated with consistent and successful production of offspring. Estimated egg-incubation requirements indicated that current reintroductions at the northern edge of the species' range are within reproductively viable environmental conditions. Our results highlight the importance of considering survival and reproduction when estimating species' ecological niches, implicating
LXtoo: an integrated live Linux distribution for the bioinformatics community.

Science.gov (United States)

Yu, Guangchuang; Wang, Li-Gen; Meng, Xiao-Hua; He, Qing-Yu

2012-07-19

Recent advances in high-throughput technologies dramatically increase biological data generation. However, many research groups lack computing facilities and specialists. This is an obstacle that remains to be addressed. Here, we present a Linux distribution, LXtoo, to provide a flexible computing platform for bioinformatics analysis. Unlike most of the existing live Linux distributions for bioinformatics limiting their usage to sequence analysis and protein structure prediction, LXtoo incorporates a comprehensive collection of bioinformatics software, including data mining tools for microarray and proteomics, protein-protein interaction analysis, and computationally complex tasks like molecular dynamics. Moreover, most of the programs have been configured and optimized for high performance computing. LXtoo aims to provide well-supported computing environment tailored for bioinformatics research, reducing duplication of efforts in building computing infrastructure. LXtoo is distributed as a Live DVD and freely available at http://bioinformatics.jnu.edu.cn/LXtoo.
A prediction model for the effective thermal conductivity of nanofluids considering agglomeration and the radial distribution function of nanoparticles

Science.gov (United States)

Zheng, Z. M.; Wang, B.

2018-06-01

Conventional heat transfer fluids usually have low thermal conductivity, limiting their efficiency in many applications. Many experiments have shown that adding nanosize solid particles to conventional fluids can greatly enhance their thermal conductivity. To explain this anomalous phenomenon, many theoretical investigations have been conducted in recent years. Some of this research has indicated that the particle agglomeration effect that commonly occurs in nanofluids should play an important role in such enhancement of the thermal conductivity, while some have shown that the enhancement of the effective thermal conductivity might be accounted for by the structure of nanofluids, which can be described using the radial distribution function of particles. However, theoretical predictions from these studies are not in very good agreement with experimental results. This paper proposes a prediction model for the effective thermal conductivity of nanofluids, considering both the agglomeration effect and the radial distribution function of nanoparticles. The resulting theoretical predictions for several sets of nanofluids are highly consistent with experimental data.
Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms

Energy Technology Data Exchange (ETDEWEB)

Ripszam, M., E-mail: matyas.ripszam@chem.umu.se [Department of Chemistry, Umea University, 901 87 Umeå (Sweden); Gallampois, C.M.J. [Department of Chemistry, Umea University, 901 87 Umeå (Sweden); Berglund, Å. [Department of Ecology and Environmental Sciences, Umeå University, 901 87 Umeå (Sweden); Larsson, H. [Umeå Marine Sciences Centre, Umeå University, Norrbyn, 905 71 Hörnefors (Sweden); Andersson, A. [Department of Ecology and Environmental Sciences, Umeå University, 901 87 Umeå (Sweden); Tysklind, M.; Haglund, P. [Department of Chemistry, Umea University, 901 87 Umeå (Sweden)

2015-06-01

Predicted consequences of future climate change in the northern Baltic Sea include increases in sea surface temperatures and terrestrial dissolved organic carbon (DOC) runoff. These changes are expected to alter environmental distribution of anthropogenic organic contaminants (OCs). To assess likely shifts in their distributions, outdoor mesocosms were employed to mimic pelagic ecosystems at two temperatures and two DOC concentrations, current: 15 °C and 4 mg DOC L{sup −1} and, within ranges of predicted increases, 18 °C and 6 mg DOC L{sup −1}, respectively. Selected organic contaminants were added to the mesocosms to monitor changes in their distribution induced by the treatments. OC partitioning to particulate matter and sedimentation were enhanced at the higher DOC concentration, at both temperatures, while higher losses and lower partitioning of OCs to DOC were observed at the higher temperature. No combined effects of higher temperature and DOC on partitioning were observed, possibly because of the balancing nature of these processes. Therefore, changes in OCs' fates may largely depend on whether they are most sensitive to temperature or DOC concentration rises. Bromoanilines, phenanthrene, biphenyl and naphthalene were sensitive to the rise in DOC concentration, whereas organophosphates, chlorobenzenes (PCBz) and polychlorinated biphenyls (PCBs) were more sensitive to temperature. Mitotane and diflufenican were sensitive to both temperature and DOC concentration rises individually, but not in combination. - Highlights: • More contaminants remained in the ecosystem at higher organic carbon levels. • More contaminants were lost in the higher temperature treatments. • The combined effects are competitive with respect to contaminant cycling. • The individual properties of each contaminant determine their respective fate.
Effects of predicted climatic changes on distribution of organic contaminants in brackish water mesocosms

International Nuclear Information System (INIS)

Ripszam, M.; Gallampois, C.M.J.; Berglund, Å.; Larsson, H.; Andersson, A.; Tysklind, M.; Haglund, P.

2015-01-01

Predicted consequences of future climate change in the northern Baltic Sea include increases in sea surface temperatures and terrestrial dissolved organic carbon (DOC) runoff. These changes are expected to alter environmental distribution of anthropogenic organic contaminants (OCs). To assess likely shifts in their distributions, outdoor mesocosms were employed to mimic pelagic ecosystems at two temperatures and two DOC concentrations, current: 15 °C and 4 mg DOC L −1 and, within ranges of predicted increases, 18 °C and 6 mg DOC L −1 , respectively. Selected organic contaminants were added to the mesocosms to monitor changes in their distribution induced by the treatments. OC partitioning to particulate matter and sedimentation were enhanced at the higher DOC concentration, at both temperatures, while higher losses and lower partitioning of OCs to DOC were observed at the higher temperature. No combined effects of higher temperature and DOC on partitioning were observed, possibly because of the balancing nature of these processes. Therefore, changes in OCs' fates may largely depend on whether they are most sensitive to temperature or DOC concentration rises. Bromoanilines, phenanthrene, biphenyl and naphthalene were sensitive to the rise in DOC concentration, whereas organophosphates, chlorobenzenes (PCBz) and polychlorinated biphenyls (PCBs) were more sensitive to temperature. Mitotane and diflufenican were sensitive to both temperature and DOC concentration rises individually, but not in combination. - Highlights: • More contaminants remained in the ecosystem at higher organic carbon levels. • More contaminants were lost in the higher temperature treatments. • The combined effects are competitive with respect to contaminant cycling. • The individual properties of each contaminant determine their respective fate
GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

Science.gov (United States)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.
Prediction of hydrogen distribution in the reactor building in CANDU6 plant

International Nuclear Information System (INIS)

Jin, Y.; Song, Y.

2008-01-01

The CANDU plants have a lot of zircaloy. The fuel cladding, calandria tubes and pressure tubes are made of zircaloy. The zircaloy can be oxidized and hydrogen is generated during severe accident progression. The detonation or deflagration to detonation transition (DDT) due to hydrogen combustion may occur if the local hydrogen concentration or global hydrogen concentration exceeds certain value. The detonation may result in the rupture of the reactor building. The inside of the reactor building of CANDU plants is complex. So prediction of hydrogen distribution in the reactor building is important. This prediction is made using ISAAC code and GOTHIC code. ISAAC code partitioned the reactor building in to 7 compartments. GOTHIC code modeled the CANDU6 reactor building using 12 nodes. The hydrogen concentrations in the various compartments in the reactor building are compared. GOTHIC code slightly underpredicts hydrogen concentration in the F/M rooms than ISAAC code, but trend is same. The hydrogen concentration in the boiler room and the moderator room shows almost same as for both codes. (author)
A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design.

Science.gov (United States)

Alford, Rebecca F; Leaver-Fay, Andrew; Gonzales, Lynda; Dolan, Erin L; Gray, Jeffrey J

2017-12-01

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.
A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design.

Directory of Open Access Journals (Sweden)

Rebecca F Alford

2017-12-01

Full Text Available Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.
A prediction method of temperature distribution and thermal stress for the throttle turbine rotor and its application

Directory of Open Access Journals (Sweden)

Yang Yu

2017-01-01

Full Text Available In this paper, a prediction method of the temperature distribution for the thermal stress for the throttle-regulated steam turbine rotor is proposed. The rotor thermal stress curve can be calculated according to the preset power requirement, the operation mode and the predicted critical parameters. The results of the 660 MW throttle turbine rotor show that the operators are able to predict the operation results and to adjust the operation parameters in advance with the help of the inertial element method. Meanwhile, it can also raise the operation level, thus providing the technical guarantee for the thermal stress optimization control and the safety of the steam turbine rotor under the variable load operation.
The integral biologically effective dose to predict brain stem toxicity of hypofractionated stereotactic radiotherapy

International Nuclear Information System (INIS)

Clark, Brenda G.; Souhami, Luis; Pla, Conrado; Al-Amro, Abdullah S.; Bahary, Jean-Paul; Villemure, Jean-Guy; Caron, Jean-Louis; Olivier, Andre; Podgorsak, Ervin B.

1998-01-01

Purpose: The aim of this work was to develop a parameter for use during fractionated stereotactic radiotherapy treatment planning to aid in the determination of the appropriate treatment volume and fractionation regimen that will minimize risk of late damage to normal tissue. Materials and Methods: We have used the linear quadratic model to assess the biologically effective dose at the periphery of stereotactic radiotherapy treatment volumes that impinge on the brain stem. This paper reports a retrospective study of 77 patients with malignant and benign intracranial lesions, treated between 1987 and 1995, with the dynamic rotation technique in 6 fractions over a period of 2 weeks, to a total dose of 42 Gy prescribed at the 90% isodose surface. From differential dose-volume histograms, we evaluated biologically effective dose-volume histograms and obtained an integral biologically-effective dose (IBED) in each case. Results: Of the 77 patients in the study, 36 had target volumes positioned so that the brain stem received more than 1% of the prescribed dose, and 4 of these, all treated for meningioma, developed serious late damage involving the brain stem. Other than type of lesion, the only significant variable was the volume of brain stem exposed. An analysis of the IBEDs received by these 36 patients shows evidence of a threshold value for late damage to the brain stem consistent with similar thresholds that have been determined for external beam radiotherapy. Conclusions: We have introduced a new parameter, the IBED, that may be used to represent the fractional effective dose to structures such as the brain stem that are partially irradiated with stereotactic dose distributions. The IBED is easily calculated prior to treatment and may be used to determine appropriate treatment volumes and fractionation regimens minimizing possible toxicity to normal tissue
TOO MANY, TOO FEW, OR JUST RIGHT? THE PREDICTED NUMBER AND DISTRIBUTION OF MILKY WAY DWARF GALAXIES

International Nuclear Information System (INIS)

Hargis, Jonathan R.; Willman, Beth; Peter, Annika H. G.

2014-01-01

We predict the spatial distribution and number of Milky Way dwarf galaxies to be discovered in the Dark Energy Survey (DES) and Large Synoptic Survey Telescope (LSST) surveys, by completeness correcting the observed Sloan Digital Sky Survey dwarf population. We apply most massive in the past, earliest forming, and earliest infall toy models to a set of dark matter-only simulated Milky Way/M31 halo pairs from the Exploring the Local Volume In Simulations project. Inclusive of all toy models and simulations, at 90% confidence we predict a total of 37-114 L ≳ 10 3 L ☉ dwarfs and 131-782 L ≲ 10 3 L ☉ dwarfs within 300 kpc. These numbers of L ≳ 10 3 L ☉ dwarfs are dramatically lower than previous predictions, owing primarily to our use of updated detection limits and the decreasing number of SDSS dwarfs discovered per sky area. For an effective r limit of 25.8 mag, we predict 3-13 L ≳ 10 3 L ☉ and 9-99 L ≲ 10 3 L ☉ dwarfs for DES, and 18-53 L ≳ 10 3 L ☉ and 53-307 L ≲ 10 3 L ☉ dwarfs for LSST. We also show that the observed spatial distribution of Milky Way dwarfs in the LSST-era will discriminate between the earliest infall and other simplified models for how dwarf galaxies populate dark matter subhalos

Inverse problems in systems biology

International Nuclear Information System (INIS)

Engl, Heinz W; Lu, James; Müller, Stefan; Flamm, Christoph; Schuster, Peter; Kügler, Philipp

2009-01-01

Systems biology is a new discipline built upon the premise that an understanding of how cells and organisms carry out their functions cannot be gained by looking at cellular components in isolation. Instead, consideration of the interplay between the parts of systems is indispensable for analyzing, modeling, and predicting systems' behavior. Studying biological processes under this premise, systems biology combines experimental techniques and computational methods in order to construct predictive models. Both in building and utilizing models of biological systems, inverse problems arise at several occasions, for example, (i) when experimental time series and steady state data are used to construct biochemical reaction networks, (ii) when model parameters are identified that capture underlying mechanisms or (iii) when desired qualitative behavior such as bistability or limit cycle oscillations is engineered by proper choices of parameter combinations. In this paper we review principles of the modeling process in systems biology and illustrate the ill-posedness and regularization of parameter identification problems in that context. Furthermore, we discuss the methodology of qualitative inverse problems and demonstrate how sparsity enforcing regularization allows the determination of key reaction mechanisms underlying the qualitative behavior. (topical review)
Genome-scale biological models for industrial microbial systems.

Science.gov (United States)

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
Hierarchical Model Predictive Control for Plug-and-Play Resource Distribution

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

2012-01-01

of autonomous units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid......This chapter deals with hierarchical model predictive control (MPC) of distributed systems. A three level hierarchical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level......, arising on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The proposed method can also be applied to supply chain management systems, where the challenge is to balance demand and supply, using a number of storages each with a maximal...
Spatially distributed flame transfer functions for predicting combustion dynamics in lean premixed gas turbine combustors

Energy Technology Data Exchange (ETDEWEB)

Kim, K.T.; Lee, J.G.; Quay, B.D.; Santavicca, D.A. [Center for Advanced Power Generation, Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA (United States)

2010-09-15

The present paper describes a methodology to improve the accuracy of prediction of the eigenfrequencies and growth rates of self-induced instabilities and demonstrates its application to a laboratory-scale, swirl-stabilized, lean-premixed, gas turbine combustor. The influence of the spatial heat release distribution is accounted for using local flame transfer function (FTF) measurements. The two-microphone technique and CH{sup *} chemiluminescence intensity measurements are used to determine the input (inlet velocity perturbation) and the output functions (heat release oscillation), respectively, for the local flame transfer functions. The experimentally determined local flame transfer functions are superposed using the flame transfer function superposition principle, and the result is incorporated into an analytic thermoacoustic model, in order to predict the linear stability characteristics of a given system. Results show that when the flame length is not acoustically compact the model prediction calculated using the local flame transfer functions is better than the prediction made using the global flame transfer function. In the case of a flame in the compact flame regime, accurate predictions of eigenfrequencies and growth rates can be obtained using the global flame transfer function. It was also found that the general response characteristics of the local FTF (gain and phase) are qualitatively the same as those of the global FTF. (author)
Microscopic prediction of speech intelligibility in spatially distributed speech-shaped noise for normal-hearing listeners.

Science.gov (United States)

Geravanchizadeh, Masoud; Fallah, Ali

2015-12-01

A binaural and psychoacoustically motivated intelligibility model, based on a well-known monaural microscopic model is proposed. This model simulates a phoneme recognition task in the presence of spatially distributed speech-shaped noise in anechoic scenarios. In the proposed model, binaural advantage effects are considered by generating a feature vector for a dynamic-time-warping speech recognizer. This vector consists of three subvectors incorporating two monaural subvectors to model the better-ear hearing, and a binaural subvector to simulate the binaural unmasking effect. The binaural unit of the model is based on equalization-cancellation theory. This model operates blindly, which means separate recordings of speech and noise are not required for the predictions. Speech intelligibility tests were conducted with 12 normal hearing listeners by collecting speech reception thresholds (SRTs) in the presence of single and multiple sources of speech-shaped noise. The comparison of the model predictions with the measured binaural SRTs, and with the predictions of a macroscopic binaural model called extended equalization-cancellation, shows that this approach predicts the intelligibility in anechoic scenarios with good precision. The square of the correlation coefficient (r(2)) and the mean-absolute error between the model predictions and the measurements are 0.98 and 0.62 dB, respectively.
Predicting the Distribution of Yellowfin Tuna in Philippine Waters

Science.gov (United States)

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

2015-12-01

The Philippines is considered as a major tuna producer in the Western and Central Pacific Ocean, both for domestic consumption and on industrial scale. However, with the ever-increasing demand of growing population, it has always been a challenge to achieve sustainable fishing. The creation of satellite-derived potential fishing zone maps is a technology that has been adopted by advanced countries for almost three decades already and has led to reduction in search times by up to 40%. In this study, a Generalized Additive Model (GAM) is developed to predict the distribution of the Yellowfin tuna species in seas surrounding the Philippines based on the Catch-Per-Unit-Effort (CPUE) index. Level 3 gridded chlorophyll-a and sea surface temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite of the National Aeronautics and Space Administration (NASA) are the main input parameters of the model. Chlorophyll-a is linked with the presence of phytoplankton, which indicates primary productivity and suggests potential regions of fish aggregation. Fish also prefers to stay in regions where the temperature is stable, thus the sea surface temperature fronts serve as a guide to locate concentrations of fish school. Historical monthly tuna catch data from Western and Central Pacific Commissions (WCPFC) is used to train the model. The resulting predictions are converted to potential fishing zone maps and are evaluated within and beyond the historical time range of the training data used. Diagnostic tests involving adjusted R2 value, GAM residual plots and root mean square error value are used to assess the accuracy of the model. The generated maps were able to confirm locations of known tuna fishing grounds in Mindanao and other parts of the country, as well us detect their seasonality and interannual variability. To improve the performance of the model, ancillary data such as surface winds reanalysis from National Centers for
Species distribution modeling for the invasive raccoon dog (Nyctereutes procyonoides) in Austria and first range predictions for alpine environments

OpenAIRE

Duscher Tanja; Nopp-Mayr Ursula

2017-01-01

Species distribution models are important tools for wildlife management planning, particularly in the case of invasive species. We employed a recent framework for niche-based invasive species distribution modeling to predict the probability of presence for the invasive raccoon dog (Nyctereutes procyonoides) in Austria. The raccoon dog is an adaptive, mobile and highly reproductive Asiatic canid that has successfully invaded many parts of Europe. It is known...
Predictive modelling of habitat use by marine predators with respect to the abundance and depth distribution of pelagic prey

Science.gov (United States)

Boyd, Charlotte; Castillo, Ramiro; Hunt, George L.; Punt, André E..; VanBlaricom, Glenn R.; Weimerskirch, Henri; Bertrand, Sophie

2015-01-01

Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey.
Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey.

Science.gov (United States)

Boyd, Charlotte; Castillo, Ramiro; Hunt, George L; Punt, André E; VanBlaricom, Glenn R; Weimerskirch, Henri; Bertrand, Sophie

2015-11-01

Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby (Sula variegata) and deeper diving Guanay Cormorant (Phalacrocorax bougainvilliorum), and the abundance and depth distribution of their main prey, Peruvian anchoveta (Engraulis ringens). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. Analysis of the relative
GPSR: A Resource for Genomics Proteomics and Systems Biology

Indian Academy of Sciences (India)

GPSR: A Resource for Genomics Proteomics and Systems Biology · Simple Calculation Programs for Biology Immunological Methods · Simple Calculation Programs for Biology Methods in Molecular Biology · Simple Calculation Programs for Biology Other Methods · PowerPoint Presentation · Slide 6 · Slide 7 · Prediction of ...
Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System

Directory of Open Access Journals (Sweden)

Tang Xiaofeng

2014-01-01

Full Text Available The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow.
The clinical features of alcohol use disorders in biological and step-fathers that predict risk for alcohol use disorders in offspring.

Science.gov (United States)

Kendler, Kenneth S; Ohlsson, Henrik; Edwards, Alexis; Sundquist, Jan; Sundquist, Kristina

2017-12-01

Given that Alcohol Use Disorder (AUD) is clinically heterogeneous, can we, in a large epidemiological sample using public registries, identify clinical features of AUD cases in biological and step-fathers that index, respectively, genetic and familial-environmental risk for AUD in their offspring? From all father-offspring pairs where the father had AUD and the offspring was born 1960-1990, we identified not-lived-with (NLW) biological fathers (n = 38,376) and step-father pairs (n = 9,711). The relationship between clinical and historical features of the father's AUD and risk for AUD in offspring was assessed by linear hazard regression. Age at first registration for AUD and recurrence of AUD registration were significantly stronger predictors of risk for AUD in the offspring of NLW fathers than in step-fathers. By contrast, number of AUD registrations in NLW fathers and step-fathers were equally predictive of risk for AUD in offspring. However, while the number of step-father AUD registrations that occurred when he was living them with significantly predicted risk for AUD in his step-children, the number of registrations that occurred when not residing with his step-children was unassociated with their AUD risk. In an epidemiological sample, we could meaningfully differentiate between features of AUD in fathers that indexed genetic risk which was transmitted to biological offspring (early age at onset and recurrence) versus indexed environmental risk (registrations while rearing) which increased risk in step-children. © 2017 Wiley Periodicals, Inc.
Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

Science.gov (United States)

Laiolo, P.; Gabellani, S.; Campo, L.; Silvestro, F.; Delogu, F.; Rudari, R.; Pulvirenti, L.; Boni, G.; Fascetti, F.; Pierdicca, N.; Crapolicchio, R.; Hasenauer, S.; Puca, S.

2016-06-01

The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012-June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash-Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.
Using Noble Gas Measurements to Derive Air-Sea Process Information and Predict Physical Gas Saturations

Science.gov (United States)

Hamme, Roberta C.; Emerson, Steven R.; Severinghaus, Jeffrey P.; Long, Matthew C.; Yashayaev, Igor

2017-10-01

Dissolved gas distributions are important because they influence oceanic habitats and Earth's climate, yet competing controls by biology and physics make gas distributions challenging to predict. Bubble-mediated gas exchange, temperature change, and varying atmospheric pressure all push gases away from equilibrium. Here we use new noble gas measurements from the Labrador Sea to demonstrate a technique to quantify physical processes. Our analysis shows that water-mass formation can be represented by a quasi steady state in which bubble fluxes and cooling push gases away from equilibrium balanced by diffusive gas exchange forcing gases toward equilibrium. We quantify the rates of these physical processes from our measurements, allowing direct comparison to gas exchange parameterizations, and predict the physically driven saturation of other gases. This technique produces predictions that reasonably match N2/Ar observations and demonstrates that physical processes should force SF6 to be ˜6% more supersaturated than CFC-11 and CFC-12, impacting ventilation age calculations.
Generalised extreme value distributions provide a natural hypothesis for the shape of seed mass distributions.

Directory of Open Access Journals (Sweden)

Will Edwards

Full Text Available Among co-occurring species, values for functionally important plant traits span orders of magnitude, are uni-modal, and generally positively skewed. Such data are usually log-transformed "for normality" but no convincing mechanistic explanation for a log-normal expectation exists. Here we propose a hypothesis for the distribution of seed masses based on generalised extreme value distributions (GEVs, a class of probability distributions used in climatology to characterise the impact of event magnitudes and frequencies; events that impose strong directional selection on biological traits. In tests involving datasets from 34 locations across the globe, GEVs described log10 seed mass distributions as well or better than conventional normalising statistics in 79% of cases, and revealed a systematic tendency for an overabundance of small seed sizes associated with low latitudes. GEVs characterise disturbance events experienced in a location to which individual species' life histories could respond, providing a natural, biological explanation for trait expression that is lacking from all previous hypotheses attempting to describe trait distributions in multispecies assemblages. We suggest that GEVs could provide a mechanistic explanation for plant trait distributions and potentially link biology and climatology under a single paradigm.
A Brief Introduction to Chinese Biological Abstracts

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Chinese Biological Abstracts (CBA), a state-level indexing and abstracting journal published monthly, is jointly sponsored by the Library of the Chinese Academy of Sciences, the Shanghai Institutes for Biological Sciences as well as the Biological Information Network of the Chinese Academy of Sciences, published and distributed by the Shanghai Institutes for Biological Sciences, and approved by the State Scientific and Technological Commission.
Development and verification of an analytical algorithm to predict absorbed dose distributions in ocular proton therapy using Monte Carlo simulations

International Nuclear Information System (INIS)

Koch, Nicholas C; Newhauser, Wayne D

2010-01-01

Proton beam radiotherapy is an effective and non-invasive treatment for uveal melanoma. Recent research efforts have focused on improving the dosimetric accuracy of treatment planning and overcoming the present limitation of relative analytical dose calculations. Monte Carlo algorithms have been shown to accurately predict dose per monitor unit (D/MU) values, but this has yet to be shown for analytical algorithms dedicated to ocular proton therapy, which are typically less computationally expensive than Monte Carlo algorithms. The objective of this study was to determine if an analytical method could predict absolute dose distributions and D/MU values for a variety of treatment fields like those used in ocular proton therapy. To accomplish this objective, we used a previously validated Monte Carlo model of an ocular nozzle to develop an analytical algorithm to predict three-dimensional distributions of D/MU values from pristine Bragg peaks and therapeutically useful spread-out Bragg peaks (SOBPs). Results demonstrated generally good agreement between the analytical and Monte Carlo absolute dose calculations. While agreement in the proximal region decreased for beams with less penetrating Bragg peaks compared with the open-beam condition, the difference was shown to be largely attributable to edge-scattered protons. A method for including this effect in any future analytical algorithm was proposed. Comparisons of D/MU values showed typical agreement to within 0.5%. We conclude that analytical algorithms can be employed to accurately predict absolute proton dose distributions delivered by an ocular nozzle.
Back to the Roots: Prediction of Biologically Active Natural Products from Ayurveda Traditional Medicine

DEFF Research Database (Denmark)

Polur, Honey; Joshi, Tejal; Workman, Christopher

2011-01-01

Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this....... We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin......, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds...
Merging metagenomics and geochemistry reveals environmental controls on biological diversity and evolution.

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Alsop, Eric B; Boyd, Eric S; Raymond, Jason

2014-05-28

The metabolic strategies employed by microbes inhabiting natural systems are, in large part, dictated by the physical and geochemical properties of the environment. This study sheds light onto the complex relationship between biology and environmental geochemistry using forty-three metagenomes collected from geochemically diverse and globally distributed natural systems. It is widely hypothesized that many uncommonly measured geochemical parameters affect community dynamics and this study leverages the development and application of multidimensional biogeochemical metrics to study correlations between geochemistry and microbial ecology. Analysis techniques such as a Markov cluster-based measure of the evolutionary distance between whole communities and a principal component analysis (PCA) of the geochemical gradients between environments allows for the determination of correlations between microbial community dynamics and environmental geochemistry and provides insight into which geochemical parameters most strongly influence microbial biodiversity. By progressively building from samples taken along well defined geochemical gradients to samples widely dispersed in geochemical space this study reveals strong links between the extent of taxonomic and functional diversification of resident communities and environmental geochemistry and reveals temperature and pH as the primary factors that have shaped the evolution of these communities. Moreover, the inclusion of extensive geochemical data into analyses reveals new links between geochemical parameters (e.g. oxygen and trace element availability) and the distribution and taxonomic diversification of communities at the functional level. Further, an overall geochemical gradient (from multivariate analyses) between natural systems provides one of the most complete predictions of microbial taxonomic and functional composition. Clustering based on the frequency in which orthologous proteins occur among metagenomes
Mathematical modeling of biological processes

CERN Document Server

Friedman, Avner

2014-01-01

This book on mathematical modeling of biological processes includes a wide selection of biological topics that demonstrate the power of mathematics and computational codes in setting up biological processes with a rigorous and predictive framework. Topics include: enzyme dynamics, spread of disease, harvesting bacteria, competition among live species, neuronal oscillations, transport of neurofilaments in axon, cancer and cancer therapy, and granulomas. Complete with a description of the biological background and biological question that requires the use of mathematics, this book is developed for graduate students and advanced undergraduate students with only basic knowledge of ordinary differential equations and partial differential equations; background in biology is not required. Students will gain knowledge on how to program with MATLAB without previous programming experience and how to use codes in order to test biological hypothesis.

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins.

Science.gov (United States)

Konc, Janez; Janežič, Dušanka

2017-09-01

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov. Copyright © 2017 Elsevier Ltd. All rights reserved.
A comparison of biological effect and spray liquid distribution and deposition for different spray application techniques in different crops

OpenAIRE

Larsolle, Anders; Wretblad, Per; Westberg, Carl

2002-01-01

The objective of this study was to compare a selection of spray application techniques with different application volumes, with respect to the spray liquid distribution on flat surfaces, the deposition in fully developed crops and the biological effect. The spray application techniques in this study were conventional spray technique with three different nozzles: Teelet XR, Lechler ID and Lurmark DriftBeta, and also AirTec, Danfoil, Hardi Twin, Kyndestoit and Släpduk. The dynamic spray liquid ...
A Gauss-Kronrod-Trapezoidal integration scheme for modeling biological tissues with continuous fiber distributions.

Science.gov (United States)

Hou, Chieh; Ateshian, Gerard A

2016-01-01

Fibrous biological tissues may be modeled using a continuous fiber distribution (CFD) to capture tension-compression nonlinearity, anisotropic fiber distributions, and load-induced anisotropy. The CFD framework requires spherical integration of weighted individual fiber responses, with fibers contributing to the stress response only when they are in tension. The common method for performing this integration employs the discretization of the unit sphere into a polyhedron with nearly uniform triangular faces (finite element integration or FEI scheme). Although FEI has proven to be more accurate and efficient than integration using spherical coordinates, it presents three major drawbacks: First, the number of elements on the unit sphere needed to achieve satisfactory accuracy becomes a significant computational cost in a finite element (FE) analysis. Second, fibers may not be in tension in some regions on the unit sphere, where the integration becomes a waste. Third, if tensed fiber bundles span a small region compared to the area of the elements on the sphere, a significant discretization error arises. This study presents an integration scheme specialized to the CFD framework, which significantly mitigates the first drawback of the FEI scheme, while eliminating the second and third completely. Here, integration is performed only over the regions of the unit sphere where fibers are in tension. Gauss-Kronrod quadrature is used across latitudes and the trapezoidal scheme across longitudes. Over a wide range of strain states, fiber material properties, and fiber angular distributions, results demonstrate that this new scheme always outperforms FEI, sometimes by orders of magnitude in the number of computational steps and relative accuracy of the stress calculation.
Prediction of calcite Cement Distribution in Shallow Marine Sandstone Reservoirs using Seismic Data

Energy Technology Data Exchange (ETDEWEB)

Bakke, N.E.

1996-12-31

This doctoral thesis investigates how calcite cemented layers can be detected by reflection seismic data and how seismic data combined with other methods can be used to predict lateral variation in calcite cementation in shallow marine sandstone reservoirs. Focus is on the geophysical aspects. Sequence stratigraphy and stochastic modelling aspects are only covered superficially. Possible sources of calcite in shallow marine sandstone are grouped into internal and external sources depending on their location relative to the presently cemented rock. Well data and seismic data from the Troll Field in the Norwegian North Sea have been analysed. Tuning amplitudes from stacks of thin calcite cemented layers are analysed. Tuning effects are constructive or destructive interference of pulses resulting from two or more closely spaced reflectors. The zero-offset tuning amplitude is shown to depend on calcite content in the stack and vertical stack size. The relationship is found by regression analysis based on extensive seismic modelling. The results are used to predict calcite distribution in a synthetic and a real data example. It is found that describing calcite cemented beds in shallow marine sandstone reservoirs is not a deterministic problem. Hence seismic inversion and sequence stratigraphy interpretation of well data have been combined in a probabilistic approach to produce models of calcite cemented barriers constrained by a maximum amount of information. It is concluded that seismic data can provide valuable information on distribution of calcite cemented beds in reservoirs where the background sandstones are relatively homogeneous. 63 refs., 78 figs., 10 tabs.
The Distributed Biological Observatory (DBO): A Change Detection Array in the Pacific Arctic Region

Science.gov (United States)

Grebmeier, J. M.; Moore, S. E.; Cooper, L. W.; Frey, K. E.; Pickart, R. S.

2012-12-01

The Pacific region of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Climate changes are likely to result in shifts in species composition and abundance, northward range expansions, and changes in lower trophic level productivity that can directly cascade and affect the life cycles of higher trophic level organisms. The developing Distributed Biological Observatory (DBO) is composed of focused biological and oceanographic sampling at biological "hot spot" sites for lower and higher trophic organisms on a latitudinal S-to-N array. The DBO is being developed by an international consortium of scientists in the Pacific Arctic as a change detection array to systematically track the broad biological response to sea ice retreat and associated environmental change. Coordinated ship-based observations over various seasons, together with satellite and mooring data collections at the designated sites, can provide an early detection system for biological and ecosystem response to climate warming. The data documenting the importance of these ecosystem "hotspots" provide a growing marine time-series from the northern Bering Sea to Barrow Canyon at the boundary of the Chukchi and Beaufort seas. Results from these studies show spatial changes in carbon production and export to the sediments as indicated by infaunal community composition and biomass, shifts in sediment grain size on a S-to-N latitudinal gradient, and range extensions for lower trophic levels and further northward migration of higher trophic organisms, such as gray whales. There is also direct evidence of negative impacts on ice dependent species, such as walrus and polar bears. As a ramp up to a fully operational observatory, hydrographic transects and select
Prediction equation for lower limbs lean soft tissue in circumpubertal boys using anthropometry and biological maturation.

Directory of Open Access Journals (Sweden)

João Valente-dos-Santos

Full Text Available Lean soft tissue (LST, a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10-13 years, using dual-energy X-ray absorptiometry (DXA as the reference method. Total body and segmental (lower limbs composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg. Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST = 0.838× ln (body mass +0.476× ln (leg length - 0.135× ln (mid-thigh circumference - 0.053× ln (anterior mid-thigh skinfold - 0.098× ln (medial calf skinfold - 2.680+0.010× (percentage of attained PMS (R = 0.95. The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS method (R2PRESS = 0.90. Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to -1.27 kg. The new model accurately predicts lower limbs LST in circumpubertal boys.
WE-B-304-02: Treatment Planning Evaluation and Optimization Should Be Biologically and Not Dose/volume Based

International Nuclear Information System (INIS)

Deasy, J.

2015-01-01

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations
Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

Science.gov (United States)

Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

2015-01-01

Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303
Biological validation of physical coastal waters classification along the NE Atlantic region based on rocky macroalgae distribution

Science.gov (United States)

Ramos, Elvira; Puente, Araceli; Juanes, José Antonio; Neto, João M.; Pedersen, Are; Bartsch, Inka; Scanlan, Clare; Wilkes, Robert; Van den Bergh, Erika; Ar Gall, Erwan; Melo, Ricardo

2014-06-01

A methodology to classify rocky shores along the North East Atlantic (NEA) region was developed. Previously, biotypes and the variability of environmental conditions within these were recognized based on abiotic data. A biological validation was required in order to support the ecological meaning of the physical typologies obtained. A database of intertidal macroalgae species occurring in the coastal area between Norway and the South Iberian Peninsula was generated. Semi-quantitative abundance data of the most representative macroalgal taxa were collected in three levels: common, rare or absent. Ordination and classification multivariate analyses revealed a clear latitudinal gradient in the distribution of macroalgae species resulting in two distinct groups: one northern and one southern group, separated at the coast of Brittany (France). In general, the results based on biological data coincided with the results based on physical characteristics. The ecological meaning of the coastal waters classification at a broad scale shown in this work demonstrates that it can be valuable as a practical tool for conservation and management purposes.
Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues

Science.gov (United States)

Möhler, Christian; Russ, Tom; Wohlfahrt, Patrick; Elter, Alina; Runz, Armin; Richter, Christian; Greilich, Steffen

2018-01-01

An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissue samples were prepared for measurement, comprising five samples each of 13 tissue types representing about 80% of the total body mass (three different muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone). Using a standard stoichiometric calibration for single-energy CT (SECT) as well as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all tissues and then compared to the measured reference. With the SECT approach, the SPRs of all tissues were predicted with a mean error of (-0.84 ± 0.12)% and a mean absolute error of (1.27 ± 0.12)%. In contrast, the DECT-based SPR predictions were overall consistent with the measured reference with a mean error of (-0.02 ± 0.15)% and a mean absolute error of (0.10 ± 0.15)%. Thus, in this study, the potential of DECT to decrease range uncertainty could be confirmed in biological tissue.
Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

OpenAIRE

Bustamante, Javier; Seoane, Javier

2004-01-01

Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short-toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southe...
Kmerind: A Flexible Parallel Library for K-mer Indexing of Biological Sequences on Distributed Memory Systems.

Science.gov (United States)

Pan, Tony; Flick, Patrick; Jain, Chirag; Liu, Yongchao; Aluru, Srinivas

2017-10-09

Counting and indexing fixed length substrings, or k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases every 3 days. We present Kmerind, a high performance parallel k-mer indexing library for distributed memory environments. The Kmerind library provides a set of simple and consistent APIs with sequential semantics and parallel implementations that are designed to be flexible and extensible. Kmerind's k-mer counter performs similarly or better than the best existing k-mer counting tools even on shared memory systems. In a distributed memory environment, Kmerind counts k-mers in a 120 GB sequence read dataset in less than 13 seconds on 1024 Xeon CPU cores, and fully indexes their positions in approximately 17 seconds. Querying for 1% of the k-mers in these indices can be completed in 0.23 seconds and 28 seconds, respectively. Kmerind is the first k-mer indexing library for distributed memory environments, and the first extensible library for general k-mer indexing and counting. Kmerind is available at https://github.com/ParBLiSS/kmerind.
Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models.

Science.gov (United States)

Alden, Kieran; Timmis, Jon; Andrews, Paul S; Veiga-Fernandes, Henrique; Coles, Mark

2017-01-01

Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.
Species-environment associations and predicted distribution of Black Oystercatcher breeding pairs in Haida Gwaii, British Columbia, Canada

Directory of Open Access Journals (Sweden)

Sebastian Dalgarno

2017-12-01

Full Text Available We present a species distribution model (SDM for prediction of Black Oystercatcher (Haematopus bachmani breeding pair occurrence in Haida Gwaii, British Columbia. Boosted regression trees, a machine learning algorithm, was used to fit the model. In total, 14 predictors were selected a priori through development of a conceptual model. Breeding pair occurrence data were compiled from two available surveys conducted in 2005 and 2010 (545 km of shoreline surveyed in total. All data were aggregated to common model units (vector polyline shoreline segments approximately 100 m in length, which approximate breeding territory size. The final model, which included eight predictors (distance to treeline, island area, wave exposure, shoreline type, intertidal area within 50 m, segment length, rat occurrence, and intertidal area within 1000 m, had excellent predictive ability assessed by 10-fold cross-validation (AUC = 0.89. Predictive ability was reduced when the model was trained and tested on spatially (AUC = 0.86 and temporally (AUC = 0.83 independent data. Distance to treeline and island area had greatest influence on the model (RI = 41.5% and RI = 36.7%, respectively; we hypothesized that these predictors are related to avoidance of predators. Partial dependence plots revealed that breeding pairs tended to occur: further from the treeline, on small islands, at high wave exposures, at moderate intertidal area, on bedrock or gravel shoreline types, and on islands without rats. However, breeding pairs tended not to occur on very small islands and at very high wave exposures, which we hypothesize to reflect avoidance of nest washout. Results may inform local conservation and management efforts, i.e., from predictive maps, and eventual development of a high-resolution (~100 m model for prediction of Black Oystercatcher breeding pairs at a regional scale. Further, methods and GIS data sets developed may be used to model distribution of other coastal species
Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

Science.gov (United States)

Marshall, Leon; Carvalheiro, Luísa G; Aguirre-Gutiérrez, Jesús; Bos, Merijn; de Groot, G Arjen; Kleijn, David; Potts, Simon G; Reemer, Menno; Roberts, Stuart; Scheper, Jeroen; Biesmeijer, Jacobus C

2015-10-01

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs' usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and
TOO MANY, TOO FEW, OR JUST RIGHT? THE PREDICTED NUMBER AND DISTRIBUTION OF MILKY WAY DWARF GALAXIES

Energy Technology Data Exchange (ETDEWEB)

Hargis, Jonathan R.; Willman, Beth [Department of Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041 (United States); Peter, Annika H. G., E-mail: jhargis@haverford.edu [CCAPP and Department of Physics, The Ohio State University, 191 West Woodruff Avenue, Columbus, OH 43210 (United States)

2014-11-01

We predict the spatial distribution and number of Milky Way dwarf galaxies to be discovered in the Dark Energy Survey (DES) and Large Synoptic Survey Telescope (LSST) surveys, by completeness correcting the observed Sloan Digital Sky Survey dwarf population. We apply most massive in the past, earliest forming, and earliest infall toy models to a set of dark matter-only simulated Milky Way/M31 halo pairs from the Exploring the Local Volume In Simulations project. Inclusive of all toy models and simulations, at 90% confidence we predict a total of 37-114 L ≳ 10{sup 3} L {sub ☉} dwarfs and 131-782 L ≲ 10{sup 3} L {sub ☉} dwarfs within 300 kpc. These numbers of L ≳ 10{sup 3} L {sub ☉} dwarfs are dramatically lower than previous predictions, owing primarily to our use of updated detection limits and the decreasing number of SDSS dwarfs discovered per sky area. For an effective r {sub limit} of 25.8 mag, we predict 3-13 L ≳ 10{sup 3} L {sub ☉} and 9-99 L ≲ 10{sup 3} L {sub ☉} dwarfs for DES, and 18-53 L ≳ 10{sup 3} L {sub ☉} and 53-307 L ≲ 10{sup 3} L {sub ☉} dwarfs for LSST. We also show that the observed spatial distribution of Milky Way dwarfs in the LSST-era will discriminate between the earliest infall and other simplified models for how dwarf galaxies populate dark matter subhalos.
Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

Science.gov (United States)

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.
The nucleic acid revolution continues - will forensic biology become forensic molecular biology?

Science.gov (United States)

Gunn, Peter; Walsh, Simon; Roux, Claude

2014-01-01

Molecular biology has evolved far beyond that which could have been predicted at the time DNA identity testing was established. Indeed we should now perhaps be referring to "forensic molecular biology." Aside from DNA's established role in identifying the "who" in crime investigations, other developments in medical and developmental molecular biology are now ripe for application to forensic challenges. The impact of DNA methylation and other post-fertilization DNA modifications, plus the emerging role of small RNAs in the control of gene expression, is re-writing our understanding of human biology. It is apparent that these emerging technologies will expand forensic molecular biology to allow for inferences about "when" a crime took place and "what" took place. However, just as the introduction of DNA identity testing engendered many challenges, so the expansion of molecular biology into these domains will raise again the issues of scientific validity, interpretation, probative value, and infringement of personal liberties. This Commentary ponders some of these emerging issues, and presents some ideas on how they will affect the conduct of forensic molecular biology in the foreseeable future.
Energy loss and straggling of MeV ions through biological samples

International Nuclear Information System (INIS)

Ma Lei; Wang Yugang; Xue Jianming; Chen Qizhong; Zhang Weiming; Zhang Yanwen

2007-01-01

Energy loss and energy straggling of energetic ions through natural dehydrated biological samples were investigated using transmission technique. Biological samples (onion membrane, egg coat, and tomato coat) with different mass thickness were studied, together with Mylar for comparison. The energy loss and energy straggling of MeV H and He ions after penetrating the biological and Mylar samples were measured. The experimental results show that the average energy losses of MeV ions through the biological samples are consistent with SRIM predictions; however, large deviation in energy straggling is observed between the measured results and the SRIM predictions. Taking into account inhomogeneity in mass density and structure of the biological sample, an energy straggling formula is suggested, and the experimental energy straggling values are well predicted by the proposed formula
Distribution of biologic, anthropogenic, and volcanic constituents as a proxy for sediment transport in the San Francisco Bay Coastal System

Science.gov (United States)

McGann, Mary; Erikson, Li H.; Wan, Elmira; Powell, Charles; Maddocks, Rosalie F.; Barnard, P.L.; Jaffee, B.E.; Schoellhamer, D.H.

2013-01-01

Although conventional sediment parameters (mean grain size, sorting, and skewness) and provenance have typically been used to infer sediment transport pathways, most freshwater, brackish, and marine environments are also characterized by abundant sediment constituents of biological, and possibly anthropogenic and volcanic, origin that can provide additional insight into local sedimentary processes. The biota will be spatially distributed according to its response to environmental parameters such as water temperature, salinity, dissolved oxygen, organic carbon content, grain size, and intensity of currents and tidal flow, whereas the presence of anthropogenic and volcanic constituents will reflect proximity to source areas and whether they are fluvially- or aerially-transported. Because each of these constituents have a unique environmental signature, they are a more precise proxy for that source area than the conventional sedimentary process indicators. This San Francisco Bay Coastal System study demonstrates that by applying a multi-proxy approach, the primary sites of sediment transport can be identified. Many of these sites are far from where the constituents originated, showing that sediment transport is widespread in the region. Although not often used, identifying and interpreting the distribution of naturally-occurring and allochthonous biologic, anthropogenic, and volcanic sediment constituents is a powerful tool to aid in the investigation of sediment transport pathways in other coastal systems.

Right Versus Left Colon Cancer Biology: Integrating the Consensus Molecular Subtypes.

Science.gov (United States)

Lee, Michael S; Menter, David G; Kopetz, Scott

2017-03-01

Although clinical management of colon cancer generally has not accounted for the primary tumor site, left-sided and right-sided colon cancers harbor different clinical and biologic characteristics. Right-sided colon cancers are more likely to have genome-wide hypermethylation via the CpG island methylator phenotype (CIMP), hypermutated state via microsatellite instability, and BRAF mutation. There are also differential exposures to potential carcinogenic toxins and microbiota in the right and left colon. Gene expression analyses further shed light on distinct biologic subtypes of colorectal cancers (CRCs), with 4 consensus molecular subtypes (CMSs) identified. Importantly, these subtypes are differentially distributed between right- and left-sided CRCs, with greater proportions of the "microsatellite unstable/immune" CMS1 and the "metabolic" CMS3 subtypes found in right-sided colon cancers. This review summarizes important biologic distinctions between right- and left-sided CRCs that likely impact prognosis and may predict for differential responses to biologic therapy. Given the inferior prognosis of stage III-IV right-sided CRCs and emerging data suggesting that anti-epidermal growth factor receptor antibody therapy is associated with worse survival in right-sided stage IV CRCs compared with left-sided cancers, these biologic differences between right- and left-sided CRCs provide critical context and may provide opportunities to personalize therapy. Copyright © 2017 by the National Comprehensive Cancer Network.
Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Science.gov (United States)

Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

2015-05-01

Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.
Predicting the distribution pattern of small carnivores in response to environmental factors in the Western Ghats.

Science.gov (United States)

Kalle, Riddhika; Ramesh, Tharmalingam; Qureshi, Qamar; Sankar, Kalyanasundaram

2013-01-01

Due to their secretive habits, predicting the pattern of spatial distribution of small carnivores has been typically challenging, yet for conservation management it is essential to understand the association between this group of animals and environmental factors. We applied maximum entropy modeling (MaxEnt) to build distribution models and identify environmental predictors including bioclimatic variables, forest and land cover type, topography, vegetation index and anthropogenic variables for six small carnivore species in Mudumalai Tiger Reserve. Species occurrence records were collated from camera-traps and vehicle transects during the years 2010 and 2011. We used the average training gain from forty model runs for each species to select the best set of predictors. The area under the curve (AUC) of the receiver operating characteristic plot (ROC) ranged from 0.81 to 0.93 for the training data and 0.72 to 0.87 for the test data. In habitat models for F. chaus, P. hermaphroditus, and H. smithii "distance to village" and precipitation of the warmest quarter emerged as some of the most important variables. "Distance to village" and aspect were important for V. indica while "distance to village" and precipitation of the coldest quarter were significant for H. vitticollis. "Distance to village", precipitation of the warmest quarter and land cover were influential variables in the distribution of H. edwardsii. The map of predicted probabilities of occurrence showed potentially suitable habitats accounting for 46 km(2) of the reserve for F. chaus, 62 km(2) for V. indica, 30 km(2) for P. hermaphroditus, 63 km(2) for H. vitticollis, 45 km(2) for H. smithii and 28 km(2) for H. edwardsii. Habitat heterogeneity driven by the east-west climatic gradient was correlated with the spatial distribution of small carnivores. This study exemplifies the usefulness of modeling small carnivore distribution to prioritize and direct conservation planning for habitat specialists in
Standard biological parts knowledgebase.

Directory of Open Access Journals (Sweden)

Michal Galdzicki

2011-02-01

Full Text Available We have created the Knowledgebase of Standard Biological Parts (SBPkb as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org. The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org. SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL, a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.
Standard Biological Parts Knowledgebase

Science.gov (United States)

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M.; Gennari, John H.

2011-01-01

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate “promoter” parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible. PMID:21390321
Standard biological parts knowledgebase.

Science.gov (United States)

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M; Gennari, John H

2011-02-24

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.
On the origin of distribution patterns of motifs in biological networks

Directory of Open Access Journals (Sweden)

Lesk Arthur M

2008-08-01

Full Text Available Abstract Background Inventories of small subgraphs in biological networks have identified commonly-recurring patterns, called motifs. The inference that these motifs have been selected for function rests on the idea that their occurrences are significantly more frequent than random. Results Our analysis of several large biological networks suggests, in contrast, that the frequencies of appearance of common subgraphs are similar in natural and corresponding random networks. Conclusion Indeed, certain topological features of biological networks give rise naturally to the common appearance of the motifs. We therefore question whether frequencies of occurrences are reasonable evidence that the structures of motifs have been selected for their functional contribution to the operation of networks.
Evaluation of distribution coefficients for the prediction of strontium and cesium migration in a uniform sand

International Nuclear Information System (INIS)

Reynolds, W.D.; Gillham, R.W.; Cherry, J.A.

1982-01-01

The validity of using a distribution coefficient (Ksub(d)) in the mathematical prediction of strontium and cesium transport through uniform saturated sand was investigated by comparing measured breakthrough curves with curves of simulations using the advection-dispersion and the advection equations. Values for Ksub(d) were determined by batch equilibration tests and, indirectly, by fitting the mathematical model to breakthrough data from column experiments. Although the advection-dispersion equation accurately represented the breakthrough curves for two nonreactive solutes (chloride and tritium), neither it nor the advection equation provided close representations of the strontium and cesium curves. The simulated breakthrough curves for strontium and cesium were nearly symmetrical, whereas the data curves were very asymmetrical, with long tails. Column experiments with different pore-water velocities indicated that the shape of the normalized breakthrough curves was not sensitive to velocity. This suggests that the asymmetry of the measured curves was the result of nonlinear partitioning of the cations between the solid and liquid phases, rather than nonequilibrium effects. The results indicate that the distribution coefficient, when used in advection-dispersion models for prediction of the migration of strontium and cesium in field situations, can result in significant error
Prediction of HAMR Debris Population Distribution Released from GEO Space

Science.gov (United States)

Rosengren, A.; Scheeres, D.

2012-09-01

in inclination. When the nodal rate of the system is commensurate with the nodal rate of the Moon, the perturbations build up more effectively over long periods to produce significant effects on the orbit. Such resonances, which occurs for a class of HAMR objects that are not cleared out of orbit, gives rise to strongly changing dynamics over longer time periods. In this paper, we present the averaged model, and discuss its fundamental predictions and comparisons with explicit long-term numerical integrations of HAMR objects in GEO space. Using this tool, we study a range of HAMR objects, released in geostationary orbit, with various area-to-mass ratios, and predict the spatiotemporal distribution of the population. We identified a unique systematic structure associated with their distribution in inclination and ascending node phase space. Given that HAMR objects are the most difficult to target from an observational point of view, this work will have many implications for the space surveillance community, and will allow observers to implement better search strategies for this class of debris.
SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

DEFF Research Database (Denmark)

Guisan, Antoine; Rahbek, Carsten

2011-01-01

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts...
The Distributed Biological Observatory (DBO)-A Change Detection Array in the Pacific Arctic Sector

Science.gov (United States)

Grebmeier, J. M.; Moore, S. E.; Cooper, L. W.; Frey, K. E.; Pickart, R. S.

2011-12-01

The Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature changes. Climate changes are likely to result in shifts in species composition and abundance, northward range expansions, and changes in lower trophic level productivity that can directly cascade and affect the life cycles of higher trophic level organisms. Several regionally critical marine sites in the Pacific Arctic sector that have very high biomass and are focused foraging points for apex predators have been re-occupied during multiple international cruises. The data documenting the importance of these ecosystem "hotspots" provide a growing marine time-series from the northern Bering Sea to Barrow Canyon at the boundary of the Chukchi and Beaufort seas. Results from these studies show spatial changes in carbon production and export to the sediments as indicated by infaunal community composition and biomass, shifts in sediment grain size on a S-to-N latitudinal gradient, and range extensions for lower trophic levels and further northward migration of higher trophic organisms, such as gray whales. There is also direct evidence of negative impacts on ice dependent species, such as walrus and polar bears. To more systematically track the broad biological response to sea ice retreat and associated environmental change, an international consortium of scientists are developing a "Distributed Biological Observatory" (DBO) that includes selected biological measurements at multiple trophic levels. The DBO currently focuses on five regional biological "hotspot" locations along a
Economic Model Predictive Control for Large-Scale and Distributed Energy Systems

DEFF Research Database (Denmark)

Standardi, Laura

Sources (RESs) in the smart grids is increasing. These energy sources bring uncertainty to the production due to their fluctuations. Hence,smart grids need suitable control systems that are able to continuously balance power production and consumption. We apply the Economic Model Predictive Control (EMPC......) strategy to optimise the economic performances of the energy systems and to balance the power production and consumption. In the case of large-scale energy systems, the electrical grid connects a high number of power units. Because of this, the related control problem involves a high number of variables......In this thesis, we consider control strategies for large and distributed energy systems that are important for the implementation of smart grid technologies. An electrical grid has to ensure reliability and avoid long-term interruptions in the power supply. Moreover, the share of Renewable Energy...
Fair Value Versus Historical Cost-Based Valuation for Biological Assets: Predictability of Financial Information

Directory of Open Access Journals (Sweden)

Josep M. Argilés

2011-12-01

Full Text Available There is an intense debate on the convenience of moving from historical cost (HC toward the fair value (FV principle. The debate and academic research is usually concerned with financial instruments, but the IAS 41 requirement of fair valuation for biological assets brings it into the agricultural domain.This paper performs an empirical study with a sample of Spanish farms valuing biological assets at HC and a sample applying FV, finding no significant differences between both valuation methods to assess future cash flows. However, most tests reveal more predictive power of future earnings under fair valuation of biological assets, which is not explained by differences in volatility of earnings and profitability. The study also evidences the existence of flawed HC accounting practices for biological assets in agriculture, which suggests scarce information content of this valuation method in the predominant small business units existing in the agricultural sector in advanced Western countries.La evolución de la contabilidad desde el coste histórico (CH hacia el valor razonable (VR ha suscitado debates y controversias, tanto en el ámbito profesional, como en el académico. Si bien el debate y los estudios se han referido principalmente a los instrumentos financieros, el requerimiento de la NIC41 de valorar los activos biológicos al VR ha ampliado el debate a la contabilidad agrícola.Este trabajo realiza un estudio empírico mediante una muestra de explotaciones agrícolas españolas que valoran sus activos biológicos al CH y otra que valoran al VR, para comparar el poder predictivo de ambos criterios de valoración. No se encuentran diferencias significativas entre ambos criterios para la predicción de los futuros flujos de tesorería. No obstante, la mayor parte de los tests realizados revelan un mayor poder predictivo de los futuros resultados contables bajo el valor razonable, que no se explica en función de diferencias en la
Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

Science.gov (United States)

Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

2015-08-07

Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions.

Science.gov (United States)

Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong

2017-05-01

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Evaluating and Predicting the Effectiveness of Green Infrastructure on a Small Watershed Scale - Emphasis on Water Quality, Flow, Thermal Regime, Substrate Integrity, and Biological Condition

Science.gov (United States)

Assessments of the effectiveness of stormwater best management practices (BMPs) have focused on measurement of load or concentration reductions, which can be translated to predict biological impacts based on chemical water quality criteria. However, many of the impacts of develo...
The fusion of biology, computer science, and engineering: towards efficient and successful synthetic biology.

Science.gov (United States)

Linshiz, Gregory; Goldberg, Alex; Konry, Tania; Hillson, Nathan J

2012-01-01

Synthetic biology is a nascent field that emerged in earnest only around the turn of the millennium. It aims to engineer new biological systems and impart new biological functionality, often through genetic modifications. The design and construction of new biological systems is a complex, multistep process, requiring multidisciplinary collaborative efforts from "fusion" scientists who have formal training in computer science or engineering, as well as hands-on biological expertise. The public has high expectations for synthetic biology and eagerly anticipates the development of solutions to the major challenges facing humanity. This article discusses laboratory practices and the conduct of research in synthetic biology. It argues that the fusion science approach, which integrates biology with computer science and engineering best practices, including standardization, process optimization, computer-aided design and laboratory automation, miniaturization, and systematic management, will increase the predictability and reproducibility of experiments and lead to breakthroughs in the construction of new biological systems. The article also discusses several successful fusion projects, including the development of software tools for DNA construction design automation, recursive DNA construction, and the development of integrated microfluidics systems.
Biological and socio-cultural factors during the school years predicting women’s lifetime educational attainment

Science.gov (United States)

Hendrick, C. Emily; Cohen, Alison K.; Deardorff, Julianna

2015-01-01

BACKGROUND Lifetime educational attainment is an important predictor of health and well-being for women in the United States. In the current study, we examine the roles of socio-cultural factors in youth and an understudied biological life event, pubertal timing, in predicting women’s lifetime educational attainment. METHODS Using data from the National Longitudinal Survey of Youth 1997 cohort (N = 3889), we conducted sequential multivariate linear regression analyses to investigate the influences of macro-level and family-level socio-cultural contextual factors in youth (region of country, urbanicity, race/ethnicity, year of birth, household composition, mother’s education, mother’s age at first birth) and early menarche, a marker of early pubertal development, on women’s educational attainment after age 24. RESULTS Pubertal timing and all socio-cultural factors in youth, other than year of birth, predicted women’s lifetime educational attainment in bivariate models. Family factors had the strongest associations. When family factors were added to multivariate models, geographic region in youth and pubertal timing were no longer significant. CONCLUSION Our findings provide additional evidence that family factors should be considered when developing comprehensive and inclusive interventions in childhood and adolescence to promote lifetime educational attainment among girls. PMID:26830508
Different Predictive Control Strategies for Active Load Management in Distributed Power Systems with High Penetration of Renewable Energy Sources

DEFF Research Database (Denmark)

Zong, Yi; Bindner, Henrik W.; Gehrke, Oliver

2013-01-01

In order to achieve a Danish energy supply based on 100% renewable energy from combinations of wind, biomass, wave and solar power in 2050 and to cover 50% of the Danish electricity consumption by wind power in 2020, it requires more renewable energy in buildings and industries (e.g. cold stores......, greenhouses, etc.), and to coordinate the management of large numbers of distributed energy resources with the smart grid solution. This paper presents different predictive control (Genetic Algorithm-based and Model Predictive Control-based) strategies that schedule controlled loads in the industrial...... and residential sectors, based on dynamic power price and weather forecast, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. Some field tests were carried out on a facility for intelligent, active and distributed power systems, which...
Distributed fiber optic sensor-enhanced detection and prediction of shrinkage-induced delamination of ultra-high-performance concrete overlay

Science.gov (United States)

Bao, Yi; Valipour, Mahdi; Meng, Weina; Khayat, Kamal H.; Chen, Genda

2017-08-01

This study develops a delamination detection system for smart ultra-high-performance concrete (UHPC) overlays using a fully distributed fiber optic sensor. Three 450 mm (length) × 200 mm (width) × 25 mm (thickness) UHPC overlays were cast over an existing 200 mm thick concrete substrate. The initiation and propagation of delamination due to early-age shrinkage of the UHPC overlay were detected as sudden increases and their extension in spatial distribution of shrinkage-induced strains measured from the sensor based on pulse pre-pump Brillouin optical time domain analysis. The distributed sensor is demonstrated effective in detecting delamination openings from microns to hundreds of microns. A three-dimensional finite element model with experimental material properties is proposed to understand the complete delamination process measured from the distributed sensor. The model is validated using the distributed sensor data. The finite element model with cohesive elements for the overlay-substrate interface can predict the complete delamination process.

Confidence limits for Neyman type A-distributed events.

Science.gov (United States)

Morand, Josselin; Deperas-Standylo, Joanna; Urbanik, Witold; Moss, Raymond; Hachem, Sabet; Sauerwein, Wolfgang; Wojcik, Andrzej

2008-01-01

The Neyman type A distribution, a generalised, 'contagious' Poisson distribution, finds application in a number of disciplines such as biology, physics and economy. In radiation biology, it best describes the distribution of chromosomal aberrations in cells that were exposed to neutrons, alpha radiations or heavy ions. Intriguingly, no method has been developed for the calculation of confidence limits (CLs) of Neyman type A-distributed events. Here, an algorithm to calculate the 95% CL of Neyman type A-distributed events is presented. Although it has been developed in response to the requirements of radiation biology, it can find application in other fields of research. The algorithm has been implemented in a PC-based computer program that can be downloaded, free of charge, from www.pu.kielce.pl/ibiol/neta.
Confidence limits for Neyman type A-distributed events

International Nuclear Information System (INIS)

Morand, J.; Deperas-Standylo, J.; Urbanik, W.; Moss, R.; Hachem, S.; Sauerwein, W.; Wojcik, A.

2008-01-01

The Neyman type A distribution, a generalised, 'contagious' Poisson distribution, finds application in a number of disciplines such as biology, physics and economy. In radiation biology, it best describes the distribution of chromosomal aberrations in cells that were exposed to neutrons, alpha radiations or heavy ions. Intriguingly, no method has been developed for the calculation of confidence limits (CLs) of Neyman type A-distributed events. Here, an algorithm to calculate the 95% CL of Neyman type A-distributed events is presented. Although it has been developed in response to the requirements of radiation biology, it can find application in other fields of research. The algorithm has been implemented in a PC-based computer program that can be downloaded, free of charge, from www.pu.kielce.pl/ibiol/neta. (authors)
Introduction to radiation biology

International Nuclear Information System (INIS)

Uma Devi, P.; Satish Rao, B.S.; Nagarathnam, A.

2000-01-01

This book is arranged in a logical sequence, starting from radiation physics and radiation chemistry, followed by molecular, subcellular and cellular effects and going on to the level of organism. Topics covered include applied radiobiology like modifiers of radiosensitivity, predictive assay, health physics, human genetics and radiopharmaceuticals. The topics covered are : 1. Radiation Physics, 2. Detection and Measurement of Radiation, 3. Radiation Chemistry, 4. DNA Damage and Repair, 5. Chromosomal Aberrations and Gene Mutations, 6. Cellular Radiobiology 7. Acute Radiation Effects, 8. Delayed Effects of Radiation, 9. Biological Basis of Radiotherapy, 10. Chemical Modifiers of Radiosensitivity, 11. Hyperthermia, 12. High LET Radiations in Cancer, Therapy, 13. Predictive Assays, 14. Radiation Effects on Embryos, 15. Human Radiation Genetics, 16. Radiolabelled Compounds in Biology and Medicine and 17. Radiological Health
An algal model for predicting attainment of tiered biological criteria of Maine's streams and rivers

Science.gov (United States)

Danielson, Thomas J.; Loftin, Cyndy; Tsomides, Leonidas; DiFranco, Jeanne L.; Connors, Beth; Courtemanch, David L.; Drummond, Francis; Davies, Susan

2012-01-01

State water-quality professionals developing new biological assessment methods often have difficulty relating assessment results to narrative criteria in water-quality standards. An alternative to selecting index thresholds arbitrarily is to include the Biological Condition Gradient (BCG) in the development of the assessment method. The BCG describes tiers of biological community condition to help identify and communicate the position of a water body along a gradient of water quality ranging from natural to degraded. Although originally developed for fish and macroinvertebrate communities of streams and rivers, the BCG is easily adapted to other habitats and taxonomic groups. We developed a discriminant analysis model with stream algal data to predict attainment of tiered aquatic-life uses in Maine's water-quality standards. We modified the BCG framework for Maine stream algae, related the BCG tiers to Maine's tiered aquatic-life uses, and identified appropriate algal metrics for describing BCG tiers. Using a modified Delphi method, 5 aquatic biologists independently evaluated algal community metrics for 230 samples from streams and rivers across the state and assigned a BCG tier (1–6) and Maine water quality class (AA/A, B, C, nonattainment of any class) to each sample. We used minimally disturbed reference sites to approximate natural conditions (Tier 1). Biologist class assignments were unanimous for 53% of samples, and 42% of samples differed by 1 class. The biologists debated and developed consensus class assignments. A linear discriminant model built to replicate a priori class assignments correctly classified 95% of 150 samples in the model training set and 91% of 80 samples in the model validation set. Locally derived metrics based on BCG taxon tolerance groupings (e.g., sensitive, intermediate, tolerant) were more effective than were metrics developed in other regions. Adding the algal discriminant model to Maine's existing macroinvertebrate discriminant
Mass distribution and multiple fragmentation events in high energy cluster-cluster collisions: evidence for a predicted phase transition

International Nuclear Information System (INIS)

Farizon, B.; Farizon, M.; Gaillard, M.J.; Genre, R.; Louc, S.; Martin, J.; Senn, G.; Scheier, P.; Maerk, T.D.

1996-09-01

Fragment size distributions including multiple fragmentation events have been measured for high energy H 25 + cluster ions (60 keV/amu) colliding with a neutral C 60 target. In contrast to earlier collision experiments with a helium target the present studies do not show a U-shaped fragment mass distribution, but a single power-law falloff with increasing fragment mass. This behaviour is similar to what is known for the intermediate regime in nuclear collision physics and thus confirms a recently predicted scaling from nuclear to molecular collisions
The preparation of 125I-β-CIT and its biological distribution in animal

International Nuclear Information System (INIS)

Sun Wenshan; Liu Zhenguo; Shen Minghua; Qian Juan; Li Peiyong; Zhu Chengmo; Chen Shengdi

2000-01-01

Objective: To prepare and label the 125 I-β-CIT and study its biological distribution in animal. Methods: 125 I-β-CIT was prepared by the peracetic acid method and the chloramine-T method, and dopamine transporter (DAT) binding properties of 125 I-β-CIT were examined by in vivo biodistribution and inhibition studies in mice and whole body autoradiography in rats. Results: The radiolabelling yields of the peracetic acid and the chloramine-T methods were (53.4 +- 7.9)% and (88.4 +- 3.49)%, respectively. Following intravenous injection in mice, 125 I-β-CIT showed high accumulation in striatum, time to peak level uptake was 2 h after injection. GBR12909 significantly inhibited 125 I-β-CIT binding in striatum, while clomipramine significantly inhibited 125 I-β-CIT binding in hippocampus and cerebral cortex. The rat whole body autoradiography showed that the clearance of the tracer occurred through the hepatobiliary route. Conclusions: The results indicate β-CIT is an agent suitable for DAT imaging and can be used for the study of Parkinson's disease
Predicting habitat distribution to conserve seagrass threatened by sea level rise

Science.gov (United States)

Saunders, M. I.; Baldock, T.; Brown, C. J.; Callaghan, D. P.; Golshani, A.; Hamylton, S.; Hoegh-guldberg, O.; Leon, J. X.; Lovelock, C. E.; Lyons, M. B.; O'Brien, K.; Mumby, P.; Phinn, S. R.; Roelfsema, C. M.

2013-12-01

Sea level rise (SLR) over the 21st century will cause significant redistribution of valuable coastal habitats. Seagrasses form extensive and highly productive meadows in shallow coastal seas support high biodiversity, including economically valuable and threatened species. Predictive habitat models can inform local management actions that will be required to conserve seagrass faced with multiple stressors. We developed novel modelling approaches, based on extensive field data sets, to examine the effects of sea level rise and other stressors on two representative seagrass habitats in Australia. First, we modelled interactive effects of SLR, water clarity and adjacent land use on estuarine seagrass meadows in Moreton Bay, Southeast Queensland. The extent of suitable seagrass habitat was predicted to decline by 17% by 2100 due to SLR alone, but losses were predicted to be significantly reduced through improvements in water quality (Fig 1a) and by allowing space for seagrass migration with inundation. The rate of sedimentation in seagrass strongly affected the area of suitable habitat for seagrass in sea level rise scenarios (Fig 1b). Further research to understand spatial, temporal and environmental variability of sediment accretion in seagrass is required. Second, we modelled changes in wave energy distribution due to predicted SLR in a linked coral reef and seagrass ecosystem at Lizard Island, Great Barrier Reef. Scenarios where the water depth over the coral reef deepened due to SLR and minimal reef accretion, resulted in larger waves propagating shoreward, changing the existing hydrodynamic conditions sufficiently to reduce area of suitable habitat for seagrass. In a scenario where accretion of the coral reef was severely compromised (e.g. warming, acidification, overfishing), the probability of the presence of seagrass declined significantly. Management to maintain coral health will therefore also benefit seagrasses subject to SLR in reef environments. Further
Maxent modeling for predicting the potential geographical distribution of two peony species under climate change.

Science.gov (United States)

Zhang, Keliang; Yao, Linjun; Meng, Jiasong; Tao, Jun

2018-09-01

Paeonia (Paeoniaceae), an economically important plant genus, includes many popular ornamentals and medicinal plant species used in traditional Chinese medicine. Little is known about the properties of the habitat distribution and the important eco-environmental factors shaping the suitability. Based on high-resolution environmental data for current and future climate scenarios, we modeled the present and future suitable habitat for P. delavayi and P. rockii by Maxent, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. The results showed that the moderate and high suitable areas for P. delavayi and P. rockii encompassed ca. 4.46×10 5 km 2 and 1.89×10 5 km 2 , respectively. Temperature seasonality and isothermality were identified as the most critical factors shaping P. delavayi distribution, and UVB-4 and annual precipitation were identified as the most critical for shaping P. rockii distribution. Under the scenario with a low concentration of greenhouse gas emissions (RCP2.6), the range of both species increased as global warming intensified; however, under the scenario with higher concentrations of emissions (RCP8.5), the suitable habitat range of P. delavayi decreased while P. rockii increased. Overall, our prediction showed that a shift in distribution of suitable habitat to higher elevations would gradually become more significant. The information gained from this study should provide a useful reference for implementing long-term conservation and management strategies for these species. Copyright © 2018. Published by Elsevier B.V.
A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system.

Directory of Open Access Journals (Sweden)

Eric S Walsh

Full Text Available Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment contamination from the sub-estuary to broader estuary extent. For this study, a Random Forest (RF model was implemented to predict the distribution of a model contaminant, triclosan (5-chloro-2-(2,4-dichlorophenoxyphenol (TCS, in Narragansett Bay, Rhode Island, USA. TCS is an unregulated contaminant used in many personal care products. The RF explanatory variables were associated with TCS transport and fate (proxies and direct and indirect environmental entry. The continuous RF TCS concentration predictions were discretized into three levels of contamination (low, medium, and high for three different quantile thresholds. The RF model explained 63% of the variance with a minimum number of variables. Total organic carbon (TOC (transport and fate proxy was a strong predictor of TCS contamination causing a mean squared error increase of 59% when compared to permutations of randomized values of TOC. Additionally, combined sewer overflow discharge (environmental entry and sand (transport and fate proxy were strong predictors. The discretization models identified a TCS area of greatest concern in the northern reach of Narragansett Bay (Providence River sub-estuary, which was validated with independent test samples. This decision-support tool performed well at the sub-estuary extent and provided the means to identify areas of concern and prioritize bay-wide sampling.
Predicting seasonal variations in coastal seabird habitats in the English Channel and the Bay of Biscay

Science.gov (United States)

Virgili, A.; Lambert, C.; Pettex, E.; Dorémus, G.; Van Canneyt, O.; Ridoux, V.

2017-07-01

Seabirds, like all animals, have to live in suitable habitats to fulfil their energetic needs for both somatic and reproductive growth and maintenance. Apart from migration trips, all coastal seabirds are linked to the coast, because they need to return daily to land for resting or breeding. Their use of marine habitats strongly depends on their biology, but also on environmental conditions, and can be described using habitat models. This study aimed to: (1) identify the processes that mostly influence seabird distributions along the coasts of the English Channel and the Bay of Biscay; (2) determine seasonal variations of these processes, (3) provide prediction maps that describe the species distributions. We collected data of coastal seabird sightings from aerial surveys carried out in the English Channel and the eastern North Atlantic in the winter 2011-2012 and summer 2012. We classified seabirds into morphological groups and described their habitats using physiographic and oceanographic variables in Generalised Additive Models (GAMs). Finally, we produced maps of predicted distributions by season for each group. The distributions of coastal seabirds were essentially determined by the distance to the nearest coast, with a weaker influence of oceanographic variables. The nature of the substrate, sand or rock, combined with the timing of reproduction, also contributed to determine seasonal at-sea distributions for some species. The highest densities were predicted near the coast, particularly in bays and estuaries for strictly coastal species with possible variations depending on the season. From this study, we were able to predict the seasonal distribution of the studied species according to varying environmental parameters that changed over time, allowing us to understand better their behaviour and ecology.
Site-specific distribution of claudin-based paracellular channels with roles in biological fluid flow and metabolism.

Science.gov (United States)

Tanaka, Hiroo; Tamura, Atsushi; Suzuki, Koya; Tsukita, Sachiko

2017-10-01

The claudins are a family of membrane proteins with at least 27 members in humans and mice. The extracellular regions of claudin proteins play essential roles in cell-cell adhesion and the paracellular barrier functions of tight junctions (TJs) in epithelial cell sheets. Furthermore, the extracellular regions of some claudins function as paracellular channels in the paracellular barrier that allow the selective passage of water, ions, and/or small organic solutes across the TJ in the extracellular space. Structural analyses have revealed a common framework of transmembrane, cytoplasmic, and extracellular regions among the claudin-based paracellular barriers and paracellular channels; however, differences in the claudins' extracellular regions, such as their charges and conformations, determine their properties. Among the biological systems that involve fluid flow and metabolism, it is noted that hepatic bile flow, renal Na + reabsorption, and intestinal nutrient absorption are dynamically regulated via site-specific distributions of paracellular channel-forming claudins in tissue. Here, we focus on how site-specific distributions of claudin-2- and claudin-15-based paracellular channels drive their organ-specific functions in the liver, kidney, and intestine. © 2017 New York Academy of Sciences.
A coupled diffusion-fluid pressure model to predict cell density distribution for cells encapsulated in a porous hydrogel scaffold under mechanical loading.

Science.gov (United States)

Zhao, Feihu; Vaughan, Ted J; Mc Garrigle, Myles J; McNamara, Laoise M

2017-10-01

Tissue formation within tissue engineering (TE) scaffolds is preceded by growth of the cells throughout the scaffold volume and attachment of cells to the scaffold substrate. It is known that mechanical stimulation, in the form of fluid perfusion or mechanical strain, enhances cell differentiation and overall tissue formation. However, due to the complex multi-physics environment of cells within TE scaffolds, cell transport under mechanical stimulation is not fully understood. Therefore, in this study, we have developed a coupled multiphysics model to predict cell density distribution in a TE scaffold. In this model, cell transport is modelled as a thermal conduction process, which is driven by the pore fluid pressure under applied loading. As a case study, the model is investigated to predict the cell density patterns of pre-osteoblasts MC3T3-e1 cells under a range of different loading regimes, to obtain an understanding of desirable mechanical stimulation that will enhance cell density distribution within TE scaffolds. The results of this study have demonstrated that fluid perfusion can result in a higher cell density in the scaffold region closed to the outlet, while cell density distribution under mechanical compression was similar with static condition. More importantly, the study provides a novel computational approach to predict cell distribution in TE scaffolds under mechanical loading. Copyright © 2017 Elsevier Ltd. All rights reserved.
A mechano-biological model of multi-tissue evolution in bone

Science.gov (United States)

Frame, Jamie; Rohan, Pierre-Yves; Corté, Laurent; Allena, Rachele

2017-12-01

cases and cartilage was shown to lead to the formation of bone in a beam replicating a fracture healing initial tissue distribution. This finding is encouraging in that it is corroborated by similar experimental observations of cartilage leading bone formation during the fracture healing process. The results of this work demonstrate that a multi-tissue mechano-biological model of tissue evolution has the potential for predictive analysis in the design and implementations of implants, describing fracture healing and bone remodeling processes.
Prediction of the Velocity Contours in Triangular Channel with Non-uniform Roughness Distributions by Adaptive Neuro-Fuzzy Inference System

Directory of Open Access Journals (Sweden)

Sara Bardestani

2017-09-01

Full Text Available Triangular channels have different applications in many water and wastewater engineering problems. For this purpose investigating hydraulic characteristics of flow in these sections has great importance. Researchers have presented different prediction methods for the velocity contours in prismatic sections. Most proposed methods are not able to consider the effect of walls roughness, the roughness distribution and secondary flows. However, due to complexity and nonlinearity of velocity contours in open channel flow, there is no simple relationship that can be fully able to exactly draw the velocity contours. In this paper an efficient approach for modeling velocity contours in triangular open channels with non-uniform roughness distributions by Adaptive Neuro-Fuzzy Inference System (ANFIS has been suggested. For training and testing model, the experimental data including 1703 data in triangular channels with geometric symmetry and non-uniform roughness distributions have been used. Comparing experimental results with predicted values by model indicates that ANFIS model is capable to be used in simulation of local velocity and determining velocity contours and the independent evaluation showed that the calculated values of discharge and depth-averaged velocity from model information are precisely in conformity with experimental values.
Using Citizen Science Observations to Model Species Distributions Over Space, Through Time, and Across Scales

Science.gov (United States)

Kelling, S.

2017-12-01

The goal of Biodiversity research is to identify, explain, and predict why a species' distribution and abundance vary through time, space, and with features of the environment. Measuring these patterns and predicting their responses to change are not exercises in curiosity. Today, they are essential tasks for understanding the profound effects that humans have on earth's natural systems, and for developing science-based environmental policies. To gain insight about species' distribution patterns requires studying natural systems at appropriate scales, yet studies of ecological processes continue to be compromised by inadequate attention to scale issues. How spatial and temporal patterns in nature change with scale often reflects fundamental laws of physics, chemistry, or biology, and we can identify such basic, governing laws only by comparing patterns over a wide range of scales. This presentation will provide several examples that integrate bird observations made by volunteers, with NASA Earth Imagery using Big Data analysis techniques to analyze the temporal patterns of bird occurrence across scales—from hemisphere-wide views of bird distributions to the impact of powerful city lights on bird migration.
Radiation physics, biophysics, and radiation biology

International Nuclear Information System (INIS)

Hall, E.J.; Zaider, M.

1993-05-01

Research at the Center for Radiological Research is a multidisciplenary blend of physics, chemistry and biology aimed at understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. The focus is increased on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights of the program from the past year are described. A mathematical model describing the production of single-strand and double-strand breaks in DNA as a function radiation quality has been completed. For the first time Monte Carlo techniques have been used to obtain directly the spatial distribution of DNA moieties altered by radiation. This information was obtained by including the transport codes a realistic description of the electronic structure of DNA. We have investigated structure activity relationships for the potential oncogenicity of a new generation of bioreductive drugs that function as hypoxic cytotoxins. Experimental and theoretical investigation of the inverse dose rate effect, whereby medium LET radiations actually produce an c effect when the dose is protracted, is now at a point where the basic mechanisms are reasonably understood and the complex interplay between dose, dose rate and radiation quality which is necessary for the effect to be present can now be predicted at least in vitro. In terms of early radiobiological damage, a quantitative link has been established between basic energy deposition and locally multiply damaged sites, the radiochemical precursor of DNA double strand breaks; specifically, the spatial and energy deposition requirements necessary to form LMDs have been evaluated. For the first time, a mechanically understood ''biological fingerprint'' of high-LET radiation has been established. Specifically measurement of the ratio of inter-to intra-chromosomal aberrations produces a unique signature from alpha-particles or neutrons
Radiation physics, biophysics, and radiation biology

Energy Technology Data Exchange (ETDEWEB)

Hall, E.J.; Zaider, M.

1993-05-01

Research at the Center for Radiological Research is a multidisciplenary blend of physics, chemistry and biology aimed at understanding the mechanisms involved in the health problems resulting from human exposure to ionizing radiations. The focus is increased on biochemistry and the application of the techniques of molecular biology to the problems of radiation biology. Research highlights of the program from the past year are described. A mathematical model describing the production of single-strand and double-strand breaks in DNA as a function radiation quality has been completed. For the first time Monte Carlo techniques have been used to obtain directly the spatial distribution of DNA moieties altered by radiation. This information was obtained by including the transport codes a realistic description of the electronic structure of DNA. We have investigated structure activity relationships for the potential oncogenicity of a new generation of bioreductive drugs that function as hypoxic cytotoxins. Experimental and theoretical investigation of the inverse dose rate effect, whereby medium LET radiations actually produce an c effect when the dose is protracted, is now at a point where the basic mechanisms are reasonably understood and the complex interplay between dose, dose rate and radiation quality which is necessary for the effect to be present can now be predicted at least in vitro. In terms of early radiobiological damage, a quantitative link has been established between basic energy deposition and locally multiply damaged sites, the radiochemical precursor of DNA double strand breaks; specifically, the spatial and energy deposition requirements necessary to form LMDs have been evaluated. For the first time, a mechanically understood biological fingerprint'' of high-LET radiation has been established. Specifically measurement of the ratio of inter-to intra-chromosomal aberrations produces a unique signature from alpha-particles or neutrons.
Quantification of 2D elemental distribution maps of intermediate-thick biological sections by low energy synchrotron μ-X-ray fluorescence spectrometry

Science.gov (United States)

Kump, P.; Vogel-Mikuš, K.

2018-05-01

Two fundamental-parameter (FP) based models for quantification of 2D elemental distribution maps of intermediate-thick biological samples by synchrotron low energy μ-X-ray fluorescence spectrometry (SR-μ-XRF) are presented and applied to the elemental analysis in experiments with monochromatic focused photon beam excitation at two low energy X-ray fluorescence beamlines—TwinMic, Elettra Sincrotrone Trieste, Italy, and ID21, ESRF, Grenoble, France. The models assume intermediate-thick biological samples composed of measured elements, the sources of the measurable spectral lines, and by the residual matrix, which affects the measured intensities through absorption. In the first model a fixed residual matrix of the sample is assumed, while in the second model the residual matrix is obtained by the iteration refinement of elemental concentrations and an adjusted residual matrix. The absorption of the incident focused beam in the biological sample at each scanned pixel position, determined from the output of a photodiode or a CCD camera, is applied as a control in the iteration procedure of quantification.
A joint calibration model for combining predictive distributions

Directory of Open Access Journals (Sweden)

Patrizia Agati

2013-05-01

Full Text Available In many research fields, as for example in probabilistic weather forecasting, valuable predictive information about a future random phenomenon may come from several, possibly heterogeneous, sources. Forecast combining methods have been developed over the years in order to deal with ensembles of sources: the aim is to combine several predictions in such a way to improve forecast accuracy and reduce risk of bad forecasts.In this context, we propose the use of a Bayesian approach to information combining, which consists in treating the predictive probability density functions (pdfs from the individual ensemble members as data in a Bayesian updating problem. The likelihood function is shown to be proportional to the product of the pdfs, adjusted by a joint “calibration function” describing the predicting skill of the sources (Morris, 1977. In this paper, after rephrasing Morris’ algorithm in a predictive context, we propose to model the calibration function in terms of bias, scale and correlation and to estimate its parameters according to the least squares criterion. The performance of our method is investigated and compared with that of Bayesian Model Averaging (Raftery, 2005 on simulated data.
The role of population inertia in predicting the outcome of stage-structured biological invasions.

Science.gov (United States)

Guiver, Chris; Dreiwi, Hanan; Filannino, Donna-Maria; Hodgson, Dave; Lloyd, Stephanie; Townley, Stuart

2015-07-01

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour-phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. Copyright © 2015. Published by Elsevier Inc.

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