Teaching neurophysiology, neuropharmacology, and experimental design using animal models of psychiatric and neurological disorders.

PubMed

Morsink, Maarten C; Dukers, Danny F

2009-03-01

Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to integrate the teaching of neurophysiology, neuropharmacology, and experimental design. For this purpose, seven case studies were developed in which animal models for several psychiatric and neurological diseases were described and in which neuroactive drugs used to treat or study these diseases were introduced. Groups of undergraduate students were assigned to one of these case studies and asked to give a classroom presentation in which 1) the disease and underlying pathophysiology are described, 2) face and construct validity of the animal model are discussed, and 3) a pharmacological experiment with the associated neuroactive drug to assess predictive validity is presented. After evaluation of the presentations, we found that the students had gained considerable insight into disease phenomenology, its underlying neurophysiology, and the mechanism of action of the neuroactive drug. Moreover, the assignment was very useful in the teaching of experimental design, allowing an in-depth discussion of experimental control groups and the prediction of outcomes in these groups if the animal model were to display predictive validity. Finally, the highly positive responses in the student evaluation forms indicated that the assignment was of great interest to the students. Hence, the currently developed case studies constitute a very useful tool for teaching neurophysiology, neuropharmacology, and experimental design.

Foraging optimally for home ranges

USGS Publications Warehouse

Mitchell, Michael S.; Powell, Roger A.

2012-01-01

Economic models predict behavior of animals based on the presumption that natural selection has shaped behaviors important to an animal's fitness to maximize benefits over costs. Economic analyses have shown that territories of animals are structured by trade-offs between benefits gained from resources and costs of defending them. Intuitively, home ranges should be similarly structured, but trade-offs are difficult to assess because there are no costs of defense, thus economic models of home-range behavior are rare. We present economic models that predict how home ranges can be efficient with respect to spatially distributed resources, discounted for travel costs, under 2 strategies of optimization, resource maximization and area minimization. We show how constraints such as competitors can influence structure of homes ranges through resource depression, ultimately structuring density of animals within a population and their distribution on a landscape. We present simulations based on these models to show how they can be generally predictive of home-range behavior and the mechanisms that structure the spatial distribution of animals. We also show how contiguous home ranges estimated statistically from location data can be misleading for animals that optimize home ranges on landscapes with patchily distributed resources. We conclude with a summary of how we applied our models to nonterritorial black bears (Ursus americanus) living in the mountains of North Carolina, where we found their home ranges were best predicted by an area-minimization strategy constrained by intraspecific competition within a social hierarchy. Economic models can provide strong inference about home-range behavior and the resources that structure home ranges by offering falsifiable, a priori hypotheses that can be tested with field observations.

Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

PubMed

Valdes-Donoso, Pablo; VanderWaal, Kimberly; Jarvis, Lovell S; Wayne, Spencer R; Perez, Andres M

2017-01-01

Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

PubMed

Fraser, Keith; Bruckner, Dylan M; Dordick, Jonathan S

2018-06-18

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Hot limpets: predicting body temperature in a conductance-mediated thermal system.

PubMed

Denny, Mark W; Harley, Christopher D G

2006-07-01

Living at the interface between the marine and terrestrial environments, intertidal organisms may serve as a bellwether for environmental change and a test of our ability to predict its biological consequences. However, current models do not allow us to predict the body temperature of intertidal organisms whose heat budgets are strongly affected by conduction to and from the substratum. Here, we propose a simple heat-budget model of one such animal, the limpet Lottia gigantea, and test the model against measurements made in the field. Working solely from easily measured physical and meteorological inputs, the model predicts the daily maximal body temperatures of live limpets within a fraction of a degree, suggesting that it may be a useful tool for exploring the thermal biology of limpets and for predicting effects of climate change. The model can easily be adapted to predict the temperatures of chitons, acorn barnacles, keyhole limpets, and encrusting animals and plants.

Technical note: Bayesian calibration of dynamic ruminant nutrition models.

PubMed

Reed, K F; Arhonditsis, G B; France, J; Kebreab, E

2016-08-01

Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need for robust mechanistic modeling tools that can accommodate error analysis by remaining within the bounds of data-based parameter estimation. For the purpose of prediction, the Bayesian approach generates a posterior predictive distribution that represents the current estimate of the value of the response variable, taking into account both the uncertainty about the parameters and model residual variability. Predictions are expressed as probability distributions, thereby conveying significantly more information than point estimates in regard to uncertainty. Our study illustrates some of the technical advantages of Bayesian calibration and discusses the future perspectives in the context of animal nutrition modeling. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Anthrax vaccine-induced antibodies provide cross-species prediction of survival to aerosol challenge.

PubMed

Fay, Michael P; Follmann, Dean A; Lynn, Freyja; Schiffer, Jarad M; Stark, Gregory V; Kohberger, Robert; Quinn, Conrad P; Nuzum, Edwin O

2012-09-12

Because clinical trials to assess the efficacy of vaccines against anthrax are not ethical or feasible, licensure for new anthrax vaccines will likely involve the Food and Drug Administration's "Animal Rule," a set of regulations that allow approval of products based on efficacy data only in animals combined with immunogenicity and safety data in animals and humans. U.S. government-sponsored animal studies have shown anthrax vaccine efficacy in a variety of settings. We examined data from 21 of those studies to determine whether an immunological bridge based on lethal toxin neutralization activity assay (TNA) can predict survival against an inhalation anthrax challenge within and across species and genera. The 21 studies were classified into 11 different settings, each of which had the same animal species, vaccine type and formulation, vaccination schedule, time of TNA measurement, and challenge time. Logistic regression models determined the contribution of vaccine dilution dose and TNA on prediction of survival. For most settings, logistic models using only TNA explained more than 75% of the survival effect of the models with dose additionally included. Cross-species survival predictions using TNA were compared to the actual survival and shown to have good agreement (Cohen's κ ranged from 0.55 to 0.78). In one study design, cynomolgus macaque data predicted 78.6% survival in rhesus macaques (actual survival, 83.0%) and 72.6% in rabbits (actual survival, 64.6%). These data add support for the use of TNA as an immunological bridge between species to extrapolate data in animals to predict anthrax vaccine effectiveness in humans.

PREDICTING CLIMATE-INDUCED RANGE SHIFTS: MODEL DIFFERENCES AND MODEL RELIABILITY

EPA Science Inventory

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common ...

A reexamination of age-related variation in body weight and morphometry of Maryland nutria

USGS Publications Warehouse

Sherfy, M.H.; Mollett, T.A.; McGowan, K.R.; Daugherty, S.L.

2006-01-01

Age-related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross-validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model-building data set (n = 192-193 M, 217-222 F) and evaluated them with the remaining animals (n = 487-488 M, 642-647 F). We used nonlinear regression to develop Gompertz growth-curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log-transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age-related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live-captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live-captured animals.

Teaching Neurophysiology, Neuropharmacology, and Experimental Design Using Animal Models of Psychiatric and Neurological Disorders

ERIC Educational Resources Information Center

Morsink, Maarten C.; Dukers, Danny F.

2009-01-01

Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to…

Mass and energy budgets of animals: Behavioral and ecological implications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Porter, W.P.

1991-11-01

The two major aims of our lab are as follows: First, to develop and field-test general mechanistic models that predict animal life history characteristics as influenced by climate and the physical, physiological behavioral characteristics of species. This involves: understanding how animal time and energy budgets are affected by climate and animal properties; predicting growth and reproductive potential from time and energy budgets; predicting mortality based on climate and time and energy budgets; and linking these individual based models to population dynamics. Second to conduct empirical studies of animal physiological ecology, particularly the effects of temperature on time and energy budgets.more » The physiological ecology of individual animals is the key link between the physical environment and population-level phenomena. We address the macroclimate to microclimate linkage on a broad spatial scale; address the links between individuals and population dynamics for lizard species; test the endotherm energetics and behavior model using beaver; address the spatial variation in climate and its effects on individual energetics, growth and reproduction; and address patchiness in the environment and constraints they may impose on individual energetics, growth and reproduction. These projects are described individually in the following section. 24 refs., 9 figs.« less

Examining speed versus selection in connectivity models using elk migration as an example

USGS Publications Warehouse

Brennan, Angela; Hanks, Ephraim M.; Merkle, Jerod A.; Cole, Eric K.; Dewey, Sarah R.; Courtemanch, Alyson B.; Cross, Paul C.

2018-01-01

ContextLandscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.ObjectiveTo compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.MethodsUsing movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.ResultsAll connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.ConclusionsCTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths.

PubMed

Panzacchi, Manuela; Van Moorter, Bram; Strand, Olav; Saerens, Marco; Kivimäki, Ilkka; St Clair, Colleen C; Herfindal, Ivar; Boitani, Luigi

2016-01-01

The loss, fragmentation and degradation of habitat everywhere on Earth prompts increasing attention to identifying landscape features that support animal movement (corridors) or impedes it (barriers). Most algorithms used to predict corridors assume that animals move through preferred habitat either optimally (e.g. least cost path) or as random walkers (e.g. current models), but neither extreme is realistic. We propose that corridors and barriers are two sides of the same coin and that animals experience landscapes as spatiotemporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfil specific ecological processes. Based on this conceptual framework, we propose a novel methodological approach that uses high-resolution individual-based movement data to predict corridor-barrier continua with increased realism. Our approach consists of two innovations. First, we use step selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly, we introduce to movement ecology the randomized shortest path algorithm (RSP) which operates on friction maps to predict the corridor-barrier continuum for strategic movements between functional areas. By modulating the parameter Ѳ, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Ѳ approaches infinity, RSP is equivalent to LCP) or random walk (when Ѳ → 0, RSP → current models). Using this approach, we identify migration corridors for GPS-monitored wild reindeer (Rangifer t. tarandus) in Norway. We demonstrate that reindeer movement is best predicted by an intermediate value of Ѳ, indicative of a movement trade-off between optimization and exploration. Model calibration allows identification of a corridor-barrier continuum that closely fits empirical data and demonstrates that RSP outperforms models that assume either optimality or random walk. The proposed approach models the multiscale cognitive maps by which animals likely navigate real landscapes and generalizes the most common algorithms for identifying corridors. Because suboptimal, but non-random, movement strategies are likely widespread, our approach has the potential to predict more realistic corridor-barrier continua for a wide range of species. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Examining speed versus selection in connectivity models using elk migration as an example

USGS Publications Warehouse

Brennan, Angela; Hanks, EM; Merkle, JA; Cole, EK; Dewey, SR; Courtemanch, AB; Cross, Paul C.

2018-01-01

Context: Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity. Objective: To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection. Methods: Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements. Results: All models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP algorithms. Conclusions: CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Development and application of a population physiologically based pharmacokinetic model for penicillin G in swine and cattle for food safety assessment.

PubMed

Li, Miao; Gehring, Ronette; Riviere, Jim E; Lin, Zhoumeng

2017-09-01

Penicillin G is a widely used antimicrobial in food-producing animals, and one of the most predominant drug residues in animal-derived food products. Due to reduced sensitivity of bacteria to penicillin, extralabel use of penicillin G is common, which may lead to violative residues in edible tissues and cause adverse reactions in consumers. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict drug residues in edible tissues and estimate extended withdrawal intervals for penicillin G in swine and cattle. A flow-limited PBPK model was developed with data from Food Animal Residue Avoidance Databank using Berkeley Madonna. The model predicted observed drug concentrations in edible tissues, including liver, muscle, and kidney for penicillin G both in swine and cattle well, including data not used in model calibration. For extralabel use (5× and 10× label dose) of penicillin G, Monte Carlo sampling technique was applied to predict times needed for tissue concentrations to fall below established tolerances for the 99th percentile of the population. This model provides a useful tool to predict tissue residues of penicillin G in swine and cattle to aid food safety assessment, and also provide a framework for extrapolation to other food animal species. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

PubMed

Lourenco, D A L; Tsuruta, S; Fragomeni, B O; Masuda, Y; Aguilar, I; Legarra, A; Bertrand, J K; Amen, T S; Wang, L; Moser, D W; Misztal, I

2015-06-01

Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE-BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

Reviewing model application to support animal health decision making.

PubMed

Singer, Alexander; Salman, Mo; Thulke, Hans-Hermann

2011-04-01

Animal health is of societal importance as it affects human welfare, and anthropogenic interests shape decision making to assure animal health. Scientific advice to support decision making is manifold. Modelling, as one piece of the scientific toolbox, is appreciated for its ability to describe and structure data, to give insight in complex processes and to predict future outcome. In this paper we study the application of scientific modelling to support practical animal health decisions. We reviewed the 35 animal health related scientific opinions adopted by the Animal Health and Animal Welfare Panel of the European Food Safety Authority (EFSA). Thirteen of these documents were based on the application of models. The review took two viewpoints, the decision maker's need and the modeller's approach. In the reviewed material three types of modelling questions were addressed by four specific model types. The correspondence between tasks and models underpinned the importance of the modelling question in triggering the modelling approach. End point quantifications were the dominating request from decision makers, implying that prediction of risk is a major need. However, due to knowledge gaps corresponding modelling studies often shed away from providing exact numbers. Instead, comparative scenario analyses were performed, furthering the understanding of the decision problem and effects of alternative management options. In conclusion, the most adequate scientific support for decision making - including available modelling capacity - might be expected if the required advice is clearly stated. Copyright © 2011 Elsevier B.V. All rights reserved.

Age- and sex-dependent regression models for predicting the live weight of West African Dwarf goat from body measurements.

PubMed

Sowande, O S; Oyewale, B F; Iyasere, O S

2010-06-01

The relationships between live weight and eight body measurements of West African Dwarf (WAD) goats were studied using 211 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), and length of hindquarter (LHQ) were fitted into simple linear, allometric, and multiple-regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight, HG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple-regression model, HG and HL best fit the model for goat kids; HG, HW, and HL for goat aged 13-24 months; while HG, LHQ, HW, and HL best fit the model for goats aged 25-36 months. Coefficients of determination (R(2)) values for linear and allometric models for predicting the live weight of WAD goat increased with age in all the body measurements, with HG being the most satisfactory single measurement in predicting the live weight of WAD goat. Sex had significant influence on the model with R(2) values consistently higher in females except the models for LHQ and HW.

Prediction of a Therapeutic Dose for Buagafuran, a Potent Anxiolytic Agent by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Starting from Pharmacokinetics in Rats and Human.

PubMed

Yang, Fen; Wang, Baolian; Liu, Zhihao; Xia, Xuejun; Wang, Weijun; Yin, Dali; Sheng, Li; Li, Yan

2017-01-01

Physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) models can contribute to animal-to-human extrapolation and therapeutic dose predictions. Buagafuran is a novel anxiolytic agent and phase I clinical trials of buagafuran have been completed. In this paper, a potentially effective dose for buagafuran of 30 mg t.i.d. in human was estimated based on the human brain concentration predicted by a PBPK/PD modeling. The software GastroPlus TM was used to build the PBPK/PD model for buagafuran in rat which related the brain tissue concentrations of buagafuran and the times of animals entering the open arms in the pharmacological model of elevated plus-maze. Buagafuran concentrations in human plasma were fitted and brain tissue concentrations were predicted by using a human PBPK model in which the predicted plasma profiles were in good agreement with observations. The results provided supportive data for the rational use of buagafuran in clinic.

Predicting climate-induced range shifts: model differences and model reliability.

Treesearch

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Predictive Model of Systemic Toxicity (SOT)

EPA Science Inventory

In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

Advancing research on animal-transported subsidies by integrating animal movement and ecosystem modelling.

PubMed

Earl, Julia E; Zollner, Patrick A

2017-09-01

Connections between ecosystems via animals (active subsidies) support ecosystem services and contribute to numerous ecological effects. Thus, the ability to predict the spatial distribution of active subsidies would be useful for ecology and conservation. Previous work modelling active subsidies focused on implicit space or static distributions, which treat passive and active subsidies similarly. Active subsidies are fundamentally different from passive subsidies, because animals can respond to the process of subsidy deposition and ecosystem changes caused by subsidy deposition. We propose addressing this disparity by integrating animal movement and ecosystem ecology to advance active subsidy investigations, make more accurate predictions of subsidy spatial distributions, and enable a mechanistic understanding of subsidy spatial distributions. We review selected quantitative techniques that could be used to accomplish integration and lead to novel insights. The ultimate objective for these types of studies is predictions of subsidy spatial distributions from characteristics of the subsidy and the movement strategy employed by animals that transport subsidies. These advances will be critical in informing the management of ecosystem services, species conservation and ecosystem degradation related to active subsidies. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

The utility of animal models to evaluate novel anti-obesity agents

PubMed Central

Vickers, Steven P; Jackson, Helen C; Cheetham, Sharon C

2011-01-01

The global incidence of obesity continues to rise and is a major driver of morbidity and mortality through cardiovascular and cerebrovascular diseases. Animal models used in the discovery of novel treatments for obesity range from straightforward measures of food intake in lean rodents to long-term studies in animals exhibiting obesity due to the continuous access to diets high in fat. The utility of these animal models can be extended to determine, for example, that weight loss is due to fat loss and/or assess whether beneficial changes in key plasma parameters (e.g. insulin) are evident. In addition, behavioural models such as the behavioural satiety sequence can be used to confirm that a drug treatment has a selective effect on food intake. Typically, animal models have excellent predictive validity whereby drug-induced weight loss in rodents subsequently translates to weight loss in man. However, despite this, at the time of writing orlistat (Europe; USA) remains the only drug currently marketed for the treatment of obesity, with sibutramine having recently been withdrawn from sale globally due to the increased incidence of serious, non-fatal cardiovascular events. While the utility of rodent models in predicting clinical weight loss is detailed, the review also discusses whether animals can be used to predict adverse events such as those seen with recent anti-obesity drugs in the clinic. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21265828

Empirical prediction of net splanchnic release of ketogenic nutrients, acetate, butyrate and β-hydroxybutyrate in ruminants: a meta-analysis.

PubMed

Loncke, C; Nozière, P; Bahloul, L; Vernet, J; Lapierre, H; Sauvant, D; Ortigues-Marty, I

2015-03-01

For energy feeding systems for ruminants to evolve towards a nutrient-based system, dietary energy supply has to be determined in terms of amount and nature of nutrients. The objective of this study was to establish response equations of the net hepatic flux and net splanchnic release of acetate, butyrate and β-hydroxybutyrate to changes in diet and animal profiles. A meta-analysis was applied on published data compiled from the FLuxes of nutrients across Organs and tissues in Ruminant Animals database, which pools the results from international publications on net splanchnic nutrient fluxes measured in multi-catheterized ruminants. Prediction variables were identified from current knowledge on digestion, hepatic and other tissue metabolism. Subsequently, physiological and other, more integrative, predictors were obtained. Models were established for intakes up to 41 g dry matter per kg BW per day and diets containing up to 70 g concentrate per 100 g dry matter. Models predicted the net hepatic fluxes or net splanchnic release of each nutrient from its net portal appearance and the animal profile. Corrections were applied to account for incomplete hepatic recovery of the blood flow marker, para-aminohippuric acid. Changes in net splanchnic release (mmol/kg BW per hour) could then be predicted by combining the previously published net portal appearance models and the present net hepatic fluxes models. The net splanchnic release of acetate and butyrate were thus predicted from the intake of ruminally fermented organic matter (RfOM) and the nature of RfOM (acetate: residual mean square error (RMSE)=0.18; butyrate: RMSE=0.01). The net splanchnic release of β-hydroxybutyrate was predicted from RfOM intake and the energy balance of the animals (RMSE=0.035), or from the net portal appearance of butyrate and the energy balance of the animals (RMSE=0.050). Models obtained were independent of ruminant species, and presented low interfering factors on the residuals, least square means or individual slopes. The model equations highlighted the importance of considering the physiological state of animals when predicting splanchnic metabolism. This work showed that it is possible to use simple predictors to accurately predict the amount and nature of ketogenic nutrients released towards peripheral tissues in both sheep and cattle at different physiological status. These results provide deeper insight into biological processes and will contribute to the development of improved tools for dietary formulation.

Mathematics as a Conduit for Translational Research in Post-Traumatic Osteoarthritis

PubMed Central

Ayati, Bruce P.; Kapitanov, Georgi I.; Coleman, Mitchell C.; Anderson, Donald D.; Martin, James A.

2016-01-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a “conduit of translation”. The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. PMID:27653021

The Roles of Mental Animations and External Animations in Understanding Mechanical Systems

ERIC Educational Resources Information Center

Hegarty, Mary; Kriz, Sarah; Cate, Christina

2003-01-01

The effects of computer animations and mental animation on people's mental models of a mechanical system are examined. In 3 experiments, students learned how a mechanical system works from various instructional treatments including viewing a static diagram of the machine, predicting motion from static diagrams, viewing computer animations, and…

Designing effective animations for computer science instruction

NASA Astrophysics Data System (ADS)

Grillmeyer, Oliver

This study investigated the potential for animations of Scheme functions to help novice computer science students understand difficult programming concepts. These animations used an instructional framework inspired by theories of constructivism and knowledge integration. The framework had students make predictions, reflect, and specify examples to animate to promote autonomous learning and result in more integrated knowledge. The framework used animated pivotal cases to help integrate disconnected ideas and restructure students' incomplete ideas by illustrating weaknesses in their existing models. The animations scaffolded learners, making the thought processes of experts more visible by modeling complex and tacit information. The animation design was guided by prior research and a methodology of design and refinement. Analysis of pilot studies led to the development of four design concerns to aid animation designers: clearly illustrate the mapping between objects in animations with the actual objects they represent, show causal connections between elements, draw attention to the salient features of the modeled system, and create animations that reduce complexity. Refined animations based on these design concerns were compared to computer-based tools, text-based instruction, and simpler animations that do not embody the design concerns. Four studies comprised this dissertation work. Two sets of animated presentations of list creation functions were compared to control groups. No significant differences were found in support of animations. Three different animated models of traces of recursive functions ranging from concrete to abstract representations were compared. No differences in learning gains were found between the three models in test performance. Three models of animations of applicative operators were compared with students using the replacement modeler and the Scheme interpreter. Significant differences were found favoring animations that addressed causality and salience in their design. Lastly, two binary tree search algorithm animations designed to reduce complexity were compared with hand-tracing of calls. Students made fewer mistakes in predicting the tree traversal when guided by the animations. However, the posttest findings were inconsistent. In summary, animations designed based on the design concerns did not consistently add value to instruction in the form investigated in this research.

In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

PubMed

Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

2015-01-01

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Technical note: Equivalent genomic models with a residual polygenic effect.

PubMed

Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R

2016-03-01

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Current nonclinical testing paradigm enables safe entry to First-In-Human clinical trials: The IQ consortium nonclinical to clinical translational database.

PubMed

Monticello, Thomas M; Jones, Thomas W; Dambach, Donna M; Potter, David M; Bolt, Michael W; Liu, Maggie; Keller, Douglas A; Hart, Timothy K; Kadambi, Vivek J

2017-11-01

The contribution of animal testing in drug development has been widely debated and challenged. An industry-wide nonclinical to clinical translational database was created to determine how safety assessments in animal models translate to First-In-Human clinical risk. The blinded database was composed of 182 molecules and contained animal toxicology data coupled with clinical observations from phase I human studies. Animal and clinical data were categorized by organ system and correlations determined. The 2×2 contingency table (true positive, false positive, true negative, false negative) was used for statistical analysis. Sensitivity was 48% with a 43% positive predictive value (PPV). The nonhuman primate had the strongest performance in predicting adverse effects, especially for gastrointestinal and nervous system categories. When the same target organ was identified in both the rodent and nonrodent, the PPV increased. Specificity was 84% with an 86% negative predictive value (NPV). The beagle dog had the strongest performance in predicting an absence of clinical adverse effects. If no target organ toxicity was observed in either test species, the NPV increased. While nonclinical studies can demonstrate great value in the PPV for certain species and organ categories, the NPV was the stronger predictive performance measure across test species and target organs indicating that an absence of toxicity in animal studies strongly predicts a similar outcome in the clinic. These results support the current regulatory paradigm of animal testing in supporting safe entry to clinical trials and provide context for emerging alternate models. Copyright © 2017 Elsevier Inc. All rights reserved.

Early Identification of Molecular Predictors of Heterotopic Ossification Following Extremity Blast Injury with a Biomarker Assay

DTIC Science & Technology

2018-03-01

biomarkers were identified by correlation between animals exhibiting radiographic evidence of HO. 15. SUBJECT TERMS Heterotopic ossification, blast...the animal model that predict the occurrence of HO in our experimental animals and determine if a correlation exists to similarly predict the...impact on other disciplines? Up-regulation of genes in the Sprague-Dawley rat contributing to fibrosis and inflammation have been correlated with the

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Whole-genome regression and prediction methods applied to plant and animal breeding.

PubMed

de Los Campos, Gustavo; Hickey, John M; Pong-Wong, Ricardo; Daetwyler, Hans D; Calus, Mario P L

2013-02-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

PubMed Central

de los Campos, Gustavo; Hickey, John M.; Pong-Wong, Ricardo; Daetwyler, Hans D.; Calus, Mario P. L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. PMID:22745228

Simulating polar bear energetics during a seasonal fast using a mechanistic model.

PubMed

Mathewson, Paul D; Porter, Warren P

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal's energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change.

Lift calculations based on accepted wake models for animal flight are inconsistent and sensitive to vortex dynamics.

PubMed

Gutierrez, Eric; Quinn, Daniel B; Chin, Diana D; Lentink, David

2016-12-06

There are three common methods for calculating the lift generated by a flying animal based on the measured airflow in the wake. However, these methods might not be accurate according to computational and robot-based studies of flapping wings. Here we test this hypothesis for the first time for a slowly flying Pacific parrotlet in still air using stereo particle image velocimetry recorded at 1000 Hz. The bird was trained to fly between two perches through a laser sheet wearing laser safety goggles. We found that the wingtip vortices generated during mid-downstroke advected down and broke up quickly, contradicting the frozen turbulence hypothesis typically assumed in animal flight experiments. The quasi-steady lift at mid-downstroke was estimated based on the velocity field by applying the widely used Kutta-Joukowski theorem, vortex ring model, and actuator disk model. The calculated lift was found to be sensitive to the applied model and its different parameters, including vortex span and distance between the bird and laser sheet-rendering these three accepted ways of calculating weight support inconsistent. The three models predict different aerodynamic force values mid-downstroke compared to independent direct measurements with an aerodynamic force platform that we had available for the same species flying over a similar distance. Whereas the lift predictions of the Kutta-Joukowski theorem and the vortex ring model stayed relatively constant despite vortex breakdown, their values were too low. In contrast, the actuator disk model predicted lift reasonably accurately before vortex breakdown, but predicted almost no lift during and after vortex breakdown. Some of these limitations might be better understood, and partially reconciled, if future animal flight studies report lift calculations based on all three quasi-steady lift models instead. This would also enable much needed meta studies of animal flight to derive bioinspired design principles for quasi-steady lift generation with flapping wings.

The Nuremberg Code subverts human health and safety by requiring animal modeling

PubMed Central

2012-01-01

Background The requirement that animals be used in research and testing in order to protect humans was formalized in the Nuremberg Code and subsequent national and international laws, codes, and declarations. Discussion We review the history of these requirements and contrast what was known via science about animal models then with what is known now. We further analyze the predictive value of animal models when used as test subjects for human response to drugs and disease. We explore the use of animals for models in toxicity testing as an example of the problem with using animal models. Summary We conclude that the requirements for animal testing found in the Nuremberg Code were based on scientifically outdated principles, compromised by people with a vested interest in animal experimentation, serve no useful function, increase the cost of drug development, and prevent otherwise safe and efficacious drugs and therapies from being implemented. PMID:22769234

The Nuremberg Code subverts human health and safety by requiring animal modeling.

PubMed

Greek, Ray; Pippus, Annalea; Hansen, Lawrence A

2012-07-08

The requirement that animals be used in research and testing in order to protect humans was formalized in the Nuremberg Code and subsequent national and international laws, codes, and declarations. We review the history of these requirements and contrast what was known via science about animal models then with what is known now. We further analyze the predictive value of animal models when used as test subjects for human response to drugs and disease. We explore the use of animals for models in toxicity testing as an example of the problem with using animal models. We conclude that the requirements for animal testing found in the Nuremberg Code were based on scientifically outdated principles, compromised by people with a vested interest in animal experimentation, serve no useful function, increase the cost of drug development, and prevent otherwise safe and efficacious drugs and therapies from being implemented.

QUANTITATIVE MODELING APPROACHES TO PREDICTING THE ACUTE NEUROTOXICITY OF VOLATILE ORGANIC COMPOUNDS (VOCS).

EPA Science Inventory

Lack of complete and appropriate human data requires prediction of the hazards for exposed human populations by extrapolation from available animal and in vitro data. Predictive models for the toxicity of chemicals can be constructed by linking kinetic and mode of action data uti...

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle.

PubMed

Ayres, D R; Pereira, R J; Boligon, A A; Silva, F F; Schenkel, F S; Roso, V M; Albuquerque, L G

2013-12-01

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Model determination in a case of heterogeneity of variance using sampling techniques.

PubMed

Varona, L; Moreno, C; Garcia-Cortes, L A; Altarriba, J

1997-01-12

A sampling determination procedure has been described in a case of heterogeneity of variance. The procedure makes use of the predictive distributions of each data given the rest of the data and the structure of the assumed model. The computation of these predictive distributions is carried out using a Gibbs Sampling procedure. The final criterion to compare between models is the Mean Square Error between the expectation of predictive distributions and real data. The procedure has been applied to a data set of weight at 210 days in the Spanish Pirenaica beef cattle breed. Three proposed models have been compared: (a) Single Trait Animal Model; (b) Heterogeneous Variance Animal Model; and (c) Multiple Trait Animal Model. After applying the procedure, the most adjusted model was the Heterogeneous Variance Animal Model. This result is probably due to a compromise between the complexity of the model and the amount of available information. The estimated heritabilities under the preferred model have been 0.489 ± 0.076 for males and 0.331 ± 0.082 for females. RESUMEN: Contraste de modelos en un caso de heterogeneidad de varianzas usando métodos de muestreo Se ha descrito un método de contraste de modelos mediante técnicas de muestreo en un caso de heterogeneidad de varianza entre sexos. El procedimiento utiliza las distribucviones predictivas de cada dato, dado el resto de datos y la estructura del modelo. El criterio para coparar modelos es el error cuadrático medio entre la esperanza de las distribuciones predictivas y los datos reales. El procedimiento se ha aplicado en datos de peso a los 210 días en la raza bovina Pirenaica. Se han propuesto tres posibles modelos: (a) Modelo Animal Unicaracter; (b) Modelo Animal con Varianzas Heterogéneas; (c) Modelo Animal Multicaracter. El modelo mejor ajustado fue el Modelo Animal con Varianzas Heterogéneas. Este resultado es probablemente debido a un compromiso entre la complejidad del modelo y la cantidad de datos disponibles. Las heredabilidades estimadas bajo el modelo preferido han sido 0,489 ± 0,076 en los machos y 0,331 ± 0,082 en las hembras. 1997 Blackwell Verlag GmbH.

From Prototypes to Caricatures: Geometrical Models for Concept Typicality

ERIC Educational Resources Information Center

Ameel, Eef; Storms, Gert

2006-01-01

In three studies, we investigated to what extent a geometrical representation in a psychological space succeeds in predicting typicality in animal, natural food and artifact concepts and whether contrast categories contribute to the prediction. In Study 1, we compared the predictive value of a family resemblance-based prototype model with a…

[Prediction of 137Cs accumulation in animal products in the territory of Semipalatinsk test site].

PubMed

Spiridonov, S I; Gontarenko, I A; Mukusheva, M K; Fesenko, S V; Semioshkina, N A

2005-01-01

The paper describes mathematical models for 137Cs behavior in the organism of horses and sheep pasturing on the bording area to the testing area "Ground Zero" of the Semipalatinsk Test Site. The models are parameterized on the base of the data from an experiment with the breeds of animals now commonly encountered within the Semipalatinsk Test Site. The predictive calculations with the models devised have shown that 137Cs concentrations in milk of horses and sheep pasturingon the testing area to "Ground Zero" can exceed the adopted standards during a long period of time.

Nonlinear cancer response at ultralow dose: a 40800-animal ED(001) tumor and biomarker study.

PubMed

Bailey, George S; Reddy, Ashok P; Pereira, Clifford B; Harttig, Ulrich; Baird, William; Spitsbergen, Jan M; Hendricks, Jerry D; Orner, Gayle A; Williams, David E; Swenberg, James A

2009-07-01

Assessment of human cancer risk from animal carcinogen studies is severely limited by inadequate experimental data at environmentally relevant exposures and by procedures requiring modeled extrapolations many orders of magnitude below observable data. We used rainbow trout, an animal model well-suited to ultralow-dose carcinogenesis research, to explore dose-response down to a targeted 10 excess liver tumors per 10000 animals (ED(001)). A total of 40800 trout were fed 0-225 ppm dibenzo[a,l]pyrene (DBP) for 4 weeks, sampled for biomarker analyses, and returned to control diet for 9 months prior to gross and histologic examination. Suspect tumors were confirmed by pathology, and resulting incidences were modeled and compared to the default EPA LED(10) linear extrapolation method. The study provided observed incidence data down to two above-background liver tumors per 10000 animals at the lowest dose (that is, an unmodeled ED(0002) measurement). Among nine statistical models explored, three were determined to fit the liver data well-linear probit, quadratic logit, and Ryzin-Rai. None of these fitted models is compatible with the LED(10) default assumption, and all fell increasingly below the default extrapolation with decreasing DBP dose. Low-dose tumor response was also not predictable from hepatic DBP-DNA adduct biomarkers, which accumulated as a power function of dose (adducts = 100 x DBP(1.31)). Two-order extrapolations below the modeled tumor data predicted DBP doses producing one excess cancer per million individuals (ED(10)(-6)) that were 500-1500-fold higher than that predicted by the five-order LED(10) extrapolation. These results are considered specific to the animal model, carcinogen, and protocol used. They provide the first experimental estimation in any model of the degree of conservatism that may exist for the EPA default linear assumption for a genotoxic carcinogen.

Predictive validity of behavioural animal models for chronic pain

PubMed Central

Berge, Odd-Geir

2011-01-01

Rodent models of chronic pain may elucidate pathophysiological mechanisms and identify potential drug targets, but whether they predict clinical efficacy of novel compounds is controversial. Several potential analgesics have failed in clinical trials, in spite of strong animal modelling support for efficacy, but there are also examples of successful modelling. Significant differences in how methods are implemented and results are reported means that a literature-based comparison between preclinical data and clinical trials will not reveal whether a particular model is generally predictive. Limited reports on negative outcomes prevents reliable estimate of specificity of any model. Animal models tend to be validated with standard analgesics and may be biased towards tractable pain mechanisms. But preclinical publications rarely contain drug exposure data, and drugs are usually given in high doses and as a single administration, which may lead to drug distribution and exposure deviating significantly from clinical conditions. The greatest challenge for predictive modelling is, however, the heterogeneity of the target patient populations, in terms of both symptoms and pharmacology, probably reflecting differences in pathophysiology. In well-controlled clinical trials, a majority of patients shows less than 50% reduction in pain. A model that responds well to current analgesics should therefore predict efficacy only in a subset of patients within a diagnostic group. It follows that successful translation requires several models for each indication, reflecting critical pathophysiological processes, combined with data linking exposure levels with effect on target. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21371010

Use, misuse and extensions of "ideal gas" models of animal encounter.

PubMed

Hutchinson, John M C; Waser, Peter M

2007-08-01

Biologists have repeatedly rediscovered classical models from physics predicting collision rates in an ideal gas. These models, and their two-dimensional analogues, have been used to predict rates and durations of encounters among animals or social groups that move randomly and independently, given population density, velocity, and distance at which an encounter occurs. They have helped to separate cases of mixed-species association based on behavioural attraction from those that simply reflect high population densities, and to detect cases of attraction or avoidance among conspecifics. They have been used to estimate the impact of population density, speeds of movement and size on rates of encounter between members of the opposite sex, between gametes, between predators and prey, and between observers and the individuals that they are counting. One limitation of published models has been that they predict rates of encounter, but give no means of determining whether observations differ significantly from predictions. Another uncertainty is the robustness of the predictions when animal movements deviate from the model's assumptions in specific, biologically relevant ways. Here, we review applications of the ideal gas model, derive extensions of the model to cover some more realistic movement patterns, correct several errors that have arisen in the literature, and show how to generate confidence limits for expected rates of encounter among independently moving individuals. We illustrate these results using data from mangabey monkeys originally used along with the ideal gas model to argue that groups avoid each other. Although agent-based simulations provide a more flexible alternative approach, the ideal gas model remains both a valuable null model and a useful, less onerous, approximation to biological reality.

Non-animal assessment of skin sensitization hazard: Is an integrated testing strategy needed, and if so what should be integrated?

PubMed

Roberts, David W; Patlewicz, Grace

2018-01-01

There is an expectation that to meet regulatory requirements, and avoid or minimize animal testing, integrated approaches to testing and assessment will be needed that rely on assays representing key events (KEs) in the skin sensitization adverse outcome pathway. Three non-animal assays have been formally validated and regulatory adopted: the direct peptide reactivity assay (DPRA), the KeratinoSens™ assay and the human cell line activation test (h-CLAT). There have been many efforts to develop integrated approaches to testing and assessment with the "two out of three" approach attracting much attention. Here a set of 271 chemicals with mouse, human and non-animal sensitization test data was evaluated to compare the predictive performances of the three individual non-animal assays, their binary combinations and the "two out of three" approach in predicting skin sensitization potential. The most predictive approach was to use both the DPRA and h-CLAT as follows: (1) perform DPRA - if positive, classify as sensitizing, and (2) if negative, perform h-CLAT - a positive outcome denotes a sensitizer, a negative, a non-sensitizer. With this approach, 85% (local lymph node assay) and 93% (human) of non-sensitizer predictions were correct, whereas the "two out of three" approach had 69% (local lymph node assay) and 79% (human) of non-sensitizer predictions correct. The findings are consistent with the argument, supported by published quantitative mechanistic models that only the first KE needs to be modeled. All three assays model this KE to an extent. The value of using more than one assay depends on how the different assays compensate for each other's technical limitations. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

A model of amygdala-hippocampal-prefrontal interaction in fear conditioning and extinction in animals

PubMed Central

Moustafa, Ahmed A.; Gilbertson, Mark W.; Orr, Scott P.; Herzallah, Mohammad M.; Servatius, Richard. J.; Myers, Catherine E.

2012-01-01

Empirical research has shown that the amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC) are involved in fear conditioning. However, the functional contribution of each brain area and the nature of their interactions are not clearly understood. Here, we extend existing neural network models of the functional roles of the hippocampus in classical conditioning to include interactions with the amygdala and prefrontal cortex. We apply the model to fear conditioning, in which animals learn physiological (e.g. heart rate) and behavioral (e.g. freezing) responses to stimuli that have been paired with a highly aversive event (e.g. electrical shock). The key feature of our model is that learning of these conditioned responses in the central nucleus of the amygdala is modulated by two separate processes, one from basolateral amygdala and signaling a positive prediction error, and one from the vmPFC, via the intercalated cells of the amygdala, and signaling a negative prediction error. In addition, we propose that hippocampal input to both vmPFC and basolateral amygdala is essential for contextual modulation of fear acquisition and extinction. The model is sufficient to account for a body of data from various animal fear conditioning paradigms, including acquisition, extinction, reacquisition, and context specificity effects. Consistent with studies on lesioned animals, our model shows that damage to the vmPFC impairs extinction, while damage to the hippocampus impairs extinction in a different context (e.g., a different conditioning chamber from that used in initial training in animal experiments). We also discuss model limitations and predictions, including the effects of number of training trials on fear conditioning. PMID:23164732

Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation

ERIC Educational Resources Information Center

Weber, Elke U.; Shafir, Sharoni; Blais, Ann-Renee

2004-01-01

This article examines the statistical determinants of risk preference. In a meta-analysis of animal risk preference (foraging birds and insects), the coefficient of variation (CV), a measure of risk per unit of return, predicts choices far better than outcome variance, the risk measure of normative models. In a meta-analysis of human risk…

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N; Friggens, N C

2012-06-01

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

Ratios of transfer coefficients for radiocesium transport in ruminants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Assimakopoulos, P.A.; Ioannides, K.G.; Karamanis, D.

1995-09-01

A corollary of the multiple-compartment model for the transport of trace elements through animals was tested for cows, goats, and sheep. According to this corollary, for a given body {open_quotes}compartment{close_quotes} k of the animal (soft tissue, lung, liver, etc.), the ratio a(k)=f(k)/f(blood) of the transfer coefficients f, should exhibit similar values for physiologically similar animals. In order to verify this prediction, two experiments were performed at the Agricultural Research Station of Ioannina and at the facilities of Ria Pripyat in Pripyat, Ukranine. Eight animals in the first experiment and eighteen in the second were housed in individual pens and weremore » artificially contaminated with a constant daily dose of radiocesium until equilibrium was reached. the animals were then sacrificed and transfer coefficients f(k) to twelve body {open_quotes}compartments{close_quotes} k were measured. These data were used to calculate the ratios a(k). The results were in accordance with predictions of the model and average values of a(k) were extracted for ruminants. It is concluded that these values may be employed for the prediction of animal contamination in any body compartment through the measurement of blood samples. 7 refs., 8 tabs.« less

Mysid Population Responses to Resource Limitation Differ from those Predicted by Cohort Studies

EPA Science Inventory

Effects of anthropogenic stressors on animal populations are often evaluated by assembling vital rate responses from isolated cohort studies into a single demographic model. However, models constructed from cohort studies are difficult to translate into ecological predictions be...

Breast Organotypic Cancer Models.

PubMed

Carranza-Rosales, Pilar; Guzmán-Delgado, Nancy Elena; Carranza-Torres, Irma Edith; Viveros-Valdez, Ezequiel; Morán-Martínez, Javier

2018-03-20

Breast cancer is the most common cancer type diagnosed in women, it represents a critical public health problem worldwide, with 1,671,149 estimated new cases and nearly 571,000 related deaths. Research on breast cancer has mainly been conducted using two-dimensional (2D) cell cultures and animal models. The usefulness of these models is reflected in the vast knowledge accumulated over the past decades. However, considering that animal models are three-dimensional (3D) in nature, the validity of the studies using 2D cell cultures has recently been questioned. Although animal models are important in cancer research, ethical questions arise about their use and usefulness as there is no clear predictivity of human disease outcome and they are very expensive and take too much time to obtain results. The poor performance or failure of most cancer drugs suggests that preclinical research on cancer has been based on an over-dependence on inadequate animal models. For these reasons, in the last few years development of alternative models has been prioritized to study human breast cancer behavior, while maintaining a 3D microenvironment, and to reduce the number of experiments conducted in animals. One way to achieve this is using organotypic cultures, which are being more frequently explored in cancer research because they mimic tissue architecture in vivo. These characteristics make organotypic cultures a valuable tool in cancer research as an alternative to replace animal models and for predicting risk assessment in humans. This chapter describes the cultures of multicellular spheroids, organoids, 3D bioreactors, and tumor slices, which are the most widely used organotypic models in breast cancer research.

The Significance of Meaning: Why Do Over 90% of Behavioral Neuroscience Results Fail to Translate to Humans, and What Can We Do to Fix It?

PubMed Central

Garner, Joseph P.

2014-01-01

The vast majority of drugs entering human trials fail. This problem (called “attrition”) is widely recognized as a public health crisis, and has been discussed openly for the last two decades. Multiple recent reviews argue that animals may be just too different physiologically, anatomically, and psychologically from humans to be able to predict human outcomes, essentially questioning the justification of basic biomedical research in animals. This review argues instead that the philosophy and practice of experimental design and analysis is so different in basic animal work and human clinical trials that an animal experiment (as currently conducted) cannot reasonably predict the outcome of a human trial. Thus, attrition does reflect a lack of predictive validity of animal experiments, but it would be a tragic mistake to conclude that animal models cannot show predictive validity. A variety of contributing factors to poor validity are reviewed. The need to adopt methods and models that are highly specific (i.e., which can identify true negative results) in order to complement the current preponderance of highly sensitive methods (which are prone to false positive results) is emphasized. Concepts in biomarker-based medicine are offered as a potential solution, and changes in the use of animal models required to embrace a translational biomarker-based approach are outlined. In essence, this review advocates a fundamental shift, where we treat every aspect of an animal experiment that we can as if it was a clinical trial in a human population. However, it is unrealistic to expect researchers to adopt a new methodology that cannot be empirically justified until a successful human trial. “Validation with known failures” is proposed as a solution. Thus new methods or models can be compared against existing ones using a drug that has translated (a known positive) and one that has failed (a known negative). Current methods should incorrectly identify both as effective, but a more specific method should identify the negative compound correctly. By using a library of known failures we can thereby empirically test the impact of suggested solutions such as enrichment, controlled heterogenization, biomarker-based models, or reverse-translated measures. PMID:25541546

Inter-species investigation of the mechano-regulation of bone healing: comparison of secondary bone healing in sheep and rat.

PubMed

Checa, Sara; Prendergast, Patrick J; Duda, Georg N

2011-04-29

Inter-species differences in regeneration exist in various levels. One aspect is the dynamics of bone regeneration and healing, e.g. small animals show a faster healing response when compared to large animals. Mechanical as well as biological factors are known to play a key role in the process. However, it remains so far unknown whether different animals follow at all comparable mechano-biological rules during tissue regeneration, and in particular during bone healing. In this study, we investigated whether differences observed in vivo in the dynamics of bone healing between rat and sheep are only due to differences in the animal size or whether these animals have a different mechano-biological response during the healing process. Histological sections from in vivo experiments were compared to in silico predictions of a mechano-biological computer model for the simulation of bone healing. Investigations showed that the healing processes in both animal models occur under significantly different levels of mechanical stimuli within the callus region, which could explain histological observations of early intramembranous ossification at the endosteal side. A species-specific adaptation of a mechano-biological model allowed a qualitative match of model predictions with histological observations. Specifically, when keeping cell activity processes at the same rate, the amount of tissue straining defining favorable mechanical conditions for the formation of bone had to be increased in the large animal model, with respect to the small animal, to achieve a qualitative agreement of model predictions with histological data. These findings illustrate that geometrical (size) differences alone cannot explain the distinctions seen in the histological appearance of secondary bone healing in sheep and rat. It can be stated that significant differences in the mechano-biological regulation of the healing process exist between these species. Future investigations should aim towards understanding whether these differences are due to differences in cell behavior, material properties of the newly formed tissues within the callus and/or differences in response to the mechanical environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Phenotypic variation in metabolism and morphology correlating with animal swimming activity in the wild: relevance for the OCLTT (oxygen- and capacity-limitation of thermal tolerance), allocation and performance models

PubMed Central

Baktoft, Henrik; Jacobsen, Lene; Skov, Christian; Koed, Anders; Jepsen, Niels; Berg, Søren; Boel, Mikkel; Aarestrup, Kim; Svendsen, Jon C.

2016-01-01

Ongoing climate change is affecting animal physiology in many parts of the world. Using metabolism, the oxygen- and capacity-limitation of thermal tolerance (OCLTT) hypothesis provides a tool to predict the responses of ectothermic animals to variation in temperature, oxygen availability and pH in the aquatic environment. The hypothesis remains controversial, however, and has been questioned in several studies. A positive relationship between aerobic metabolic scope and animal activity would be consistent with the OCLTT but has rarely been tested. Moreover, the performance model and the allocation model predict positive and negative relationships, respectively, between standard metabolic rate and activity. Finally, animal activity could be affected by individual morphology because of covariation with cost of transport. Therefore, we hypothesized that individual variation in activity is correlated with variation in metabolism and morphology. To test this prediction, we captured 23 wild European perch (Perca fluviatilis) in a lake, tagged them with telemetry transmitters, measured standard and maximal metabolic rates, aerobic metabolic scope and fineness ratio and returned the fish to the lake to quantify individual in situ activity levels. Metabolic rates were measured using intermittent flow respirometry, whereas the activity assay involved high-resolution telemetry providing positions every 30 s over 12 days. We found no correlation between individual metabolic traits and activity, whereas individual fineness ratio correlated with activity. Independent of body length, and consistent with physics theory, slender fish maintained faster mean and maximal swimming speeds, but this variation did not result in a larger area (in square metres) explored per 24 h. Testing assumptions and predictions of recent conceptual models, our study indicates that individual metabolism is not a strong determinant of animal activity, in contrast to individual morphology, which is correlated with in situ activity patterns. PMID:27382465

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.

PubMed

Macmillan, Donna S; Canipa, Steven J; Chilton, Martyn L; Williams, Richard V; Barber, Christopher G

2016-04-01

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays. Copyright © 2016 Elsevier Inc. All rights reserved.

Specificity of disgust domains in the prediction of contamination anxiety and avoidance: a multimodal examination.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Haidt, Jonathan; Sawchuk, Craig N

2014-07-01

Although core, animal-reminder, and contamination disgust are viewed as distinct "types" of disgust vulnerabilities, the extent to which individual differences in the three disgust domains uniquely predict contamination-related anxiety and avoidance remains unclear. Three studies were conducted to fill this important gap in the literature. Study 1 was conducted to first determine if the three types of disgust could be replicated in a larger and more heterogeneous sample. Confirmatory factor analysis revealed that a bifactor model consisting of a "general disgust" dimension and the three distinct disgust dimensions yielded a better fit than a one-factor model. Structural equation modeling in Study 2 showed that while latent core, animal-reminder, and contamination disgust factors each uniquely predicted a latent "contamination anxiety" factor above and beyond general disgust, only animal-reminder uniquely predicted a latent "non-contamination anxiety" factor above and beyond general disgust. However, Study 3 found that only contamination disgust uniquely predicted behavioral avoidance in a public restroom where contamination concerns are salient. These findings suggest that although the three disgust domains are associated with contamination anxiety and avoidance, individual differences in contamination disgust sensitivity appear to be most uniquely predictive of contamination-related distress. The implications of these findings for the development and maintenance of anxiety-related disorders marked by excessive contamination concerns are discussed. Copyright © 2014. Published by Elsevier Ltd.

Mathematics as a conduit for translational research in post-traumatic osteoarthritis.

PubMed

Ayati, Bruce P; Kapitanov, Georgi I; Coleman, Mitchell C; Anderson, Donald D; Martin, James A

2017-03-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a "conduit of translation." The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:566-572, 2017. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models.

PubMed

Hristov, A N; Kebreab, E; Niu, M; Oh, J; Bannink, A; Bayat, A R; Boland, T B; Brito, A F; Casper, D P; Crompton, L A; Dijkstra, J; Eugène, M; Garnsworthy, P C; Haque, N; Hellwing, A L F; Huhtanen, P; Kreuzer, M; Kuhla, B; Lund, P; Madsen, J; Martin, C; Moate, P J; Muetzel, S; Muñoz, C; Peiren, N; Powell, J M; Reynolds, C K; Schwarm, A; Shingfield, K J; Storlien, T M; Weisbjerg, M R; Yáñez-Ruiz, D R; Yu, Z

2018-04-18

Ruminant production systems are important contributors to anthropogenic methane (CH 4 ) emissions, but there are large uncertainties in national and global livestock CH 4 inventories. Sources of uncertainty in enteric CH 4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH 4 emission factors. There is also significant uncertainty associated with enteric CH 4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF 6 ) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH 4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH 4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH 4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH 4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH 4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH 4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Mathematical modeling and simulation in animal health - Part II: principles, methods, applications, and value of physiologically based pharmacokinetic modeling in veterinary medicine and food safety assessment.

PubMed

Lin, Z; Gehring, R; Mochel, J P; Lavé, T; Riviere, J E

2016-10-01

This review provides a tutorial for individuals interested in quantitative veterinary pharmacology and toxicology and offers a basis for establishing guidelines for physiologically based pharmacokinetic (PBPK) model development and application in veterinary medicine. This is important as the application of PBPK modeling in veterinary medicine has evolved over the past two decades. PBPK models can be used to predict drug tissue residues and withdrawal times in food-producing animals, to estimate chemical concentrations at the site of action and target organ toxicity to aid risk assessment of environmental contaminants and/or drugs in both domestic animals and wildlife, as well as to help design therapeutic regimens for veterinary drugs. This review provides a comprehensive summary of PBPK modeling principles, model development methodology, and the current applications in veterinary medicine, with a focus on predictions of drug tissue residues and withdrawal times in food-producing animals. The advantages and disadvantages of PBPK modeling compared to other pharmacokinetic modeling approaches (i.e., classical compartmental/noncompartmental modeling, nonlinear mixed-effects modeling, and interspecies allometric scaling) are further presented. The review finally discusses contemporary challenges and our perspectives on model documentation, evaluation criteria, quality improvement, and offers solutions to increase model acceptance and applications in veterinary pharmacology and toxicology. © 2016 John Wiley & Sons Ltd.

Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships.

PubMed

Liu, Zhichao; Kelly, Reagan; Fang, Hong; Ding, Don; Tong, Weida

2011-07-18

The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the TGx models' predictive accuracy (0.77, 0.77, and 0.82 for the 1-day, 3-day, and 5-day models, respectively) was much higher for an independent validation set than that of a QSAR model (0.55). Permutation tests confirmed the statistical significance of the model's prediction performance. The study concluded that a short-term 5-day TGx animal model holds the potential to predict nongenotoxic hepatocarcinogenicity. © 2011 American Chemical Society

Comparison of predictability for human pharmacokinetics parameters among monkeys, rats, and chimeric mice with humanised liver.

PubMed

Miyamoto, Maki; Iwasaki, Shinji; Chisaki, Ikumi; Nakagawa, Sayaka; Amano, Nobuyuki; Hirabayashi, Hideki

2017-12-01

1. The aim of the present study was to evaluate the usefulness of chimeric mice with humanised liver (PXB mice) for the prediction of clearance (CL t ) and volume of distribution at steady state (Vd ss ), in comparison with monkeys, which have been reported as a reliable model for human pharmacokinetics (PK) prediction, and with rats, as a conventional PK model. 2. CL t and Vd ss values in PXB mice, monkeys and rats were determined following intravenous administration of 30 compounds known to be mainly eliminated in humans via the hepatic metabolism by various drug-metabolising enzymes. Using single-species allometric scaling, human CL t and Vd ss values were predicted from the three animal models. 3. Predicted CL t values from PXB mice exhibited the highest predictability: 25 for PXB mice, 21 for monkeys and 14 for rats were predicted within a three-fold range of actual values among 30 compounds. For predicted human Vd ss values, the number of compounds falling within a three-fold range was 23 for PXB mice, 24 for monkeys, and 16 for rats among 29 compounds. PXB mice indicated a higher predictability for CL t and Vd ss values than the other animal models. 4. These results demonstrate the utility of PXB mice in predicting human PK parameters.

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

PubMed

Muñoz-Tamayo, R; Puillet, L; Daniel, J B; Sauvant, D; Martin, O; Taghipoor, M; Blavy, P

2018-04-01

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. PMID:23071521

Comparing toxicologic and epidemiologic studies: methylene chloride--a case study.

PubMed

Stayner, L T; Bailer, A J

1993-12-01

Exposure to methylene chloride induces lung and liver cancers in mice. The mouse bioassay data have been used as the basis for several cancer risk assessments. The results from epidemiologic studies of workers exposed to methylene chloride have been mixed with respect to demonstrating an increased cancer risk. The results from a negative epidemiologic study of Kodak workers have been used by two groups of investigators to test the predictions from the EPA risk assessment models. These two groups used very different approaches to this problem, which resulted in opposite conclusions regarding the consistency between the animal model predictions and the Kodak study results. The results from the Kodak study are used to test the predictions from OSHA's multistage models of liver and lung cancer risk. Confidence intervals for the standardized mortality ratios (SMRs) from the Kodak study are compared with the predicted confidence intervals derived from OSHA's risk assessment models. Adjustments for the "healthy worker effect," differences in length of follow-up, and dosimetry between animals and humans were incorporated into these comparisons. Based on these comparisons, we conclude that the negative results from the Kodak study are not inconsistent with the predictions from OSHA's risk assessment model.

Old models explain new observations of butterfly movement at patch edges.

PubMed

Crone, Elizabeth E; Schultz, Cheryl B

2008-07-01

Understanding movement in heterogeneous environments is central to predicting how landscape changes affect animal populations. Several recent studies point out an intriguing and distinctive looping behavior by butterflies at habitat patch edges and hypothesize that this behavior requires a new framework for analyzing animal movement. We show that this looping behavior could be caused by a longstanding movement model, biased correlated random walk, with bias toward habitat patches. The ability of this longstanding model to explain recent observations reinforces the point that butterflies respond to habitat heterogeneity and do not move randomly through heterogeneous environments. We discuss the implications of different movement models for predicting butterfly responses to landscape change, and our rationale for retaining longstanding movement models, rather than developing new modeling frameworks for looping behavior at patch edges.

Incorporating a prediction of postgrazing herbage mass into a whole-farm model for pasture-based dairy systems.

PubMed

Gregorini, P; Galli, J; Romera, A J; Levy, G; Macdonald, K A; Fernandez, H H; Beukes, P C

2014-07-01

The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat.

PubMed

Bentzley, Brandon S; Jhou, Thomas C; Aston-Jones, Gary

2014-08-12

Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaine-seeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments.

Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat

PubMed Central

Bentzley, Brandon S.; Jhou, Thomas C.; Aston-Jones, Gary

2014-01-01

Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaine-seeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments. PMID:25071176

Social stress in tree shrews as an animal model of depression: an example of a behavioral model of a CNS disorder.

PubMed

Fuchs, Eberhard

2005-03-01

Animal models are invaluable in preclinical research on human psychopathology. Valid animal models to study the pathophysiology of depression and specific biological and behavioral responses to antidepressant drug treatments are of prime interest. In order to improve our knowledge of the causal mechanisms of stress-related disorders such as depression, we need animal models that mirror the situation seen in patients. One promising model is the chronic psychosocial stress paradigm in male tree shrews. Coexistence of two males in visual and olfactory contact leads to a stable dominant/subordinate relationship, with the subordinates showing obvious changes in behavioral, neuroendocrine, and central nervous activity that are similar to the signs and symptoms observed during episodes of depression in patients. To discover whether this model, besides its "face validity" for depression, also has "predictive validity," we treated subordinate animals with the tricyclic antidepressant clomipramine and found a time-dependent recovery of both endocrine function and normal behavior. In contrast, the anxiolytic diazepam was ineffective. Chronic psychosocial stress in male tree shrews significantly decreased hippocampal volume and the proliferation rate of the granule precursor cells in the dentate gyrus. These stress-induced changes can be prevented by treating the animals with clomipramine, tianeptine, or the selective neurokinin receptor antagonist L-760,735. In addition to its apparent face and predictive validity, the tree shrew model also has a "molecular validity" due to the degradation routes of psychotropic compounds and gene sequences of receptors are very similar to those in humans. Although further research is required to validate this model fully, it provides an adequate and interesting non-rodent experimental paradigm for preclinical research on depression.

Predictive Models of target organ and Systemic toxicities (BOSC)

EPA Science Inventory

The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

Estimating Bat and Bird Mortality Occurring at Wind Energy Turbines from Covariates and Carcass Searches Using Mixture Models

PubMed Central

Korner-Nievergelt, Fränzi; Brinkmann, Robert; Niermann, Ivo; Behr, Oliver

2013-01-01

Environmental impacts of wind energy facilities increasingly cause concern, a central issue being bats and birds killed by rotor blades. Two approaches have been employed to assess collision rates: carcass searches and surveys of animals prone to collisions. Carcass searches can provide an estimate for the actual number of animals being killed but they offer little information on the relation between collision rates and, for example, weather parameters due to the time of death not being precisely known. In contrast, a density index of animals exposed to collision is sufficient to analyse the parameters influencing the collision rate. However, quantification of the collision rate from animal density indices (e.g. acoustic bat activity or bird migration traffic rates) remains difficult. We combine carcass search data with animal density indices in a mixture model to investigate collision rates. In a simulation study we show that the collision rates estimated by our model were at least as precise as conventional estimates based solely on carcass search data. Furthermore, if certain conditions are met, the model can be used to predict the collision rate from density indices alone, without data from carcass searches. This can reduce the time and effort required to estimate collision rates. We applied the model to bat carcass search data obtained at 30 wind turbines in 15 wind facilities in Germany. We used acoustic bat activity and wind speed as predictors for the collision rate. The model estimates correlated well with conventional estimators. Our model can be used to predict the average collision rate. It enables an analysis of the effect of parameters such as rotor diameter or turbine type on the collision rate. The model can also be used in turbine-specific curtailment algorithms that predict the collision rate and reduce this rate with a minimal loss of energy production. PMID:23844144

Estimating bat and bird mortality occurring at wind energy turbines from covariates and carcass searches using mixture models.

PubMed

Korner-Nievergelt, Fränzi; Brinkmann, Robert; Niermann, Ivo; Behr, Oliver

2013-01-01

Environmental impacts of wind energy facilities increasingly cause concern, a central issue being bats and birds killed by rotor blades. Two approaches have been employed to assess collision rates: carcass searches and surveys of animals prone to collisions. Carcass searches can provide an estimate for the actual number of animals being killed but they offer little information on the relation between collision rates and, for example, weather parameters due to the time of death not being precisely known. In contrast, a density index of animals exposed to collision is sufficient to analyse the parameters influencing the collision rate. However, quantification of the collision rate from animal density indices (e.g. acoustic bat activity or bird migration traffic rates) remains difficult. We combine carcass search data with animal density indices in a mixture model to investigate collision rates. In a simulation study we show that the collision rates estimated by our model were at least as precise as conventional estimates based solely on carcass search data. Furthermore, if certain conditions are met, the model can be used to predict the collision rate from density indices alone, without data from carcass searches. This can reduce the time and effort required to estimate collision rates. We applied the model to bat carcass search data obtained at 30 wind turbines in 15 wind facilities in Germany. We used acoustic bat activity and wind speed as predictors for the collision rate. The model estimates correlated well with conventional estimators. Our model can be used to predict the average collision rate. It enables an analysis of the effect of parameters such as rotor diameter or turbine type on the collision rate. The model can also be used in turbine-specific curtailment algorithms that predict the collision rate and reduce this rate with a minimal loss of energy production.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

PubMed

Fitzpatrick, J M; Roberts, D W; Patlewicz, G

2018-06-01

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

[(18)F]FDG PET Neuroimaging Predicts Pentylenetetrazole (PTZ) Kindling Outcome in Rats.

PubMed

Bascuñana, Pablo; Javela, Julián; Delgado, Mercedes; Fernández de la Rosa, Rubén; Shiha, Ahmed Anis; García-García, Luis; Pozo, Miguel Ángel

2016-10-01

Epileptogenesis, i.e., development of epilepsy, involves a number of processes that alter the brain function in the way that triggers spontaneous seizures. Kindling is one of the most used animal models of temporal lobe epilepsy (TLE) and epileptogenesis, although chemical kindling suffers from high inter-assay success unpredictability. This study was aimed to analyze the eventual regional brain metabolic changes during epileptogenesis in the pentylenetetrazole (PTZ) kindling model in order to obtain a predictive kindling outcome parameter. In vivo longitudinal positron emission tomography (PET) scans with 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) along the PTZ kindling protocol (35 mg/kg intraperitoneally (i.p.), 18 sessions) in adult male rats were performed in order to evaluate the regional brain metabolism. The half of the PTZ-injected rats reached the kindled state. In addition, a significant decrease of [(18)F]FDG uptake at the end of the protocol in most of the brain structures of kindled animals was found, reflecting the characteristic epilepsy-associated hypometabolism. However, PTZ-injected animals but not reaching the kindled state did not show this widespread brain hypometabolism. Retrospective analysis of the data revealed that hippocampal [(18)F]FDG uptake normalized to pons turned out to be a predictive index of the kindling outcome. Thus, a 19.06 % reduction (p = 0.008) of the above parameter was found in positively kindled rats compared to non-kindled ones just after the fifth PTZ session. Non-invasive PET neuroimaging was a useful tool for discerning epileptogenesis progression in this animal model. Particularly, the [(18)F]FDG uptake of the hippocampus proved to be an early predictive parameter to differentiate resistant and non-resistant animals to the PTZ kindling.

Modeling in vivo fluorescence of small animals using TracePro software

NASA Astrophysics Data System (ADS)

Leavesley, Silas; Rajwa, Bartek; Freniere, Edward R.; Smith, Linda; Hassler, Richard; Robinson, J. Paul

2007-02-01

The theoretical modeling of fluorescence excitation, emission, and propagation within living tissue has been a limiting factor in the development and calibration of in vivo small animal fluorescence imagers. To date, no definitive calibration standard, or phantom, has been developed for use with small animal fluorescence imagers. Our work in the theoretical modeling of fluorescence in small animals using solid modeling software is useful in optimizing the design of small animal imaging systems, and in predicting their response to a theoretical model. In this respect, it is also valuable in the design of a fluorescence phantom for use in in vivo small animal imaging. The use of phantoms is a critical step in the testing and calibration of most diagnostic medical imaging systems. Despite this, a realistic, reproducible, and informative phantom has yet to be produced for use in small animal fluorescence imaging. By modeling the theoretical response of various types of phantoms, it is possible to determine which parameters are necessary for accurately modeling fluorescence within inhomogenous scattering media such as tissue. Here, we present the model that has been developed, the challenges and limitations associated with developing such a model, and the applicability of this model to experimental results obtained in a commercial small animal fluorescence imager.

Measurement and prediction of enteric methane emission

NASA Astrophysics Data System (ADS)

Sejian, Veerasamy; Lal, Rattan; Lakritz, Jeffrey; Ezeji, Thaddeus

2011-01-01

The greenhouse gas (GHG) emissions from the agricultural sector account for about 25.5% of total global anthropogenic emission. While CO2 receives the most attention as a factor relative to global warming, CH4, N2O and chlorofluorocarbons (CFCs) also cause significant radiative forcing. With the relative global warming potential of 25 compared with CO2, CH4 is one of the most important GHGs. This article reviews the prediction models, estimation methodology and strategies for reducing enteric CH4 emissions. Emission of CH4 in ruminants differs among developed and developing countries, depending on factors like animal species, breed, pH of rumen fluid, ratio of acetate:propionate, methanogen population, composition of diet and amount of concentrate fed. Among the ruminant animals, cattle contribute the most towards the greenhouse effect through methane emission followed by sheep, goats and buffalos, respectively. The estimated CH4 emission rate per cattle, buffaloe, sheep and goat in developed countries are 150.7, 137, 21.9 and 13.7 (g/animal/day) respectively. However, the estimated rates in developing countries are significantly lower at 95.9 and 13.7 (g/animal/day) per cattle and sheep, respectively. There exists a strong interest in developing new and improving the existing CH4 prediction models to identify mitigation strategies for reducing the overall CH4 emissions. A synthesis of the available literature suggests that the mechanistic models are superior to empirical models in accurately predicting the CH4 emission from dairy farms. The latest development in prediction model is the integrated farm system model which is a process-based whole-farm simulation technique. Several techniques are used to quantify enteric CH4 emissions starting from whole animal chambers to sulfur hexafluoride (SF6) tracer techniques. The latest technology developed to estimate CH4 more accurately is the micrometeorological mass difference technique. Because the conditions under which animals are managed vary greatly by country, CH4 emissions reduction strategies must be tailored to country-specific circumstances. Strategies that are cost effective, improve productivity, and have limited potential negative effects on livestock production hold a greater chance of being adopted by producers. It is also important to evaluate CH4 mitigation strategies in terms of the total GHG budget and to consider the economics of various strategies. Although reductions in GHG emissions from livestock industries are seen as high priorities, strategies for reducing emissions should not reduce the economic viability of enterprises.

Insights on in vitro models for safety and toxicity assessment of cosmetic ingredients.

PubMed

Almeida, Andreia; Sarmento, Bruno; Rodrigues, Francisca

2017-03-15

According to the current European legislation, the safety assessment of each individual cosmetic ingredient of any formulation is the basis for the safety evaluation of a cosmetic product. Also, animal testing in the European Union is prohibited for cosmetic ingredients and products since 2004 and 2009, respectively. Additionally, the commercialization of any cosmetic products containing ingredients tested on animal models was forbidden in 2009. In consequence of these boundaries, the European Centre for the Validation of Alternative Methods (ECVAM) proposes a list of validated cell-based in vitro models for predicting the safety and toxicity of cosmetic ingredients. These models have been demonstrated as valuable and effective tools to overcome the limitations of animal in vivo studies. Although the use of in vitro cell-based models for the evaluation of absorption and permeability of cosmetic ingredients is widespread, a detailed study on the properties of these platforms and the in vitro-in vivo correlation compared with human data are required. Moreover, additional efforts must be taken to develop in vitro models to predict carcinogenicity, repeat dose toxicity and reproductive toxicity, for which no alternative in vitro methods are currently available. This review paper summarizes and characterizes the most relevant in vitro models validated by ECVAM employed to predict the safety and toxicology of cosmetic ingredients. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast and simultaneous prediction of animal feed nutritive values using near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Samadi; Wajizah, S.; Munawar, A. A.

2018-02-01

Feed plays an important factor in animal production. The purpose of this study is to apply NIRS method in determining feed values. NIRS spectra data were acquired for feed samples in wavelength range of 1000 - 2500 nm with 32 scans and 0.2 nm wavelength. Spectral data were corrected by de-trending (DT) and standard normal variate (SNV) methods. Prediction of in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) were established as model by using principal component regression (PCR) and validated using leave one out cross validation (LOOCV). Prediction performance was quantified using coefficient correlation (r) and residual predictive deviation (RPD) index. The results showed that IVDMD and IVOMD can be predicted by using SNV spectra data with r and RPD index: 0.93 and 2.78 for IVDMD ; 0.90 and 2.35 for IVOMD respectively. In conclusion, NIRS technique appears feasible to predict animal feed nutritive values.

[Are non-clinical studies predictive of adverse events in humans?].

PubMed

Claude, N

2007-09-01

The predictibility of adverse events induced by drugs in non-clinical safety studies performed on in vitro and/or in vivo models is a key point for the safety of humans exposed to pharmaceuticals. The strength and the weakness of animal studies to predict human toxicity were assessed by an international study on the concordance of the toxicity of 150 pharmaceuticals observed in humans with that observed in experimental animals. The results showed a good correlation (70% of the adverse events in humans were detected in animal studies) and an early time to first appearance of concordant animal toxicity: 94% were first observed in studies of 1 month or less in duration. The highest incidence of overall concordance was seen in hematological and cardiovascular adverse effects and the least was seen in cutaneous and ophthalmological adverse effects. These studies, scientifically and regulatory standardized, need, in some cases to be adapted to specific problems linked to sensitive populations (young, old or with a pathology which could be worsened by the drug), or specific pharmaceuticals (produced by biotechnology). Some severe adverse events are not detected in conventional animal models (immuno-allergy, idiosyncrasy). Taken together, these elements support the value of toxicology studies to predict many human toxic events associated with pharmaceuticals. Nevertheless, a part of human toxicity is not detected by these experimental approaches, and new tools developed through progress in biology and bio-informatics should reduce this uncertainly margin.

Reverse-translational biomarker validation of Abnormal Repetitive Behaviors in mice: an illustration of the 4P's modeling approach

PubMed Central

Garner, Joseph P.; Thogerson, Collette M.; Dufour, Brett D.; Würbel, Hanno; Murray, James D.; Mench, Joy A.

2011-01-01

The NIMH's new strategic plan, with its emphasis on the “4P's” (Prediction, Preemption, Personalization, & Populations) and biomarker-based medicine requires a radical shift in animal modeling methodology. In particular 4P's models will be non-determinant (i.e. disease severity will depend on secondary environmental and genetic factors); and validated by reverse-translation of animal homologues to human biomarkers. A powerful consequence of the biomarker approach is that different closely-related disorders have a unique fingerprint of biomarkers. Animals can be validated as a highly-specific model of a single disorder by matching this `fingerprint'; or as a model of a symptom seen in multiple disorders by matching common biomarkers. Here we illustrate this approach with two Abnormal Repetitive Behaviors (ARBs) in mice: stereotypies; and barbering (hair pulling). We developed animal versions of the neuropsychological biomarkers that distinguish human ARBs, and tested the fingerprint of the different mouse ARBs. As predicted, the two mouse ARBs were associated with different biomarkers. Both barbering and stereotypy could be discounted as models of OCD (even though they are widely used as such), due to the absence of limbic biomarkers which are characteristic of OCD and hence are necessary for a valid model. Conversely barbering matched the fingerprint of trichotillomania (i.e. selective deficits in set-shifting), suggesting it may be a highly specific model of this disorder. In contrast stereotypies were correlated only with a biomarker (deficits in response shifting) correlated with stereotypies in multiple disorders, suggesting that animal stereotypies model stereotypies in multiple disorders. PMID:21219937

ANIMAL MODELS FOR IMMUNOTOXICITY

EPA Science Inventory

Greater susceptibility to infection is a hallmark of compromised immune function in humans and animals, and is often considered the benchmark against which the predictive value of immune function tests are compared. This focus of this paper is resistance to infection with the pa...

Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

PubMed

Heilbron, Micha; Chait, Maria

2017-08-04

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Individual variation in social aggression and the probability of inheritance: theory and a field test.

PubMed

Cant, Michael A; Llop, Justine B; Field, Jeremy

2006-06-01

Recent theory suggests that much of the wide variation in individual behavior that exists within cooperative animal societies can be explained by variation in the future direct component of fitness, or the probability of inheritance. Here we develop two models to explore the effect of variation in future fitness on social aggression. The models predict that rates of aggression will be highest toward the front of the queue to inherit and will be higher in larger, more productive groups. A third prediction is that, in seasonal animals, aggression will increase as the time available to inherit the breeding position runs out. We tested these predictions using a model social species, the paper wasp Polistes dominulus. We found that rates of both aggressive "displays" (aimed at individuals of lower rank) and aggressive "tests" (aimed at individuals of higher rank) decreased down the hierarchy, as predicted by our models. The only other significant factor affecting aggression rates was date, with more aggression observed later in the season, also as predicted. Variation in future fitness due to inheritance rank is the hidden factor accounting for much of the variation in aggressiveness among apparently equivalent individuals in this species.

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

PubMed

García-Ballesteros, S; Varona, L; Valera, M; Gutiérrez, J P; Cervantes, I

2018-01-01

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.

Analysis of longitudinal data of beef cattle raised on pasture from northern Brazil using nonlinear models.

PubMed

Lopes, Fernando B; da Silva, Marcelo C; Marques, Ednira G; McManus, Concepta M

2012-12-01

This study was undertaken to aim of estimating the genetic parameters and trends for asymptotic weight (A) and maturity rate (k) of Nellore cattle from northern Brazil. The data set was made available by the Brazilian Association of Zebu Breeders and collected between the years of 1997 and 2007. The Von Bertalanffy, Brody, Gompertz, and logistic nonlinear models were fitted by the Gauss-Newton method to weight-age data of 45,895 animals collected quarterly of the birth to 750 days old. The curve parameters were analyzed using the procedures GLM and CORR. The estimation of (co)variance components and genetic parameters was obtained using the MTDFREML software. The estimated heritability coefficients were 0.21 ± 0.013 and 0.25 ± 0.014 for asymptotic weight and maturity rate, respectively. This indicates that selection for any trait shall results in genetic progress in the herd. The genetic correlation between A and k was negative (-0.57 ± 0.03) and indicated that animals selected for high maturity rate shall result in low asymptotic weight. The Von Bertalanffy function is adequate to establish the mean growth patterns and to predict the adult weight of Nellore cattle. This model is more accurate in predicting the birth weight of these animals and has better overall fit. The prediction of adult weight using nonlinear functions can be accurate when growth curve parameters and their (co)variance components are estimated jointly. The model used in this study can be applied to the prediction of mature weight in herds where a portion of the animals are culled before they reach the adult age.

MODELING VOLATILE ORGANIC COMPOUND PHARMACOKINETICS IN RAT PUPS

EPA Science Inventory

PBPK model predictions of internal dosimetry in young rats were compared to adult animals for benzene, chloroform (CHL), methylene chloride, methyl ethly ketone (MEK), perchloroethylene, and trichloroethylene.

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

PubMed Central

Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius

2016-01-01

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations. PMID:26977807

Animal models of fibromyalgia

PubMed Central

2013-01-01

Animal models of disease states are valuable tools for developing new treatments and investigating underlying mechanisms. They should mimic the symptoms and pathology of the disease and importantly be predictive of effective treatments. Fibromyalgia is characterized by chronic widespread pain with associated co-morbid symptoms that include fatigue, depression, anxiety and sleep dysfunction. In this review, we present different animal models that mimic the signs and symptoms of fibromyalgia. These models are induced by a wide variety of methods that include repeated muscle insults, depletion of biogenic amines, and stress. All potential models produce widespread and long-lasting hyperalgesia without overt peripheral tissue damage and thus mimic the clinical presentation of fibromyalgia. We describe the methods for induction of the model, pathophysiological mechanisms for each model, and treatment profiles. PMID:24314231

How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs.

PubMed

Monk, Christopher T; Barbier, Matthieu; Romanczuk, Pawel; Watson, James R; Alós, Josep; Nakayama, Shinnosuke; Rubenstein, Daniel I; Levin, Simon A; Arlinghaus, Robert

2018-06-01

Understanding how humans and other animals behave in response to changes in their environments is vital for predicting population dynamics and the trajectory of coupled social-ecological systems. Here, we present a novel framework for identifying emergent social behaviours in foragers (including humans engaged in fishing or hunting) in predator-prey contexts based on the exploration difficulty and exploitation potential of a renewable natural resource. A qualitative framework is introduced that predicts when foragers should behave territorially, search collectively, act independently or switch among these states. To validate it, we derived quantitative predictions from two models of different structure: a generic mathematical model, and a lattice-based evolutionary model emphasising exploitation and exclusion costs. These models independently identified that the exploration difficulty and exploitation potential of the natural resource controls the social behaviour of resource exploiters. Our theoretical predictions were finally compared to a diverse set of empirical cases focusing on fisheries and aquatic organisms across a range of taxa, substantiating the framework's predictions. Understanding social behaviour for given social-ecological characteristics has important implications, particularly for the design of governance structures and regulations to move exploited systems, such as fisheries, towards sustainability. Our framework provides concrete steps in this direction. © 2018 John Wiley & Sons Ltd/CNRS.

Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn’t Fit All

PubMed Central

Brommesson, Peter; Wennergren, Uno; Lindström, Tom

2016-01-01

The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate animal movements that assume the same pattern across all regions and seasons without explicitly testing for spatiotemporal variation. PMID:27760155

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

USGS Publications Warehouse

Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.

2013-01-01

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep.

PubMed

Peñagaricano, F; Urioste, J I; Naya, H; de los Campos, G; Gianola, D

2011-04-01

Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep. © 2010 Blackwell Verlag GmbH.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

PubMed Central

Eng, Christine L. P.; Tong, Joo Chuan; Tan, Tin Wee

2017-01-01

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. PMID:28587080

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

PubMed

Eng, Christine L P; Tong, Joo Chuan; Tan, Tin Wee

2017-05-25

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Multivariate Models for Prediction of Human Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2016-01-01

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

Preclinical Studies for Cartilage Repair

PubMed Central

Hurtig, Mark B.; Buschmann, Michael D.; Fortier, Lisa A.; Hoemann, Caroline D.; Hunziker, Ernst B.; Jurvelin, Jukka S.; Mainil-Varlet, Pierre; McIlwraith, C. Wayne; Sah, Robert L.; Whiteside, Robert A.

2011-01-01

Investigational devices for articular cartilage repair or replacement are considered to be significant risk devices by regulatory bodies. Therefore animal models are needed to provide proof of efficacy and safety prior to clinical testing. The financial commitment and regulatory steps needed to bring a new technology to clinical use can be major obstacles, so the implementation of highly predictive animal models is a pressing issue. Until recently, a reductionist approach using acute chondral defects in immature laboratory species, particularly the rabbit, was considered adequate; however, if successful and timely translation from animal models to regulatory approval and clinical use is the goal, a step-wise development using laboratory animals for screening and early development work followed by larger species such as the goat, sheep and horse for late development and pivotal studies is recommended. Such animals must have fully organized and mature cartilage. Both acute and chronic chondral defects can be used but the later are more like the lesions found in patients and may be more predictive. Quantitative and qualitative outcome measures such as macroscopic appearance, histology, biochemistry, functional imaging, and biomechanical testing of cartilage, provide reliable data to support investment decisions and subsequent applications to regulatory bodies for clinical trials. No one model or species can be considered ideal for pivotal studies, but the larger animal species are recommended for pivotal studies. Larger species such as the horse, goat and pig also allow arthroscopic delivery, and press-fit or sutured implant fixation in thick cartilage as well as second look arthroscopies and biopsy procedures. PMID:26069576

Animal versus human oral drug bioavailability: Do they correlate?

PubMed Central

Musther, Helen; Olivares-Morales, Andrés; Hatley, Oliver J.D.; Liu, Bo; Rostami Hodjegan, Amin

2014-01-01

Oral bioavailability is a key consideration in development of drug products, and the use of preclinical species in predicting bioavailability in human has long been debated. In order to clarify whether any correlation between human and animal bioavailability exist, an extensive analysis of the published literature data was conducted. Due to the complex nature of bioavailability calculations inclusion criteria were applied to ensure integrity of the data. A database of 184 compounds was assembled. Linear regression for the reported compounds indicated no strong or predictive correlations to human data for all species, individually and combined. The lack of correlation in this extended dataset highlights that animal bioavailability is not quantitatively predictive of bioavailability in human. Although qualitative (high/low bioavailability) indications might be possible, models taking into account species-specific factors that may affect bioavailability are recommended for developing quantitative prediction. PMID:23988844

The magnetic sense and its use in long-distance navigation by animals.

PubMed

Walker, Michael M; Dennis, Todd E; Kirschvink, Joseph L

2002-12-01

True navigation by animals is likely to depend on events occurring in the individual cells that detect magnetic fields. Minimum thresholds of detection, perception and 'interpretation' of magnetic field stimuli must be met if animals are to use a magnetic sense to navigate. Recent technological advances in animal tracking devices now make it possible to test predictions from models of navigation based on the use of variations in magnetic intensity.

Evaluation of a whole-farm model for pasture-based dairy systems.

PubMed

Beukes, P C; Palliser, C C; Macdonald, K A; Lancaster, J A S; Levy, G; Thorrold, B S; Wastney, M E

2008-06-01

In the temperate climate of New Zealand, animals can be grazed outdoors all year round. The pasture is supplemented with conserved feed, with the amount being determined by seasonal pasture growth, genetics of the herd, and stocking rate. The large number of factors that affect production makes it impractical and expensive to use field trials to explore all the farm system options. A model of an in situ-grazed pasture system has been developed to provide a tool for developing and testing novel farm systems; for example, different levels of bought-in supplements and different levels of nitrogen fertilizer application, to maintain sustainability or environmental integrity and profitability. It consists of a software framework that links climate information, on a daily basis, with dynamic, mechanistic component-models for pasture growth and animal metabolism, as well as management policies. A unique feature is that the component models were developed and published by other groups, and are retained in their original software language. The aim of this study was to compare the model, called the whole-farm model (WFM) with a farm trial that was conducted over 3 yr and in which data were collected specifically for evaluating the WFM. Data were used from the first year to develop the WFM and data from the second and third year to evaluate the model. The model predicted annual pasture production, end-of-season cow liveweight, cow body condition score, and pasture cover across season with relative prediction error <20%. Milk yield and milksolids (fat + protein) were overpredicted by approximately 30% even though both annual and monthly pasture and supplement intake were predicted with acceptable accuracy, suggesting that the metabolic conversion of feed to fat, protein, and lactose in the mammary gland needs to be refined. Because feed growth and intake predictions were acceptable, economic predictions can be made using the WFM, with an adjustment for milk yield, to test different management policies, alterations in climate, or the use of genetically improved animals, pastures, or crops.

Effective Antimicrobial Regimens for Use in Humans for Therapy of Bacillus anthracis Infections and Postexposure Prophylaxis†

PubMed Central

Deziel, Mark R.; Heine, Henry; Louie, Arnold; Kao, Mark; Byrne, William R.; Basset, Jennifer; Miller, Lynda; Bush, Karen; Kelly, Michael; Drusano, G. L.

2005-01-01

Expanded options for treatments directed against pathogens that can be used for bioterrorism are urgently needed. Treatment regimens directed against such pathogens can be identified only by using data derived from in vitro and animal studies. It is crucial that these studies reliably predict the efficacy of proposed treatments in humans. The objective of this study was to identify a levofloxacin treatment regimen that will serve as an effective therapy for Bacillus anthracis infections and postexposure prophylaxis. An in vitro hollow-fiber infection model that replicates the pharmacokinetic profile of levofloxacin observed in humans (half-life [t1/2], 7.5 h) or in animals, such as the mouse or the rhesus monkey (t1/2, ∼2 h), was used to evaluate a proposed indication for levofloxacin (500 mg once daily) for the treatment of Bacillus anthracis infections. The results obtained with the in vitro model served as the basis for the doses and the dose schedules that were evaluated in the mouse inhalational anthrax model. The effects of levofloxacin and ciprofloxacin treatment were compared to those of no treatment (untreated controls). The main outcome measure in the in vitro hollow-fiber infection model was a persistent reduction of culture density (≥4 log10 reduction) and prevention of the emergence of levofloxacin-resistant organisms. In the mouse inhalational anthrax model the main outcome measure was survival. The results indicated that levofloxacin given once daily with simulated human pharmacokinetics effectively sterilized Bacillus anthracis cultures. By using a simulated animal pharmacokinetic profile, a once-daily dosing regimen that provided a human-equivalent exposure failed to sterilize the cultures. Dosing regimens that “partially humanized” levofloxacin exposures within the constraints of animal pharmacokinetics reproduced the antimicrobial efficacy seen with human pharmacokinetics. In a mouse inhalational anthrax model, once-daily dosing was significantly inferior (survival end point) to regimens of dosing every 12 h or every 6 h with identical total daily levofloxacin doses. These results demonstrate the predictive value of the in vitro hollow-fiber infection model with respect to the success or the failure of treatment regimens in animals. Furthermore, the model permits the evaluation of treatment regimens that “humanize” antibiotic exposures in animal models, enhancing the confidence with which animal models may be used to reliably predict the efficacies of proposed antibiotic treatments in humans in situations (e.g., the release of pathogens as agents of bioterrorism or emerging infectious diseases) where human trials cannot be performed. A treatment regimen effective in rhesus monkeys was identified. PMID:16304178

Exploring the Validity of Proposed Transgenic Animal Models of Attention-Deficit Hyperactivity Disorder (ADHD).

PubMed

de la Peña, June Bryan; Dela Peña, Irene Joy; Custodio, Raly James; Botanas, Chrislean Jun; Kim, Hee Jin; Cheong, Jae Hoon

2018-05-01

Attention-deficit/hyperactivity disorder (ADHD) is a common, behavioral, and heterogeneous neurodevelopmental condition characterized by hyperactivity, impulsivity, and inattention. Symptoms of this disorder are managed by treatment with methylphenidate, amphetamine, and/or atomoxetine. The cause of ADHD is unknown, but substantial evidence indicates that this disorder has a significant genetic component. Transgenic animals have become an essential tool in uncovering the genetic factors underlying ADHD. Although they cannot accurately reflect the human condition, they can provide insights into the disorder that cannot be obtained from human studies due to various limitations. An ideal animal model of ADHD must have face (similarity in symptoms), predictive (similarity in response to treatment or medications), and construct (similarity in etiology or underlying pathophysiological mechanism) validity. As the exact etiology of ADHD remains unclear, the construct validity of animal models of ADHD would always be limited. The proposed transgenic animal models of ADHD have substantially increased and diversified over the years. In this paper, we compiled and explored the validity of proposed transgenic animal models of ADHD. Each of the reviewed transgenic animal models has strengths and limitations. Some fulfill most of the validity criteria of an animal model of ADHD and have been extensively used, while there are others that require further validation. Nevertheless, these transgenic animal models of ADHD have provided and will continue to provide valuable insights into the genetic underpinnings of this complex disorder.

Do pigeons (Columba livia) use information about the absence of food appropriately? A further look into suboptimal choice.

PubMed

Fortes, Inês; Machado, Armando; Vasconcelos, Marco

2017-11-01

In the natural environment, when an animal encounters a stimulus that signals the absence of food-a 'bad-news' stimulus-it will most likely redirect its search to another patch or prey. Because the animal does not pay the opportunity cost of waiting in the presence of a bad-news stimulus, the properties of the stimulus (e.g., its duration and probability) may have little impact in the evolution of the decision processes deployed in these circumstances. Hence, in the laboratory, when animals are forced to experience a bad-news stimulus they seem to ignore its duration, even though they pay the cost of waiting. Under certain circumstances, this insensitivity to the opportunity cost can lead to suboptimal preferences, such as a preference for an option yielding a low rather than a high rate of reinforcement. In 2 experiments, we tested Vasconcelos, Monteiro, and Kacelnik's (2015) assumption that, if given the opportunity, animals will escape the bad-news stimulus. To predict when an escape response should occur, we incorporated ideas from the prey choice model into Vasconcelos et al. (2015) model and made 2 novel predictions. Namely, both longer intertrial intervals and longer durations of signals predicting food or no food should lead to higher proportions of escape responses. The results of 2 experiments with pigeons supported these predictions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Paul R., E-mail: pwest@stemina.co; Weir, April M.; Smith, Alan M.

2010-08-15

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statisticalmore » analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.« less

The flaws and human harms of animal experimentation.

PubMed

Akhtar, Aysha

2015-10-01

Nonhuman animal ("animal") experimentation is typically defended by arguments that it is reliable, that animals provide sufficiently good models of human biology and diseases to yield relevant information, and that, consequently, its use provides major human health benefits. I demonstrate that a growing body of scientific literature critically assessing the validity of animal experimentation generally (and animal modeling specifically) raises important concerns about its reliability and predictive value for human outcomes and for understanding human physiology. The unreliability of animal experimentation across a wide range of areas undermines scientific arguments in favor of the practice. Additionally, I show how animal experimentation often significantly harms humans through misleading safety studies, potential abandonment of effective therapeutics, and direction of resources away from more effective testing methods. The resulting evidence suggests that the collective harms and costs to humans from animal experimentation outweigh potential benefits and that resources would be better invested in developing human-based testing methods.

Mechanism - based translational pharmacokinetic - pharmacodynamic model to predict intraocular pressure lowering effect of drugs in patients with glaucoma or ocular hypertension.

PubMed

Durairaj, Chandrasekar; Shen, Jie; Cherukury, Madhu

2014-08-01

To develop a mechanism based translational pharmacokinetic-pharmacodynamic (PKPD) model in preclinical species and to predict the intraocular pressure (IOP) following drug treatment in patients with glaucoma or ocular hypertension (OHT). Baseline diurnal IOP of normotensive albino rabbits, beagle dogs and patients with glaucoma or OHT was collected from literature. In addition, diurnal IOP of patients treated with brimonidine or Xalatan® were also obtained from literature. Healthy normotensive New Zealand rabbits were topically treated with a single drop of 0.15% brimonidine tartrate and normotensive beagle dogs were treated with a single drop of Xalatan®. At pre-determined time intervals, IOP was measured and aqueous humor samples were obtained from a satellite group of animals. Population based PKPD modeling was performed to describe the IOP data and the chosen model was extended to predict the IOP in patients. Baseline IOP clearly depicts a distinctive circadian rhythm in rabbits versus human. An aqueous humor dynamics based physiological model was developed to describe the baseline diurnal IOP across species. Model was extended to incorporate the effect of drug administration on baseline IOP in rabbits and dogs. The translational model with substituted human aqueous humor dynamic parameters predicted IOP in patients following drug treatment. A physiology based mechanistic PKPD model was developed to describe the baseline and post-treatment IOP in animals. The preclinical PKPD model was successfully translated to predict IOP in patients with glaucoma or OHT and can be applied in assisting dose and treatment selection and predicting outcome of glaucoma clinical trials.

Properties of animal-manure based hydrochars and predictions using published models

USDA-ARS?s Scientific Manuscript database

In order to fully utilize hydrothermal carbonization (HTC) to produce value-added hydrochars from animal manures, it is important to understand how process conditions (e.g., temperature, reaction time, solids concentration) influence product characteristics. The effect of process conditions on the e...

Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus

NASA Astrophysics Data System (ADS)

Lobo, Daniel; Lobikin, Maria; Levin, Michael

2017-01-01

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.

The aerodynamic cost of flight in the short-tailed fruit bat (Carollia perspicillata): comparing theory with measurement

PubMed Central

von Busse, Rhea; Waldman, Rye M.; Swartz, Sharon M.; Voigt, Christian C.; Breuer, Kenneth S.

2014-01-01

Aerodynamic theory has long been used to predict the power required for animal flight, but widely used models contain many simplifications. It has been difficult to ascertain how closely biological reality matches model predictions, largely because of the technical challenges of accurately measuring the power expended when an animal flies. We designed a study to measure flight speed-dependent aerodynamic power directly from the kinetic energy contained in the wake of bats flying in a wind tunnel. We compared these measurements with two theoretical predictions that have been used for several decades in diverse fields of vertebrate biology and to metabolic measurements from a previous study using the same individuals. A high-accuracy displaced laser sheet stereo particle image velocimetry experimental design measured the wake velocities in the Trefftz plane behind four bats flying over a range of speeds (3–7 m s−1). We computed the aerodynamic power contained in the wake using a novel interpolation method and compared these results with the power predicted by Pennycuick's and Rayner's models. The measured aerodynamic power falls between the two theoretical predictions, demonstrating that the models effectively predict the appropriate range of flight power, but the models do not accurately predict minimum power or maximum range speeds. Mechanical efficiency—the ratio of aerodynamic power output to metabolic power input—varied from 5.9% to 9.8% for the same individuals, changing with flight speed. PMID:24718450

Reverse-translational biomarker validation of Abnormal Repetitive Behaviors in mice: an illustration of the 4P's modeling approach.

PubMed

Garner, Joseph P; Thogerson, Collette M; Dufour, Brett D; Würbel, Hanno; Murray, James D; Mench, Joy A

2011-06-01

The NIMH's new strategic plan, with its emphasis on the "4P's" (Prediction, Pre-emption, Personalization, and Populations) and biomarker-based medicine requires a radical shift in animal modeling methodology. In particular 4P's models will be non-determinant (i.e. disease severity will depend on secondary environmental and genetic factors); and validated by reverse-translation of animal homologues to human biomarkers. A powerful consequence of the biomarker approach is that different closely related disorders have a unique fingerprint of biomarkers. Animals can be validated as a highly specific model of a single disorder by matching this 'fingerprint'; or as a model of a symptom seen in multiple disorders by matching common biomarkers. Here we illustrate this approach with two Abnormal Repetitive Behaviors (ARBs) in mice: stereotypies and barbering (hair pulling). We developed animal versions of the neuropsychological biomarkers that distinguish human ARBs, and tested the fingerprint of the different mouse ARBs. As predicted, the two mouse ARBs were associated with different biomarkers. Both barbering and stereotypy could be discounted as models of OCD (even though they are widely used as such), due to the absence of limbic biomarkers which are characteristic of OCD and hence are necessary for a valid model. Conversely barbering matched the fingerprint of trichotillomania (i.e. selective deficits in set-shifting), suggesting it may be a highly specific model of this disorder. In contrast stereotypies were correlated only with a biomarker (deficits in response shifting) correlated with stereotypies in multiple disorders, suggesting that animal stereotypies model stereotypies in multiple disorders. Copyright © 2011 Elsevier B.V. All rights reserved.

Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50).

PubMed

Sazonovas, A; Japertas, P; Didziapetris, R

2010-01-01

This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.

Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

NASA Astrophysics Data System (ADS)

Guha, Rajarshi; Schürer, Stephan C.

2008-06-01

Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.

Are Plant Species Able to Keep Pace with the Rapidly Changing Climate?

PubMed Central

Cunze, Sarah; Heydel, Felix; Tackenberg, Oliver

2013-01-01

Future climate change is predicted to advance faster than the postglacial warming. Migration may therefore become a key driver for future development of biodiversity and ecosystem functioning. For 140 European plant species we computed past range shifts since the last glacial maximum and future range shifts for a variety of Intergovernmental Panel on Climate Change (IPCC) scenarios and global circulation models (GCMs). Range shift rates were estimated by means of species distribution modelling (SDM). With process-based seed dispersal models we estimated species-specific migration rates for 27 dispersal modes addressing dispersal by wind (anemochory) for different wind conditions, as well as dispersal by mammals (dispersal on animal's coat – epizoochory and dispersal by animals after feeding and digestion – endozoochory) considering different animal species. Our process-based modelled migration rates generally exceeded the postglacial range shift rates indicating that the process-based models we used are capable of predicting migration rates that are in accordance with realized past migration. For most of the considered species, the modelled migration rates were considerably lower than the expected future climate change induced range shift rates. This implies that most plant species will not entirely be able to follow future climate-change-induced range shifts due to dispersal limitation. Animals with large day- and home-ranges are highly important for achieving high migration rates for many plant species, whereas anemochory is relevant for only few species. PMID:23894290

Childhood cruelty to animals in China: the relationship with psychological adjustment and family functioning.

PubMed

Wong, J; Mellor, D; Richardson, B; Xu, X

2013-09-01

The current study broadened the general scope of research conducted on childhood cruelty to animals by examining the association between psychological adjustment, family functioning and animal cruelty in an Eastern context, China. The mothers and fathers of 729 children attending primary school in Chengdu, China participated in this study. Each parent completed the Strengths and Difficulties Questionnaire, the Chinese Family Assessment Instrument, and the Children's Attitudes and Behaviours towards Animals questionnaire. Findings from an actor partner interdependence model demonstrated that parents' ratings of family functioning and of their child's externalizing coping style predicted only modest amounts of variance in animal cruelty. In particular, parents' ratings of their child's externalizing coping style most consistently predicted animal cruelty. Family functioning, fathers' ratings in particular, played a minor role, more so for boys compared with girls. This study provided the first insight into childhood animal cruelty in China, and suggests that further research may enhance our understanding of these phenomena. © 2012 John Wiley & Sons Ltd.

Improving production efficiency through genetic selection

USDA-ARS?s Scientific Manuscript database

The goal of dairy cattle breeding is to increase productivity and efficiency by means of genetic selection. This is possible because related animals share some of their DNA in common, and we can use statistical models to predict the genetic merit animals based on the performance of their relatives. ...

Predicting caregiver burden in general veterinary clients: Contribution of companion animal clinical signs and problem behaviors.

PubMed

Spitznagel, M B; Jacobson, D M; Cox, M D; Carlson, M D

2018-06-01

Caregiver burden, found in many clients with a chronically or terminally ill companion animal, has been linked to poorer psychosocial function in the client and greater utilization of non-billable veterinary services. To reduce client caregiver burden, its determinants must first be identified. This study examined if companion animal clinical signs and problem behaviors predict veterinary client burden within broader client- and patient-based risk factor models. Data were collected in two phases. Phase 1 included 238 companion animal owners, including those with a sick companion animal (n=119) and matched healthy controls (n=119) recruited online. Phase 2 was comprised of 602 small animal general veterinary hospital clients (n=95 with a sick dog or cat). Participants completed cross-sectional online assessments of caregiver burden, psychosocial resources (social support, active coping, self-mastery), and an item pool of companion animal clinical signs and problem behaviors. Several signs/behaviors correlated with burden, most prominently: weakness, appearing sad/depressed or anxious, appearing to have pain/discomfort, change in personality, frequent urination, and excessive sleeping/lethargy. Within patient-based risk factors, caregiver burden was predicted by frequency of the companion animal's signs/behaviors (P<.01). Within client-based factors, potentially modifiable factors of client reaction to the animal's signs/behaviors (P=.01), and client sense of control (P<.04) predicted burden. Understanding burden may enhance veterinarian-client communication, and is important due to potential downstream effects of client burden, such as higher workload for the veterinarian. Supporting the client's sense of control may help alleviate burden when amelioration of the companion animal's presentation is not feasible. Copyright © 2018 Elsevier Ltd. All rights reserved.

Intraoperative neural monitoring in thyroid surgery: lessons learned from animal studies

PubMed Central

Randolph, Gregory W.; Lu, I-Cheng; Chang, Pi-Ying; Chen, Yi-Ting; Hun, Pao-Chu; Lin, Yi-Chu; Dionigi, Gianlorenzo; Chiang, Feng-Yu

2016-01-01

Recurrent laryngeal nerve (RLN) injury remains a significant morbidity associated with thyroid and parathyroid surgery. In the past decade, surgeons have increasingly used intraoperative neural monitoring (IONM) as an adjunct technique for localizing and identifying the RLN, detecting RLN injury, and predicting the outcome of vocal cord function. In recent years, many animal studies have investigated common pitfalls and new applications of IONM. For example, the use of IONM technology in animal models has proven valuable in studies of the electrophysiology of RLN injury. The advent of animal studies has substantially improved understanding of IONM technology. Lessons learned from animal studies have immediate clinical applications in establishing reliable strategies for preventing intraoperative RLN injury. This article gives an overview of the research progress on IONM-relevant animal models. PMID:27867861

Modeling mania in preclinical settings: a comprehensive review

PubMed Central

Sharma, Ajaykumar N.; Fries, Gabriel R.; Galvez, Juan F.; Valvassori, Samira S.; Soares, Jair C.; Carvalho, André F.; Quevedo, Joao

2015-01-01

The current pathophysiological understanding of mechanisms leading to onset and progression of bipolar manic episodes remains limited. At the same time, available animal models for mania have limited face, construct, and predictive validities. Additionally, these models fail to encompass recent pathophysiological frameworks of bipolar disorder (BD), e.g. neuroprogression. Therefore, there is a need to search for novel preclinical models for mania that could comprehensively address these limitations. Herein we review the history, validity, and caveats of currently available animal models for mania. We also review new genetic models for mania, namely knockout mice for genes involved in neurotransmission, synapse formation, and intracellular signaling pathways. Furthermore, we review recent trends in preclinical models for mania that may aid in the comprehension of mechanisms underlying the neuroprogressive and recurring nature of BD. In conclusion, the validity of animal models for mania remains limited. Nevertheless, novel (e.g. genetic) animal models as well as adaptation of existing paradigms hold promise. PMID:26545487

Universal model for water costs of gas exchange by animals and plants

PubMed Central

Woods, H. Arthur; Smith, Jennifer N.

2010-01-01

For terrestrial animals and plants, a fundamental cost of living is water vapor lost to the atmosphere during exchange of metabolic gases. Here, by bringing together previously developed models for specific taxa, we integrate properties common to all terrestrial gas exchangers into a universal model of water loss. The model predicts that water loss scales to gas exchange with an exponent of 1 and that the amount of water lost per unit of gas exchanged depends on several factors: the surface temperature of the respiratory system near the outside of the organism, the gas consumed (oxygen or carbon dioxide), the steepness of the gradients for gas and vapor, and the transport mode (convective or diffusive). Model predictions were largely confirmed by data on 202 species in five taxa—insects, birds, bird eggs, mammals, and plants—spanning nine orders of magnitude in rate of gas exchange. Discrepancies between model predictions and data seemed to arise from biologically interesting violations of model assumptions, which emphasizes how poorly we understand gas exchange in some taxa. The universal model provides a unified conceptual framework for analyzing exchange-associated water losses across taxa with radically different metabolic and exchange systems. PMID:20404161

Universal model for water costs of gas exchange by animals and plants.

PubMed

Woods, H Arthur; Smith, Jennifer N

2010-05-04

For terrestrial animals and plants, a fundamental cost of living is water vapor lost to the atmosphere during exchange of metabolic gases. Here, by bringing together previously developed models for specific taxa, we integrate properties common to all terrestrial gas exchangers into a universal model of water loss. The model predicts that water loss scales to gas exchange with an exponent of 1 and that the amount of water lost per unit of gas exchanged depends on several factors: the surface temperature of the respiratory system near the outside of the organism, the gas consumed (oxygen or carbon dioxide), the steepness of the gradients for gas and vapor, and the transport mode (convective or diffusive). Model predictions were largely confirmed by data on 202 species in five taxa--insects, birds, bird eggs, mammals, and plants--spanning nine orders of magnitude in rate of gas exchange. Discrepancies between model predictions and data seemed to arise from biologically interesting violations of model assumptions, which emphasizes how poorly we understand gas exchange in some taxa. The universal model provides a unified conceptual framework for analyzing exchange-associated water losses across taxa with radically different metabolic and exchange systems.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

Sound transmission in the chest under surface excitation - An experimental and computational study with diagnostic applications

PubMed Central

Peng, Ying; Dai, Zoujun; Mansy, Hansen A.; Sandler, Richard H.; Balk, Robert A; Royston, Thomas. J

2014-01-01

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions. PMID:25001497

Earthquake Prediction is Coming

ERIC Educational Resources Information Center

MOSAIC, 1977

1977-01-01

Describes (1) several methods used in earthquake research, including P:S ratio velocity studies, dilatancy models; and (2) techniques for gathering base-line data for prediction using seismographs, tiltmeters, laser beams, magnetic field changes, folklore, animal behavior. The mysterious Palmdale (California) bulge is discussed. (CS)

Pharmacokinetics of Mequindox and Its Marker Residue 1,4-Bisdesoxymequindox in Swine Following Multiple Oral Gavage and Intramuscular Administration: An Experimental Study Coupled with Population Physiologically Based Pharmacokinetic Modeling.

PubMed

Zeng, Dongping; Lin, Zhoumeng; Fang, Binghu; Li, Miao; Gehring, Ronette; Riviere, Jim E; Zeng, Zhenling

2017-07-19

Mequindox (MEQ) is a quinoxaline-N,N-dioxide antibiotic used in food-producing animals. MEQ residue in animal-derived foods is a food safety concern. The tissue distribution of MEQ and its marker residue 1,4-bisdesoxymequindox (M1) were determined in swine following oral gavage or intramuscular injection twice daily for 3 days. The experimental data were used to construct a flow-limited physiologically based pharmacokinetic (PBPK) model. The model predictions correlated with available data well. Monte Carlo analysis showed that the times needed for M1 concentrations to fall below limit of detection (5 μg/kg) in liver for the 99th percentile of the population were 27 and 34 days after oral gavage and intramuscular administration twice daily for 3 days, respectively. This population PBPK model can be used to predict depletion kinetic profiles and tissue residues of MEQ's marker residue M1 in swine and as a foundation for scaling to other quinoxaline-N,N-dioxide antibiotics and to other animal species.

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research.

PubMed

Weidner, Christopher; Steinfath, Matthias; Wistorf, Elisa; Oelgeschläger, Michael; Schneider, Marlon R; Schönfelder, Gilbert

2017-08-16

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

Evaluation of animal models of neurobehavioral disorders

PubMed Central

van der Staay, F Josef; Arndt, Saskia S; Nordquist, Rebecca E

2009-01-01

Animal models play a central role in all areas of biomedical research. The process of animal model building, development and evaluation has rarely been addressed systematically, despite the long history of using animal models in the investigation of neuropsychiatric disorders and behavioral dysfunctions. An iterative, multi-stage trajectory for developing animal models and assessing their quality is proposed. The process starts with defining the purpose(s) of the model, preferentially based on hypotheses about brain-behavior relationships. Then, the model is developed and tested. The evaluation of the model takes scientific and ethical criteria into consideration. Model development requires a multidisciplinary approach. Preclinical and clinical experts should establish a set of scientific criteria, which a model must meet. The scientific evaluation consists of assessing the replicability/reliability, predictive, construct and external validity/generalizability, and relevance of the model. We emphasize the role of (systematic and extended) replications in the course of the validation process. One may apply a multiple-tiered 'replication battery' to estimate the reliability/replicability, validity, and generalizability of result. Compromised welfare is inherent in many deficiency models in animals. Unfortunately, 'animal welfare' is a vaguely defined concept, making it difficult to establish exact evaluation criteria. Weighing the animal's welfare and considerations as to whether action is indicated to reduce the discomfort must accompany the scientific evaluation at any stage of the model building and evaluation process. Animal model building should be discontinued if the model does not meet the preset scientific criteria, or when animal welfare is severely compromised. The application of the evaluation procedure is exemplified using the rat with neonatal hippocampal lesion as a proposed model of schizophrenia. In a manner congruent to that for improving animal models, guided by the procedure expounded upon in this paper, the developmental and evaluation procedure itself may be improved by careful definition of the purpose(s) of a model and by defining better evaluation criteria, based on the proposed use of the model. PMID:19243583

Preventing Drug-Induced Liver Injury: How Useful Are Animal Models?

PubMed

Ballet, François

2015-01-01

Drug-induced liver injury (DILI) is the most common organ toxicity encountered in regulatory animal toxicology studies required prior to the clinical development of new drug candidates. Very few reports have evaluated the value of these studies for predicting DILI in humans. Indeed, compounds inducing liver toxicity in regulatory toxicology studies are not always correlated with a risk of DILI in humans. Conversely, compounds associated with the occurrence of DILI in phase 3 studies or after market release are often tested negative in regulatory toxicology studies. Idiosyncratic DILI is a rare event that is precipitated in an individual by the simultaneous occurrence of several critical factors. These factors may relate to the host (e.g. human leukocyte antigen polymorphism, inflammation), the drug (e.g. reactive metabolites) or the environment (e.g. diet/microbiota). This type of toxicity therefore cannot be detected in conventional animal toxicology studies. Several animal models have recently been proposed for the identification of drugs with the potential to cause idiosyncratic DILI: rats treated with lipopolysaccharide, Sod2(+/-) mice, panels of inbred mouse strains or chimeric mice with humanized livers. These models are not suitable for use in the prospective screening of new drug candidates. Humans therefore constitute the best model for predicting and assessing idiopathic DILI. © 2015 S. Karger AG, Basel.

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

PubMed Central

2014-01-01

Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

New Breed of Mice May Improve Accuracy for Preclinical Testing of Cancer Drugs | FNLCR Staging

Cancer.gov

A new breed of lab animals, dubbed “glowing head mice,” may do a better job than conventional mice in predicting the success of experimental cancer drugs—while also helping to meet an urgent need for more realistic preclinical animal models. Th

Predictable enriched environment prevents development of hyper-emotionality in the VPA rat model of autism.

PubMed

Favre, Mônica R; La Mendola, Deborah; Meystre, Julie; Christodoulou, Dimitri; Cochrane, Melissa J; Markram, Henry; Markram, Kamila

2015-01-01

Understanding the effects of environmental stimulation in autism can improve therapeutic interventions against debilitating sensory overload, social withdrawal, fear and anxiety. Here, we evaluate the role of environmental predictability on behavior and protein expression, and inter-individual differences, in the valproic acid (VPA) model of autism. Male rats embryonically exposed (E11.5) either to VPA, a known autism risk factor in humans, or to saline, were housed from weaning into adulthood in a standard laboratory environment, an unpredictably enriched environment, or a predictably enriched environment. Animals were tested for sociability, nociception, stereotypy, fear conditioning and anxiety, and for tissue content of glutamate signaling proteins in the primary somatosensory cortex, hippocampus and amygdala, and of corticosterone in plasma, amygdala and hippocampus. Standard group analyses on separate measures were complemented with a composite emotionality score, using Cronbach's Alpha analysis, and with multivariate profiling of individual animals, using Hierarchical Cluster Analysis. We found that predictable environmental enrichment prevented the development of hyper-emotionality in the VPA-exposed group, while unpredictable enrichment did not. Individual variation in the severity of the autistic-like symptoms (fear, anxiety, social withdrawal and sensory abnormalities) correlated with neurochemical profiles, and predicted their responsiveness to predictability in the environment. In controls, the association between socio-affective behaviors, neurochemical profiles and environmental predictability was negligible. This study suggests that rearing in a predictable environment prevents the development of hyper-emotional features in animals exposed to an autism risk factor, and demonstrates that unpredictable environments can lead to negative outcomes, even in the presence of environmental enrichment.

Predictable enriched environment prevents development of hyper-emotionality in the VPA rat model of autism

PubMed Central

Favre, Mônica R.; La Mendola, Deborah; Meystre, Julie; Christodoulou, Dimitri; Cochrane, Melissa J.; Markram, Henry; Markram, Kamila

2015-01-01

Understanding the effects of environmental stimulation in autism can improve therapeutic interventions against debilitating sensory overload, social withdrawal, fear and anxiety. Here, we evaluate the role of environmental predictability on behavior and protein expression, and inter-individual differences, in the valproic acid (VPA) model of autism. Male rats embryonically exposed (E11.5) either to VPA, a known autism risk factor in humans, or to saline, were housed from weaning into adulthood in a standard laboratory environment, an unpredictably enriched environment, or a predictably enriched environment. Animals were tested for sociability, nociception, stereotypy, fear conditioning and anxiety, and for tissue content of glutamate signaling proteins in the primary somatosensory cortex, hippocampus and amygdala, and of corticosterone in plasma, amygdala and hippocampus. Standard group analyses on separate measures were complemented with a composite emotionality score, using Cronbach's Alpha analysis, and with multivariate profiling of individual animals, using Hierarchical Cluster Analysis. We found that predictable environmental enrichment prevented the development of hyper-emotionality in the VPA-exposed group, while unpredictable enrichment did not. Individual variation in the severity of the autistic-like symptoms (fear, anxiety, social withdrawal and sensory abnormalities) correlated with neurochemical profiles, and predicted their responsiveness to predictability in the environment. In controls, the association between socio-affective behaviors, neurochemical profiles and environmental predictability was negligible. This study suggests that rearing in a predictable environment prevents the development of hyper-emotional features in animals exposed to an autism risk factor, and demonstrates that unpredictable environments can lead to negative outcomes, even in the presence of environmental enrichment. PMID:26089770

Animal models of listeriosis: a comparative review of the current state of the art and lessons learned

PubMed Central

2012-01-01

Listeriosis is a leading cause of hospitalization and death due to foodborne illness in the industrialized world. Animal models have played fundamental roles in elucidating the pathophysiology and immunology of listeriosis, and will almost certainly continue to be integral components of the research on listeriosis. Data derived from animal studies helped for example characterize the importance of cell-mediated immunity in controlling infection, allowed evaluation of chemotherapeutic treatments for listeriosis, and contributed to quantitative assessments of the public health risk associated with L. monocytogenes contaminated food commodities. Nonetheless, a number of pivotal questions remain unresolved, including dose-response relationships, which represent essential components of risk assessments. Newly emerging data about species-specific differences have recently raised concern about the validity of most traditional animal models of listeriosis. However, considerable uncertainty about the best choice of animal model remains. Here we review the available data on traditional and potential new animal models to summarize currently recognized strengths and limitations of each model. This knowledge is instrumental for devising future studies and for interpreting current data. We deliberately chose a historical, comparative and cross-disciplinary approach, striving to reveal clues that may help predict the ultimate value of each animal model in spite of incomplete data. PMID:22417207

Animal models of listeriosis: a comparative review of the current state of the art and lessons learned.

PubMed

Hoelzer, Karin; Pouillot, Régis; Dennis, Sherri

2012-03-14

Listeriosis is a leading cause of hospitalization and death due to foodborne illness in the industrialized world. Animal models have played fundamental roles in elucidating the pathophysiology and immunology of listeriosis, and will almost certainly continue to be integral components of the research on listeriosis. Data derived from animal studies helped for example characterize the importance of cell-mediated immunity in controlling infection, allowed evaluation of chemotherapeutic treatments for listeriosis, and contributed to quantitative assessments of the public health risk associated with L. monocytogenes contaminated food commodities. Nonetheless, a number of pivotal questions remain unresolved, including dose-response relationships, which represent essential components of risk assessments. Newly emerging data about species-specific differences have recently raised concern about the validity of most traditional animal models of listeriosis. However, considerable uncertainty about the best choice of animal model remains. Here we review the available data on traditional and potential new animal models to summarize currently recognized strengths and limitations of each model. This knowledge is instrumental for devising future studies and for interpreting current data. We deliberately chose a historical, comparative and cross-disciplinary approach, striving to reveal clues that may help predict the ultimate value of each animal model in spite of incomplete data.

Simulating Polar Bear Energetics during a Seasonal Fast Using a Mechanistic Model

PubMed Central

Mathewson, Paul D.; Porter, Warren P.

2013-01-01

In this study we tested the ability of a mechanistic model (Niche Mapper™) to accurately model adult, non-denning polar bear (Ursus maritimus) energetics while fasting during the ice-free season in the western Hudson Bay. The model uses a steady state heat balance approach, which calculates the metabolic rate that will allow an animal to maintain its core temperature in its particular microclimate conditions. Predicted weight loss for a 120 day fast typical of the 1990s was comparable to empirical studies of the population, and the model was able to reach a heat balance at the target metabolic rate for the entire fast, supporting use of the model to explore the impacts of climate change on polar bears. Niche Mapper predicted that all but the poorest condition bears would survive a 120 day fast under current climate conditions. When the fast extended to 180 days, Niche Mapper predicted mortality of up to 18% for males. Our results illustrate how environmental conditions, variation in animal properties, and thermoregulation processes may impact survival during extended fasts because polar bears were predicted to require additional energetic expenditure for thermoregulation during a 180 day fast. A uniform 3°C temperature increase reduced male mortality during a 180 day fast from 18% to 15%. Niche Mapper explicitly links an animal’s energetics to environmental conditions and thus can be a valuable tool to help inform predictions of climate-related population changes. Since Niche Mapper is a generic model, it can make energetic predictions for other species threatened by climate change. PMID:24019883

Can we use genetic and genomic approaches to identify candidate animals for targeted selective treatment.

PubMed

Laurenson, Yan C S M; Kyriazakis, Ilias; Bishop, Stephen C

2013-10-18

Estimated breeding values (EBV) for faecal egg count (FEC) and genetic markers for host resistance to nematodes may be used to identify resistant animals for selective breeding programmes. Similarly, targeted selective treatment (TST) requires the ability to identify the animals that will benefit most from anthelmintic treatment. A mathematical model was used to combine the concepts and evaluate the potential of using genetic-based methods to identify animals for a TST regime. EBVs obtained by genomic prediction were predicted to be the best determinant criterion for TST in terms of the impact on average empty body weight and average FEC, whereas pedigree-based EBVs for FEC were predicted to be marginally worse than using phenotypic FEC as a determinant criterion. Whilst each method has financial implications, if the identification of host resistance is incorporated into a wider genomic selection indices or selective breeding programmes, then genetic or genomic information may be plausibly included in TST regimes. Copyright © 2013 Elsevier B.V. All rights reserved.

Population sensitivities of animals to chronic ionizing radiation-model predictions from mice to elephant.

PubMed

Sazykina, Tatiana G

2018-02-01

Model predictions of population response to chronic ionizing radiation (endpoint 'morbidity') were made for 11 species of warm-blooded animals, differing in body mass and lifespan - from mice to elephant. Predictions were made also for 3 bird species (duck, pigeon, and house sparrow). Calculations were based on analytical solutions of the mathematical model, simulating a population response to low-LET ionizing radiation in an ecosystem with a limiting resource (Sazykina, Kryshev, 2016). Model parameters for different species were taken from biological and radioecological databases; allometric relationships were employed for estimating some parameter values. As a threshold of decreased health status in exposed populations ('health threshold'), a 10% reduction in self-repairing capacity of organisms was suggested, associated with a decline in ability to sustain environmental stresses. Results of the modeling demonstrate a general increase of population vulnerability to ionizing radiation in animal species of larger size and longevity. Populations of small widespread species (mice, house sparrow; body mass 20-50 g), which are characterized by intensive metabolism and short lifespan, have calculated 'health thresholds' at dose rates about 6.5-7.5 mGy day -1 . Widespread animals with body mass 200-500 g (rat, common pigeon) - demonstrate 'health threshold' values at 4-5 mGy day -1 . For populations of animals with body mass 2-5 kg (rabbit, fox, raccoon), the indicators of 10% health decrease are in the range 2-3.4 mGy day -1 . For animals with body mass 40-100 kg (wolf, sheep, wild boar), thresholds are within 0.5-0.8 mGy day -1 ; for herbivorous animals with body mass 200-300 kg (deer, horse) - 0.5-0.6 mGy day -1 . The lowest health threshold was estimated for elephant (body mass around 5000 kg) - 0.1 mGy day -1 . According to the model results, the differences in population sensitivities of warm-blooded animal species to ionizing radiation are generally depended on the metabolic rate and longevity of organisms, also on individual radiosensitivity of biological tissues. The results of 'health threshold' calculations are formulated as a graded scale of wildlife sensitivities to chronic radiation stress, ranging from potentially vulnerable to more resistant species. Further studies are needed to expand the scale of population sensitivities to radiation, including other groups of wildlife - cold-blooded species, invertebrates, and plants. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mortality of atomic bomb survivors predicted from laboratory animals

NASA Technical Reports Server (NTRS)

Carnes, Bruce A.; Grahn, Douglas; Hoel, David

2003-01-01

Exposure, pathology and mortality data for mice, dogs and humans were examined to determine whether accurate interspecies predictions of radiation-induced mortality could be achieved. The analyses revealed that (1) days of life lost per unit dose can be estimated for a species even without information on radiation effects in that species, and (2) accurate predictions of age-specific radiation-induced mortality in beagles and the atomic bomb survivors can be obtained from a dose-response model for comparably exposed mice. These findings illustrate the value of comparative mortality analyses and the relevance of animal data to the study of human health effects.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Accuracy of genomic prediction using deregressed breeding values estimated from purebred and crossbred offspring phenotypes in pigs.

PubMed

Hidalgo, A M; Bastiaansen, J W M; Lopes, M S; Veroneze, R; Groenen, M A M; de Koning, D-J

2015-07-01

Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit.

Predicting skin sensitization potential and inter-laboratory reproducibility of a human Cell Line Activation Test (h-CLAT) in the European Cosmetics Association (COLIPA) ring trials.

PubMed

Sakaguchi, Hitoshi; Ryan, Cindy; Ovigne, Jean-Marc; Schroeder, Klaus R; Ashikaga, Takao

2010-09-01

Regulatory policies in Europe prohibited the testing of cosmetic ingredients in animals for a number of toxicological endpoints. Currently no validated non-animal test methods exist for skin sensitization. Evaluation of changes in cell surface marker expression in dendritic cell (DC)-surrogate cell lines represents one non-animal approach. The human Cell Line Activation Test (h-CLAT) examines the level of CD86 and CD54 expression on the surface of THP-1 cells, a human monocytic leukemia cell line, following 24h of chemical exposure. To examine protocol transferability, between-lab reproducibility, and predictive capacity, the h-CLAT has been evaluated by five independent laboratories in several ring trials (RTs) coordinated by the European Cosmetics Association (COLIPA). The results of the first and second RTs demonstrated that the protocol was transferable and basically had good between-lab reproducibility and predictivity, but there were some false negative data. To improve performance, protocol and prediction model were modified. Using the modified prediction model in the first and second RT, accuracy was improved. However, about 15% of the outcomes were not correctly identified, which exposes some of the limitations of the assay. For the chemicals evaluated, the limitation may due to chemical being a weak allergen or having low solubility (ex. alpha-hexylcinnamaldehyde). The third RT evaluated the modified prediction model and satisfactory results were obtained. From the RT data, the feasibility of utilizing cell lines as surrogate DC in development of in vitro skin sensitization methods shows promise. The data also support initiating formal pre-validation of the h-CLAT in order to fully understand the capabilities and limitations of the assay. Copyright 2010 Elsevier Ltd. All rights reserved.

Comparative aspects of rodent and nonrodent animal models for mechanistic and translational diabetes research.

PubMed

Renner, Simone; Dobenecker, Britta; Blutke, Andreas; Zöls, Susanne; Wanke, Rüdiger; Ritzmann, Mathias; Wolf, Eckhard

2016-07-01

The prevalence of diabetes mellitus, which currently affects 387 million people worldwide, is permanently rising in both adults and adolescents. Despite numerous treatment options, diabetes mellitus is a progressive disease with severe comorbidities, such as nephropathy, neuropathy, and retinopathy, as well as cardiovascular disease. Therefore, animal models predictive of the efficacy and safety of novel compounds in humans are of great value to address the unmet need for improved therapeutics. Although rodent models provide important mechanistic insights, their predictive value for therapeutic outcomes in humans is limited. In recent years, the pig has gained importance for biomedical research because of its close similarity to human anatomy, physiology, size, and, in contrast to non-human primates, better ethical acceptance. In this review, anatomic, biochemical, physiological, and morphologic aspects relevant to diabetes research will be compared between different animal species, that is, mouse, rat, rabbit, pig, and non-human primates. The value of the pig as a model organism for diabetes research will be highlighted, and (dis)advantages of the currently available approaches for the generation of pig models exhibiting characteristics of metabolic syndrome or type 2 diabetes mellitus will be discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Individual differences in the behavioral effects of nicotine: A review of the preclinical animal literature

PubMed Central

Falco, Adriana M.; Bevins, Rick A.

2015-01-01

Not everyone who tries tobacco or other nicotine-containing products becomes a long-term user. Certain traits or factors that are differentially present in these individuals must be able to help health care providers and researchers determine who is more likely to become chronic users of nicotine-containing products. Some of these factors, particularly sensation-seeking/novelty, impulsivity, and anxiety, lend themselves to the creation of animal models of reactivity to nicotine. These models of reactivity to nicotine can improve the translational aspects of preclinical animal research on nicotine-induced behaviors and treatments in order to help reduce negative outcomes in human populations. The goal of this review is to evaluate the current status of animal models of individual differences that serve to predict the later behavioral effects of nicotine. The limited utility and inconsistency of existing novelty models is considered, as well as the promise of impulsivity and anxiety models in preclinical animal populations. Finally, other models that could be employed to extend the benefit of the current research are examined. PMID:26410616

Colour spaces in ecology and evolutionary biology.

PubMed

Renoult, Julien P; Kelber, Almut; Schaefer, H Martin

2017-02-01

The recognition that animals sense the world in a different way than we do has unlocked important lines of research in ecology and evolutionary biology. In practice, the subjective study of natural stimuli has been permitted by perceptual spaces, which are graphical models of how stimuli are perceived by a given animal. Because colour vision is arguably the best-known sensory modality in most animals, a diversity of colour spaces are now available to visual ecologists, ranging from generalist and basic models allowing rough but robust predictions on colour perception, to species-specific, more complex models giving accurate but context-dependent predictions. Selecting among these models is most often influenced by historical contingencies that have associated models to specific questions and organisms; however, these associations are not always optimal. The aim of this review is to provide visual ecologists with a critical perspective on how models of colour space are built, how well they perform and where their main limitations are with regard to their most frequent uses in ecology and evolutionary biology. We propose a classification of models based on their complexity, defined as whether and how they model the mechanisms of chromatic adaptation and receptor opponency, the nonlinear association between the stimulus and its perception, and whether or not models have been fitted to experimental data. Then, we review the effect of modelling these mechanisms on predictions of colour detection and discrimination, colour conspicuousness, colour diversity and diversification, and for comparing the perception of colour traits between distinct perceivers. While a few rules emerge (e.g. opponent log-linear models should be preferred when analysing very distinct colours), in general model parameters still have poorly known effects. Colour spaces have nonetheless permitted significant advances in ecology and evolutionary biology, and more progress is expected if ecologists compare results between models and perform behavioural experiments more routinely. Such an approach would further contribute to a better understanding of colour vision and its links to the behavioural ecology of animals. While visual ecology is essentially a transfer of knowledge from visual sciences to evolutionary ecology, we hope that the discipline will benefit both fields more evenly in the future. © 2015 Cambridge Philosophical Society.

In vitro blood-brain barrier permeability predictions for GABAA receptor modulating piperine analogs.

PubMed

Eigenmann, Daniela Elisabeth; Dürig, Carmen; Jähne, Evelyn Andrea; Smieško, Martin; Culot, Maxime; Gosselet, Fabien; Cecchelli, Romeo; Helms, Hans Christian Cederberg; Brodin, Birger; Wimmer, Laurin; Mihovilovic, Marko D; Hamburger, Matthias; Oufir, Mouhssin

2016-06-01

The alkaloid piperine from black pepper (Piper nigrum L.) and several synthetic piperine analogs were recently identified as positive allosteric modulators of γ-aminobutyric acid type A (GABAA) receptors. In order to reach their target sites of action, these compounds need to enter the brain by crossing the blood-brain barrier (BBB). We here evaluated piperine and five selected analogs (SCT-66, SCT-64, SCT-29, LAU397, and LAU399) regarding their BBB permeability. Data were obtained in three in vitro BBB models, namely a recently established human model with immortalized hBMEC cells, a human brain-like endothelial cells (BLEC) model, and a primary animal (bovine endothelial/rat astrocytes co-culture) model. For each compound, quantitative UHPLC-MS/MS methods in the range of 5.00-500ng/mL in the corresponding matrix were developed, and permeability coefficients in the three BBB models were determined. In vitro predictions from the two human BBB models were in good agreement, while permeability data from the animal model differed to some extent, possibly due to protein binding of the screened compounds. In all three BBB models, piperine and SCT-64 displayed the highest BBB permeation potential. This was corroborated by data from in silico prediction. For the other piperine analogs (SCT-66, SCT-29, LAU397, and LAU399), BBB permeability was low to moderate in the two human BBB models, and moderate to high in the animal BBB model. Efflux ratios (ER) calculated from bidirectional permeability experiments indicated that the compounds were likely not substrates of active efflux transporters. Copyright © 2016 Elsevier B.V. All rights reserved.

Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre

An integrated physiology model to study regional lung damage effects and the physiologic response

PubMed Central

2014-01-01

Background This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. Methods A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. Results The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. Conclusions We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations. PMID:25044032

Compartmental analysis of the disposition of benzo[a]pyrene in rats.

PubMed

Bevan, D R; Weyand, E H

1988-11-01

We have previously reported the disposition of benzo[a]pyrene (B[a]P) and its metabolites in male Sprague-Dawley rats following intratracheal instillation of [3H]B[a]P [Weyand, E.H. and Bevan, D.R. (1986) Cancer Res., 46, 5655-5661]. In some experiments, cannulas were implanted in the bile duct of the animals prior to administration of [3H]B[a]P [Weyand, E.H. and Bevan, D.R. (1987) Drug Metab. Disposition, 15, 442-448]. Based on these data, we have developed a compartmental model of the distribution of radioactivity to provide a quantitative description of the fate of B[a]P and its metabolites in rats. Modeling of the distribution of radioactivity was performed using the Simulation, Analysis and Modeling (SAAM) and conversational SAAM (CONSAM) computer programs. Compartments in the model included organs into which the largest amounts of radioactivity were distributed as well as pathways for excretion of radioactivity from the animals. Data from animals with and without cannulas implanted in the bile duct were considered simultaneously during modeling. Radioactivity was so rapidly absorbed from the lungs that an absorption phase into blood was not apparent at the earliest sampling times. Using the model of extrapolate to shorter times, it was predicted that the maximum amount of radioactivity was present in blood within 2 min after administration. In addition, considerable recycling of radioactivity back to lungs from blood was predicted by the model. Transfer of radioactivity from blood to liver and carcass (skin, muscle, bones, fat and associated blood) also was extensive. Carcass was modeled as the sum of two compartments to obtain agreement between the model and experimental data. The model accounted for enterohepatic circulation of B[a]P metabolites; data also required that intestinal secretion be included in the model. Quantitative data obtained from compartmental analysis included rate constants for transfer of radioactivity among compartments as well as statistical parameters indicating the identifiability of the rate constants. That the model is consistent with two sets of data, those obtained in animals with and without a biliary cannula, indicates its potential utility in predicting the disposition of B[a]P and its metabolites in vivo.

[Creation of a line of "depressed" mice from a selection of breeders exhibiting a behavioral helplessness].

PubMed

Vaugeois, J M; Costentin, J

1998-01-01

Antidepressants are used since 40 years. All presently used antidepressants have a slow onset of action and do not improve all patients; thus, there is an absolute need for new antidepressants. A variety of animal models, often based upon the monoaminergic theory of depressive disorders, has been used to screen the current antidepressants. In fact, the main focus of most of these animal models has been to predict the antidepressant potential i.e. to establish predictive validity. However, the evaluation of such animal models should also consider face validity, i.e. how closely the model resembles the human condition, and this should help to identify innovating medicines. Antidepressants, when taken by a healthy person, induce nothing more than side effects, unrelated to an action on mood, whereas they alleviate depressive symptomatology in depressed patients. We have speculated that genetically selected animal models would be closer to the human clinical situation than models based on standard laboratory strains. We have depicted here that marked differences exist between strains of mice in the amount of immobility i.e. "spontaneous helplessness" observed in the tail suspension test, a method used to screen potential antidepressants. We have studied the behavioural characteristics of mice selectively bred for spontaneous high or low immobility scores in the tail suspension test. Hopefully, these selectively bred lines will provide a novel approach to investigate behavioural, neurochemical and neuroendocrine correlates of antidepressant action.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

The multifactorial role of the 3Rs in shifting the harm-benefit analysis in animal models of disease.

PubMed

Graham, Melanie L; Prescott, Mark J

2015-07-15

Ethics on animal use in science in Western society is based on utilitarianism, weighing the harms and benefits to the animals involved against those of the intended human beneficiaries. The 3Rs concept (Replacement, Reduction, Refinement) is both a robust framework for minimizing animal use and suffering (addressing the harms to animals) and a means of supporting high quality science and translation (addressing the benefits). The ambiguity of basic research performed early in the research continuum can sometimes make harm-benefit analysis more difficult since anticipated benefit is often an incremental contribution to a field of knowledge. On the other hand, benefit is much more evident in translational research aimed at developing treatments for direct application in humans or animals suffering from disease. Though benefit may be easier to define, it should certainly not be considered automatic. Issues related to model validity seriously compromise experiments and have been implicated as a major impediment in translation, especially in complex disease models where harms to animals can be intensified. Increased investment and activity in the 3Rs is delivering new research models, tools and approaches with reduced reliance on animal use, improved animal welfare, and improved scientific and predictive value. Copyright © 2015 Elsevier B.V. All rights reserved.

The multifactorial role of the 3Rs in shifting the harm-benefit analysis in animal models of disease

PubMed Central

Graham, Melanie L.; Prescott, Mark J.

2015-01-01

Ethics on animal use in science in Western society is based on utilitarianism, weighing the harms and benefits to the animals involved against those of the intended human beneficiaries. The 3Rs concept (Replacement, Reduction, Refinement) is both a robust framework for minimizing animal use and suffering (addressing the harms to animals) and a means of supporting high quality science and translation (addressing the benefits). The ambiguity of basic research performed early in the research continuum can sometimes make harm-benefit analysis more difficult since anticipated benefit is often an incremental contribution to a field of knowledge. On the other hand, benefit is much more evident in translational research aimed at developing treatments for direct application in humans or animals suffering from disease. Though benefit may be easier to define, it should certainly not be considered automatic. Issues related to model validity seriously compromise experiments and have been implicated as a major impediment in translation, especially in complex disease models where harms to animals can be intensified. Increased investment and activity in the 3Rs is delivering new research models, tools and approaches with reduced reliance on animal use, improved animal welfare, and improved scientific and predictive value. PMID:25823812

Virtual Liver: integrating in vitro and in vivo data to predict chemical-induced toxicity

EPA Science Inventory

It is difficult to assess the health impact of long-term exposure to low levels of contaminants from animal studies. Current methods for testing the toxicity of a single chemical can cost millions of dollars, take up to two years and sacrifice thousands of animals. In vitro model...

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

PubMed Central

Huang, Ruili; Xia, Menghang; Sakamuru, Srilatha; Zhao, Jinghua; Shahane, Sampada A.; Attene-Ramos, Matias; Zhao, Tongan; Austin, Christopher P.; Simeonov, Anton

2016-01-01

Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing. PMID:26811972

Maximum-entropy description of animal movement.

PubMed

Fleming, Chris H; Subaşı, Yiğit; Calabrese, Justin M

2015-03-01

We introduce a class of maximum-entropy states that naturally includes within it all of the major continuous-time stochastic processes that have been applied to animal movement, including Brownian motion, Ornstein-Uhlenbeck motion, integrated Ornstein-Uhlenbeck motion, a recently discovered hybrid of the previous models, and a new model that describes central-place foraging. We are also able to predict a further hierarchy of new models that will emerge as data quality improves to better resolve the underlying continuity of animal movement. Finally, we also show that Langevin equations must obey a fluctuation-dissipation theorem to generate processes that fall from this class of maximum-entropy distributions when the constraints are purely kinematic.

A Way Forward Commentary

EPA Science Inventory

Models for predicting adverse outcomes can help reduce and focus animal testing with new and existing chemicals. This short "thought starter" describes how quantitative-structure activity relationship and systems biology models can be used to help define toxicity pathways and li...

Estimating animal populations and body sizes from burrows: Marine ecologists have their heads buried in the sand

NASA Astrophysics Data System (ADS)

Schlacher, Thomas A.; Lucrezi, Serena; Peterson, Charles H.; Connolly, Rod M.; Olds, Andrew D.; Althaus, Franziska; Hyndes, Glenn A.; Maslo, Brooke; Gilby, Ben L.; Leon, Javier X.; Weston, Michael A.; Lastra, Mariano; Williams, Alan; Schoeman, David S.

2016-06-01

Most ecological studies require knowledge of animal abundance, but it can be challenging and destructive of habitat to obtain accurate density estimates for cryptic species, such as crustaceans that tunnel deeply into the seafloor, beaches, or mudflats. Such fossorial species are, however, widely used in environmental impact assessments, requiring sampling techniques that are reliable, efficient, and environmentally benign for these species and environments. Counting and measuring the entrances of burrows made by cryptic species is commonly employed to index population and body sizes of individuals. The fundamental premise is that burrow metrics consistently predict density and size. Here we review the evidence for this premise. We also review criteria for selecting among sampling methods: burrow counts, visual censuses, and physical collections. A simple 1:1 correspondence between the number of holes and population size cannot be assumed. Occupancy rates, indexed by the slope of regression models, vary widely between species and among sites for the same species. Thus, 'average' or 'typical' occupancy rates should not be extrapolated from site- or species specific field validations and then be used as conversion factors in other situations. Predictions of organism density made from burrow counts often have large uncertainty, being double to half of the predicted mean value. Whether such prediction uncertainty is 'acceptable' depends on investigators' judgements regarding the desired detectable effect sizes. Regression models predicting body size from burrow entrance dimensions are more precise, but parameter estimates of most models are specific to species and subject to site-to-site variation within species. These results emphasise the need to undertake thorough field validations of indirect census techniques that include tests of how sensitive predictive models are to changes in habitat conditions or human impacts. In addition, new technologies (e.g. drones, thermal-, acoustic- or chemical sensors) should be used to enhance visual census techniques of burrows and surface-active animals.

Evidence for Emergency Vaccination Having Played a Crucial Role to Control the 1965/66 Foot-and-Mouth Disease Outbreak in Switzerland

PubMed Central

Zingg, Dana; Häsler, Stephan; Schuepbach-Regula, Gertraud; Schwermer, Heinzpeter; Dürr, Salome

2015-01-01

Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution. PMID:26697436

Improving removal-based estimates of abundance by sampling a population of spatially distinct subpopulations

USGS Publications Warehouse

Dorazio, R.M.; Jelks, H.L.; Jordan, F.

2005-01-01

 A statistical modeling framework is described for estimating the abundances of spatially distinct subpopulations of animals surveyed using removal sampling. To illustrate this framework, hierarchical models are developed using the Poisson and negative-binomial distributions to model variation in abundance among subpopulations and using the beta distribution to model variation in capture probabilities. These models are fitted to the removal counts observed in a survey of a federally endangered fish species. The resulting estimates of abundance have similar or better precision than those computed using the conventional approach of analyzing the removal counts of each subpopulation separately. Extension of the hierarchical models to include spatial covariates of abundance is straightforward and may be used to identify important features of an animal's habitat or to predict the abundance of animals at unsampled locations.

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

PubMed

Masuda, Y; Misztal, I; Tsuruta, S; Legarra, A; Aguilar, I; Lourenco, D A L; Fragomeni, B O; Lawlor, T J

2016-03-01

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix GAPY(-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY(-1) for 569,404 genotyped animals with 10,000 core animals took 1.3h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Practical implications for genetic modeling in the genomics era

USDA-ARS?s Scientific Manuscript database

Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

EPA Science Inventory

Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

A human life-stage physiologically based pharmacokinetic and pharmacodynamic model for chlorpyrifos: development and validation.

PubMed

Smith, Jordan Ned; Hinderliter, Paul M; Timchalk, Charles; Bartels, Michael J; Poet, Torka S

2014-08-01

Sensitivity to some chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to predict disposition of chlorpyrifos and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, previously measured age-dependent metabolism of chlorpyrifos and chlorpyrifos-oxon were integrated into age-related descriptions of human anatomy and physiology. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ⩾0.6mg/kg of chlorpyrifos (100- to 1000-fold higher than environmental exposure levels), 6months old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent doses. At lower doses more relevant to environmental exposures, simulations predict that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict chlorpyrifos disposition and biological response over various postnatal life stages. Copyright © 2013 Elsevier Inc. All rights reserved.

Non-animal approaches for toxicokinetics in risk evaluations of food chemicals.

PubMed

Punt, Ans; Peijnenburg, Ad A C M; Hoogenboom, Ron L A P; Bouwmeester, Hans

2017-01-01

The objective of the present work was to review the availability and predictive value of non-animal toxicokinetic approaches and to evaluate their current use in European risk evaluations of food contaminants, additives and food contact materials, as well as pesticides and medicines. Results revealed little use of quantitative animal or human kinetic data in risk evaluations of food chemicals, compared with pesticides and medicines. Risk evaluations of medicines provided sufficient in vivo kinetic data from different species to evaluate the predictive value of animal kinetic data for humans. These data showed a relatively poor correlation between the in vivo bioavailability in rats and dogs versus that in humans. In contrast, in vitro (human) kinetic data have been demonstrated to provide adequate predictions of the fate of compounds in humans, using appropriate in vitro-in vivo scalers and by integration of in vitro kinetic data with in silico kinetic modelling. Even though in vitro kinetic data were found to be occasionally included within risk evaluations of food chemicals, particularly results from Caco-2 absorption experiments and in vitro data on gut-microbial conversions, only minor use of in vitro methods for metabolism and quantitative in vitro-in vivo extrapolation methods was identified. Yet, such quantitative predictions are essential in the development of alternatives to animal testing as well as to increase human relevance of toxicological risk evaluations. Future research should aim at further improving and validating quantitative alternative methods for kinetics, thereby increasing regulatory acceptance of non-animal kinetic data.

Blunted Hypothalamo-pituitary Adrenal Axis Response to Predator Odor Predicts High Stress Reactivity

PubMed Central

Whitaker, Annie M.; Gilpin, Nicholas W.

2015-01-01

Individuals with trauma- and stress-related disorders exhibit increases in avoidance of trauma-related stimuli, heightened anxiety and altered neuroendocrine stress responses. Our laboratory uses a rodent model of stress that mimics the avoidance symptom cluster associated with stress-related disorders. Animals are classified as ‘Avoiders’ or Non-Avoiders' post-stress based on avoidance of predator-odor paired context. Utilizing this model, we are able to examine subpopulation differences in stress reactivity. Here, we used this predator odor model of stress to examine differences in anxiety-like behavior and hypothalamo-pituitary adrenal (HPA) axis function in animals that avoid a predator-paired context relative to those that do not. Rats were exposed to predator odor stress paired with a context and tested for avoidance (24 hours and 11 days), anxiety-like behavior (48 hours and 5 days) and HPA activation following stress. Control animals were exposed to room air. Predator odor stress produced avoidance in approximately 65% of the animals at 24 hours that persisted 11 days post-stress. Both Avoiders and Non-Avoiders exhibited heightened anxiety-like behavior at 48 hours and 5 days post-stress when compared to unstressed Controls. Non-Avoiders exhibited significant increases in circulating adrenocorticotropin hormone (ACTH) and corticosterone (CORT) concentrations immediately following predator odor stress compared to Controls and this response was significantly attenuated in Avoiders. There was an inverse correlation between circulating ACTH/CORT concentrations and avoidance, indicating that lower levels of ACTH/CORT predicted higher levels of avoidance. These results suggest that stress effects on HPA stress axis activation predict long-term avoidance of stress-paired stimuli, and builds on previous data showing the utility of this model for exploring the neurobiological mechanisms of trauma- and stress-related disorders. PMID:25824191

Criteria of validity for animal models of psychiatric disorders: focus on anxiety disorders and depression

PubMed Central

2011-01-01

Animal models of psychiatric disorders are usually discussed with regard to three criteria first elaborated by Willner; face, predictive and construct validity. Here, we draw the history of these concepts and then try to redraw and refine these criteria, using the framework of the diathesis model of depression that has been proposed by several authors. We thus propose a set of five major criteria (with sub-categories for some of them); homological validity (including species validity and strain validity), pathogenic validity (including ontopathogenic validity and triggering validity), mechanistic validity, face validity (including ethological and biomarker validity) and predictive validity (including induction and remission validity). Homological validity requires that an adequate species and strain be chosen: considering species validity, primates will be considered to have a higher score than drosophila, and considering strains, a high stress reactivity in a strain scores higher than a low stress reactivity in another strain. Pathological validity corresponds to the fact that, in order to shape pathological characteristics, the organism has been manipulated both during the developmental period (for example, maternal separation: ontopathogenic validity) and during adulthood (for example, stress: triggering validity). Mechanistic validity corresponds to the fact that the cognitive (for example, cognitive bias) or biological mechanisms (such as dysfunction of the hormonal stress axis regulation) underlying the disorder are identical in both humans and animals. Face validity corresponds to the observable behavioral (ethological validity) or biological (biomarker validity) outcomes: for example anhedonic behavior (ethological validity) or elevated corticosterone (biomarker validity). Finally, predictive validity corresponds to the identity of the relationship between the triggering factor and the outcome (induction validity) and between the effects of the treatments on the two organisms (remission validity). The relevance of this framework is then discussed regarding various animal models of depression. PMID:22738250

Blunted hypothalamo-pituitary adrenal axis response to predator odor predicts high stress reactivity.

PubMed

Whitaker, Annie M; Gilpin, Nicholas W

2015-08-01

Individuals with trauma- and stress-related disorders exhibit increases in avoidance of trauma-related stimuli, heightened anxiety and altered neuroendocrine stress responses. Our laboratory uses a rodent model of stress that mimics the avoidance symptom cluster associated with stress-related disorders. Animals are classified as 'Avoiders' or 'Non-Avoiders' post-stress based on avoidance of predator-odor paired context. Utilizing this model, we are able to examine subpopulation differences in stress reactivity. Here, we used this predator odor model of stress to examine differences in anxiety-like behavior and hypothalamo-pituitary adrenal (HPA) axis function in animals that avoid a predator-paired context relative to those that do not. Rats were exposed to predator odor stress paired with a context and tested for avoidance (24h and 11days), anxiety-like behavior (48h and 5days) and HPA activation following stress. Control animals were exposed to room air. Predator odor stress produced avoidance in approximately 65% of the animals at 24h that persisted 11days post-stress. Both Avoiders and Non-Avoiders exhibited a heightened anxiety-like behavior at 48h and 5days post-stress when compared to unstressed Controls. Non-Avoiders exhibited significant increases in circulating adrenocorticotropin hormone (ACTH) and corticosterone (CORT) concentrations immediately following predator odor stress compared to Controls and this response was significantly attenuated in Avoiders. There was an inverse correlation between circulating ACTH/CORT concentrations and avoidance, indicating that lower levels of ACTH/CORT predicted higher levels of avoidance. These results suggest that stress effects on HPA stress axis activation predict long-term avoidance of stress-paired stimuli, and build on previous data showing the utility of this model for exploring the neurobiological mechanisms of trauma- and stress-related disorders. Copyright © 2015. Published by Elsevier Inc.

True Numerical Cognition in the Wild.

PubMed

Piantadosi, Steven T; Cantlon, Jessica F

2017-04-01

Cognitive and neural research over the past few decades has produced sophisticated models of the representations and algorithms underlying numerical reasoning in humans and other animals. These models make precise predictions for how humans and other animals should behave when faced with quantitative decisions, yet primarily have been tested only in laboratory tasks. We used data from wild baboons' troop movements recently reported by Strandburg-Peshkin, Farine, Couzin, and Crofoot (2015) to compare a variety of models of quantitative decision making. We found that the decisions made by these naturally behaving wild animals rely specifically on numerical representations that have key homologies with the psychophysics of human number representations. These findings provide important new data on the types of problems human numerical cognition was designed to solve and constitute the first robust evidence of true numerical reasoning in wild animals.

Multivariate Models for Prediction of Human Skin Sensitization ...

EPA Pesticide Factsheets

One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine

A Novel Animal Model for Panic Disorder: Attempted Reproduction of the Fear of Fear

DTIC Science & Technology

1999-11-04

and haloperidol . Buspirone, ipsapirone, flesi noxin, and 8- O H-DPAT (aI1 5HT IA agoni sts) strongly reduced USV in treated animals. T he 5HT 1A...Robinson & Shrol, 1989). Alprazolam (an effective anti-panic agent) and haloperidol (3 dopamine antagonist), produced similar profiles. Both drugs...identical to a drug serving as a negative control ( haloperidol ) suggests this model has poor predictive validity. Furthermore, the benzodiazepine

Osteoarthritis: new insights in animal models.

PubMed

Longo, Umile Giuseppe; Loppini, Mattia; Fumo, Caterina; Rizzello, Giacomo; Khan, Wasim Sardar; Maffulli, Nicola; Denaro, Vincenzo

2012-01-01

Osteoarthritis (OA) is the most frequent and symptomatic health problem in the middle-aged and elderly population, with over one-half of all people over the age of 65 showing radiographic changes in painful knees. The aim of the present study was to perform an overview on the available animal models used in the research field on the OA. Discrepancies between the animal models and the human disease are present. As regards human 'idiopathic' OA, with late onset and slow progression, it is perhaps wise not to be overly enthusiastic about animal models that show severe chondrodysplasia and very early OA. Advantage by using genetically engineered mouse models, in comparison with other surgically induced models, is that molecular etiology is known. Find potential molecular markers for the onset of the disease and pay attention to the role of gender and environmental factors should be very helpful in the study of mice that acquire premature OA. Surgically induced destabilization of joint is the most widely used induction method. These models allow the temporal control of disease induction and follow predictable progression of the disease. In animals, ACL transection and meniscectomy show a speed of onset and severity of disease higher than in humans after same injury.

Effect of water activity and temperature on the growth of Eurotium species isolated from animal feeds.

PubMed

Greco, Mariana; Pardo, Alejandro; Pose, Graciela; Patriarca, Andrea

Xerophilic fungi represent a serious problem due to their ability to grow at low water activities causing the spoiling of low and intermediate moisture foods, stored goods and animal feeds, with the consequent economic losses. The combined effect of water activity and temperature of four Eurotium species isolated from animal feeds was investigated. Eurotium amstelodami, Eurotium chevalieri, Eurotium repens and Eurotium rubrum were grown at 5, 15, 25, 37 and 45°C on malt extract agar adjusted with glycerol in the range 0.710-0.993 of water activities. The cardinal model proposed by Rosso and Robinson (2001) was applied to fit growth data, with the variable water activity at fixed temperatures, obtaining three cardinal water activities (a wmin , a wmax , a wopt ) and the specific growth rate at the optimum a w (μ opt ). A probabilistic model was also applied to define the interface between growth and no-growth. The cardinal model provided an adequate estimation of the optimal a w to grow and the maximum growth rate. The probabilistic model showed a good performance to fit growth/no-growth cases in the predicted range. The results presented here could be applied to predict Eurotium species growth in animal feeds. Copyright © 2017 Asociación Española de Micología. Publicado por Elsevier España, S.L.U. All rights reserved.

A Probablistic Diagram to Guide Chemical Design with ...

EPA Pesticide Factsheets

Toxicity is a concern with many chemicals currently in commerce, and with new chemicals that are introduced each year. The standard approach to testing chemicals is to run studies in laboratory animals (e.g. rats, mice, dogs), but because of the expense of these studies and concerns for animal welfare, few chemicals besides pharmaceuticals and pesticides are fully tested. Over the last decade there have been significant developments in the field of computational toxicology which combines in vitro tests and computational models. The ultimate goal of this ?field is to test all chemicals in a rapid, cost effective manner with minimal use of animals. One of the simplest measures of toxicity is provided by high-throughput in vitro cytotoxicity assays, which measure the concentration of a chemical that kills particular types of cells. Chemicals that are cytotoxic at low concentrations tend to be more toxic to animals than chemicals that are less cytotoxic. We employed molecular characteristics derived from density functional theory (DFT) and predicted values of log(octanol-water partition coe?fficient) (logP)to construct a design variable space, and built a predictive model for cytotoxicity using a Naive Bayesian algorithm. External evaluation showed that the area under the curve (AUC) for the receiver operating characteristic (ROC) of the model to be 0.81. Using this model, we provide design rules to help synthetic chemists minimize the chance that a newly synthesize

Comprehensive summary--Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing.

PubMed

Mueller, Stefan O; Dekant, Wolfgang; Jennings, Paul; Testai, Emanuela; Bois, Frederic

2015-12-25

This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project "Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics" (Predict-IV; Grant 202222). The project's overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluating the Impact of Naltrexone on the Rat Gambling Task to Test Its Predictive Validity for Gambling Disorder.

PubMed

Di Ciano, Patricia; Le Foll, Bernard

2016-01-01

Gambling Disorder has serious consequences and no medications are currently approved for the treatment of this disorder. One factor that may make medication development difficult is the lack of animal models of gambling that would allow for the pre-clinical screening of efficacy. Despite this, there is evidence from clinical trials that opiate antagonists, in particular naltrexone, may be useful in treating gambling disorder. To-date, the effects of naltrexone on pre-clinical models of gambling have not been evaluated. The purpose of the present study was to evaluate the effects of naltrexone in an animal model of gambling, the rat gambling task (rGT), to determine whether this model has some predictive validity. The rGT is a model in which rats are given a choice of making either a response that produces a large reward or a small reward. The larger the reward, the greater the punishment, and thus this task requires that the animal inhibit the 'tempting' choice, as the smaller reward option produces overall the most number of rewards per session. People with gambling disorder chose the tempting option more, thus the rGT may provide a model of problem gambling. It was found that naltrexone improved performance on this task in a subset of animals that chose the 'tempting', disadvantageous choice, more at baseline. Thus, the results of this study suggest that the rGT should be further investigated as a pre-clinical model of gambling disorder and that further investigation into whether opioid antagonists are effective in treating Gambling Disorder may be warranted.

Diffusion model to describe osteogenesis within a porous titanium scaffold.

PubMed

Schmitt, M; Allena, R; Schouman, T; Frasca, S; Collombet, J M; Holy, X; Rouch, P

2016-01-01

In this study, we develop a two-dimensional finite element model, which is derived from an animal experiment and allows simulating osteogenesis within a porous titanium scaffold implanted in ewe's hemi-mandible during 12 weeks. The cell activity is described through diffusion equations and regulated by the stress state of the structure. We compare our model to (i) histological observations and (ii) experimental data obtained from a mechanical test done on sacrificed animal. We show that our mechano-biological approach provides consistent numerical results and constitutes a useful tool to predict osteogenesis pattern.

Predicting mosaics and wildlife diversity resulting from fire disturbance to a forest ecosystem

NASA Astrophysics Data System (ADS)

Potter, Meredith W.; Kessell, Stephen R.

1980-05-01

A model for predicting community mosaics and wildlife diversity resulting from fire disturbance to a forest ecosystem is presented. It applies an algorithm that delineates the size and shape of each patch from grid-based input data and calculates standard diversity measures for the entire mosaic of community patches and their included animal species. The user can print these diversity calculations, maps of the current community-type-age-class mosaic, and maps of habitat utilization by each animal species. Furthermore, the user can print estimates of changes in each resulting from natural disturbance. Although data and resolution level independent, the model is demonstrated and tested with data from the Lewis and Clark National Forest in Montana.

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

PubMed Central

2014-01-01

Background Although the X chromosome is the second largest bovine chromosome, markers on the X chromosome are not used for genomic prediction in some countries and populations. In this study, we presented a method for computing genomic relationships using X chromosome markers, investigated the accuracy of imputation from a low density (7K) to the 54K SNP (single nucleotide polymorphism) panel, and compared the accuracy of genomic prediction with and without using X chromosome markers. Methods The impact of considering X chromosome markers on prediction accuracy was assessed using data from Nordic Holstein bulls and different sets of SNPs: (a) the 54K SNPs for reference and test animals, (b) SNPs imputed from the 7K to the 54K SNP panel for test animals, (c) SNPs imputed from the 7K to the 54K panel for half of the reference animals, and (d) the 7K SNP panel for all animals. Beagle and Findhap were used for imputation. GBLUP (genomic best linear unbiased prediction) models with or without X chromosome markers and with or without a residual polygenic effect were used to predict genomic breeding values for 15 traits. Results Averaged over the two imputation datasets, correlation coefficients between imputed and true genotypes for autosomal markers, pseudo-autosomal markers, and X-specific markers were 0.971, 0.831 and 0.935 when using Findhap, and 0.983, 0.856 and 0.937 when using Beagle. Estimated reliabilities of genomic predictions based on the imputed datasets using Findhap or Beagle were very close to those using the real 54K data. Genomic prediction using all markers gave slightly higher reliabilities than predictions without X chromosome markers. Based on our data which included only bulls, using a G matrix that accounted for sex-linked relationships did not improve prediction, compared with a G matrix that did not account for sex-linked relationships. A model that included a polygenic effect did not recover the loss of prediction accuracy from exclusion of X chromosome markers. Conclusions The results from this study suggest that markers on the X chromosome contribute to accuracy of genomic predictions and should be used for routine genomic evaluation. PMID:25080199

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

PubMed

Weng, Ziqing; Wolc, Anna; Shen, Xia; Fernando, Rohan L; Dekkers, Jack C M; Arango, Jesus; Settar, Petek; Fulton, Janet E; O'Sullivan, Neil P; Garrick, Dorian J

2016-03-19

Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

Animal models of obsessive–compulsive disorder: utility and limitations

PubMed Central

Alonso, Pino; López-Solà, Clara; Real, Eva; Segalàs, Cinto; Menchón, José Manuel

2015-01-01

Obsessive–compulsive disorder (OCD) is a disabling and common neuropsychiatric condition of poorly known etiology. Many attempts have been made in the last few years to develop animal models of OCD with the aim of clarifying the genetic, neurochemical, and neuroanatomical basis of the disorder, as well as of developing novel pharmacological and neurosurgical treatments that may help to improve the prognosis of the illness. The latter goal is particularly important given that around 40% of patients with OCD do not respond to currently available therapies. This article summarizes strengths and limitations of the leading animal models of OCD including genetic, pharmacologically induced, behavioral manipulation-based, and neurodevelopmental models according to their face, construct, and predictive validity. On the basis of this evaluation, we discuss that currently labeled “animal models of OCD” should be regarded not as models of OCD but, rather, as animal models of different psychopathological processes, such as compulsivity, stereotypy, or perseverance, that are present not only in OCD but also in other psychiatric or neurological disorders. Animal models might constitute a challenging approach to study the neural and genetic mechanism of these phenomena from a trans-diagnostic perspective. Animal models are also of particular interest as tools for developing new therapeutic options for OCD, with the greatest convergence focusing on the glutamatergic system, the role of ovarian and related hormones, and the exploration of new potential targets for deep brain stimulation. Finally, future research on neurocognitive deficits associated with OCD through the use of analogous animal tasks could also provide a genuine opportunity to disentangle the complex etiology of the disorder. PMID:26346234

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

PubMed

Kleinstreuer, Nicole C; Hoffmann, Sebastian; Alépée, Nathalie; Allen, David; Ashikaga, Takao; Casey, Warren; Clouet, Elodie; Cluzel, Magalie; Desprez, Bertrand; Gellatly, Nichola; Göbel, Carsten; Kern, Petra S; Klaric, Martina; Kühnl, Jochen; Martinozzi-Teissier, Silvia; Mewes, Karsten; Miyazawa, Masaaki; Strickland, Judy; van Vliet, Erwin; Zang, Qingda; Petersohn, Dirk

2018-05-01

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.

Evaluation of the GARD assay in a blind Cosmetics Europe study.

PubMed

Johansson, Henrik; Gradin, Robin; Forreryd, Andy; Agemark, Maria; Zeller, Kathrin; Johansson, Angelica; Larne, Olivia; van Vliet, Erwin; Borrebaeck, Carl; Lindstedt, Malin

2017-01-01

Chemical hypersensitivity is an immunological response towards foreign substances, commonly referred to as sensitizers, which gives rise primarily to the clinical symptoms known as allergic contact dermatitis. For the purpose of mitigating risks associated with consumer products, chemicals are screened for sensitizing effects. Historically, such predictive screenings have been performed using animal models. However, due to industrial and regulatory demand, animal models for the purpose of sensitization assessment are being replaced by non-animal testing methods, a global trend that is spreading across industries and market segments. To meet this demand, the Genomic Allergen Rapid Detection (GARD) assay was developed. GARD is a novel, cell-based assay that utilizes the innate recognition of xenobiotic substances by dendritic cells, as measured by a multivariate readout of genomic biomarkers. Following cellular stimulation, chemicals are classified as sensitizers or non-sensitizers based on induced transcriptional profiles. Recently, a number of non-animal methods were comparatively evaluated by Cosmetics Europe, using a coherent and blinded test panel of reference chemicals with human and local lymph node assay data, comprising a wide range of sensitizers and non-sensitizers. The outcome of the GARD assay is presented in this paper. It was demonstrated that GARD is a highly functional assay with a predictive performance of 83% in this Cosmetics Europe dataset. The average accumulated predictive accuracy of GARD across independent datasets was 86% for skin sensitization hazard.

Companion animals: Translational scientist’s new best friends

PubMed Central

Kol, Amir; Arzi, Boaz; Athanasiou, Kyriacos A.; Farmer, Diana L.; Nolta, Jan A.; Rebhun, Robert B.; Chen, Xinbin; Griffiths, Leigh G.; Verstraete, Frank J. M.; Murphy, Christopher J.; Borjesson, Dori L.

2016-01-01

Knowledge and resources derived from veterinary medicine represent an underused resource that could serve as a bridge between data obtained from diseases models in laboratory animals and human clinical trials. Naturally occurring disease in companion animals that display the defining attributes of similar, if not identical, diseases in humans hold promise for providing predictive proof of concept in the evaluation of new therapeutics and devices. Here we outline comparative aspects of naturally occurring diseases in companion animals and discuss their current uses in translational medicine, benefits, and shortcomings. Last, we envision how these natural models of disease might ultimately decrease the failure rate in human clinical trials and accelerate the delivery of effective treatments to the human clinical market. PMID:26446953

Intra- and inter-laboratory reliability of a cryopreserved trout hepatocyte assay for the prediction of chemical bioaccumulation potential

EPA Science Inventory

Cryopreserved trout hepatocytes provide a convenient in vitro system for measuring the intrinsic clearance of xenobiotics. Measured clearance rates can then be extrapolated to the whole animal as a means of improving modeled bioaccumulation predictions. To date, however, the in...

Genetic interactions for heat stress and production level: predicting foreign from domestic data

USDA-ARS?s Scientific Manuscript database

Genetic by environmental interactions were estimated from U.S. national data by separately adding random regressions for heat stress (HS) and herd production level (HL) to the all-breed animal model to improve predictions of future records and rankings in other climate and production situations. Yie...

Use of Isolated Trout Hepatocytes to Predict Measured Hepatic Clearance and Whole-animal Bioconcentration Factors for Six Polyaromatic Hydrocarbons

EPA Science Inventory

Hepatic metabolism is an important determinant of chemical bioaccumulation in fish. Consequently, measured in vitro hepatic metabolism may improve model predictions of bioaccumulation. In this study, fresh and cryopreserved trout hepatocytes were used to measure in vitro intrin...

20170921 - An evaluation of selected (Q)SARs/expert systems for the Prediction of Skin Sensitization Potential (ASCCT)

EPA Science Inventory

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximization Test (GPMT). In recent years, EU regulations have provided a strong incentiv...

Large animal models for vaccine development and testing.

PubMed

Gerdts, Volker; Wilson, Heather L; Meurens, Francois; van Drunen Littel-van den Hurk, Sylvia; Wilson, Don; Walker, Stewart; Wheler, Colette; Townsend, Hugh; Potter, Andrew A

2015-01-01

The development of human vaccines continues to rely on the use of animals for research. Regulatory authorities require novel vaccine candidates to undergo preclinical assessment in animal models before being permitted to enter the clinical phase in human subjects. Substantial progress has been made in recent years in reducing and replacing the number of animals used for preclinical vaccine research through the use of bioinformatics and computational biology to design new vaccine candidates. However, the ultimate goal of a new vaccine is to instruct the immune system to elicit an effective immune response against the pathogen of interest, and no alternatives to live animal use currently exist for evaluation of this response. Studies identifying the mechanisms of immune protection; determining the optimal route and formulation of vaccines; establishing the duration and onset of immunity, as well as the safety and efficacy of new vaccines, must be performed in a living system. Importantly, no single animal model provides all the information required for advancing a new vaccine through the preclinical stage, and research over the last two decades has highlighted that large animals more accurately predict vaccine outcome in humans than do other models. Here we review the advantages and disadvantages of large animal models for human vaccine development and demonstrate that much of the success in bringing a new vaccine to market depends on choosing the most appropriate animal model for preclinical testing. © The Author 2015. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

PubMed

Heidaritabar, M; Wolc, A; Arango, J; Zeng, J; Settar, P; Fulton, J E; O'Sullivan, N P; Bastiaansen, J W M; Fernando, R L; Garrick, D J; Dekkers, J C M

2016-10-01

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population. © 2016 Blackwell Verlag GmbH.

Relating Neuronal to Behavioral Performance: Variability of Optomotor Responses in the Blowfly

PubMed Central

Rosner, Ronny; Warzecha, Anne-Kathrin

2011-01-01

Behavioral responses of an animal vary even when they are elicited by the same stimulus. This variability is due to stochastic processes within the nervous system and to the changing internal states of the animal. To what extent does the variability of neuronal responses account for the overall variability at the behavioral level? To address this question we evaluate the neuronal variability at the output stage of the blowfly's (Calliphora vicina) visual system by recording from motion-sensitive interneurons mediating head optomotor responses. By means of a simple modelling approach representing the sensory-motor transformation, we predict head movements on the basis of the recorded responses of motion-sensitive neurons and compare the variability of the predicted head movements with that of the observed ones. Large gain changes of optomotor head movements have previously been shown to go along with changes in the animals' activity state. Our modelling approach substantiates that these gain changes are imposed downstream of the motion-sensitive neurons of the visual system. Moreover, since predicted head movements are clearly more reliable than those actually observed, we conclude that substantial variability is introduced downstream of the visual system. PMID:22066014

Separability of drag and thrust in undulatory animals and machines

NASA Astrophysics Data System (ADS)

Bale, Rahul; Shirgaonkar, Anup A.; Neveln, Izaak D.; Bhalla, Amneet Pal Singh; Maciver, Malcolm A.; Patankar, Neelesh A.

2014-12-01

For nearly a century, researchers have tried to understand the swimming of aquatic animals in terms of a balance between the forward thrust from swimming movements and drag on the body. Prior approaches have failed to provide a separation of these two forces for undulatory swimmers such as lamprey and eels, where most parts of the body are simultaneously generating drag and thrust. We nonetheless show that this separation is possible, and delineate its fundamental basis in undulatory swimmers. Our approach unifies a vast diversity of undulatory aquatic animals (anguilliform, sub-carangiform, gymnotiform, bal-istiform, rajiform) and provides design principles for highly agile bioinspired underwater vehicles. This approach has practical utility within biology as well as engineering. It is a predictive tool for use in understanding the role of the mechanics of movement in the evolutionary emergence of morphological features relating to locomotion. For example, we demonstrate that the drag-thrust separation framework helps to predict the observed height of the ribbon fin of electric knifefish, a diverse group of neotropical fish which are an important model system in sensory neurobiology. We also show how drag-thrust separation leads to models that can predict the swimming velocity of an organism or a robotic vehicle.

Physiologically Based Pharmacokinetic Models: Integration of In Silico Approaches with Micro Cell Culture Analogues

PubMed Central

Chen, A.; Yarmush, M.L.; Maguire, T.

2014-01-01

There is a large emphasis within the pharmaceutical industry to provide tools that will allow early research and development groups to better predict dose ranges for and metabolic responses of candidate molecules in a high throughput manner, prior to entering clinical trials. These tools incorporate approaches ranging from PBPK, QSAR, and molecular dynamics simulations in the in silico realm, to micro cell culture analogue (CCAs)s in the in vitro realm. This paper will serve to review these areas of high throughput predictive research, and highlight hurdles and potential solutions. In particular we will focus on CCAs, as their incorporation with PBPK modeling has the potential to replace animal testing, with a more predictive assay that can combine multiple organ analogs on one microfluidic platform in physiologically correct volume ratios. While several advantages arise from the current embodiments of CCAS in a microfluidic format that can be exploited for realistic simulations of drug absorption, metabolism and action, we explore some of the concerns with these systems, and provide a potential path forward to realizing animal-free solutions. Furthermore we envision that, together with theoretical modeling, CCAs may produce reliable predictions of the efficacy of newly developed drugs. PMID:22571482

Communicating forest management science and practices through visualized and animated media approaches to community presentations: An exploration and assessment

Treesearch

Donald E. Zimmerman; Carol Akerelrea; Jane Kapler Smith; Garrett J. O' Keefe

2006-01-01

Natural-resource managers have used a variety of computer-mediated presentation methods to communicate management practices to diverse publics. We explored the effects of visualizing and animating predictions from mathematical models in computerized presentations explaining forest succession (forest growth and change through time), fire behavior, and management options...

Application of single-step genomic evaluation for crossbred performance in pig.

PubMed

Xiang, T; Nielsen, B; Su, G; Legarra, A; Christensen, O F

2016-03-01

Crossbreding is predominant and intensively used in commercial meat production systems, especially in poultry and swine. Genomic evaluation has been successfully applied for breeding within purebreds but also offers opportunities of selecting purebreds for crossbred performance by combining information from purebreds with information from crossbreds. However, it generally requires that all relevant animals are genotyped, which is costly and presently does not seem to be feasible in practice. Recently, a novel single-step BLUP method for genomic evaluation of both purebred and crossbred performance has been developed that can incorporate marker genotypes into a traditional animal model. This new method has not been validated in real data sets. In this study, we applied this single-step method to analyze data for the maternal trait of total number of piglets born in Danish Landrace, Yorkshire, and two-way crossbred pigs in different scenarios. The genetic correlation between purebred and crossbred performances was investigated first, and then the impact of (crossbred) genomic information on prediction reliability for crossbred performance was explored. The results confirm the existence of a moderate genetic correlation, and it was seen that the standard errors on the estimates were reduced when including genomic information. Models with marker information, especially crossbred genomic information, improved model-based reliabilities for crossbred performance of purebred boars and also improved the predictive ability for crossbred animals and, to some extent, reduced the bias of prediction. We conclude that the new single-step BLUP method is a good tool in the genetic evaluation for crossbred performance in purebred animals.

Simulations of lattice animals and trees

NASA Astrophysics Data System (ADS)

Hsu, Hsiao-Ping; Nadler, Walter; Grassberger, Peter

2005-01-01

The scaling behaviour of randomly branched polymers in a good solvent is studied in two to nine dimensions, using as microscopic models lattice animals and lattice trees on simple hypercubic lattices. As a stochastic sampling method we use a biased sequential sampling algorithm with re-sampling, similar to the pruned-enriched Rosenbluth method (PERM) used extensively for linear polymers. Essentially we start simulating percolation clusters (either site or bond), re-weigh them according to the animal (tree) ensemble, and prune or branch the further growth according to a heuristic fitness function. In contrast to previous applications of PERM, this fitness function is not the weight with which the actual configuration would contribute to the partition sum, but is closely related to it. We obtain high statistics of animals with up to several thousand sites in all dimension 2 <= d <= 9. In addition to the partition sum (number of different animals) we estimate gyration radii and numbers of perimeter sites. In all dimensions we verify the Parisi-Sourlas prediction, and we verify all exactly known critical exponents in dimensions 2, 3, 4 and >=8. In addition, we present the hitherto most precise estimates for growth constants in d >= 3. For clusters with one site attached to an attractive surface, we verify for d >= 3 the superuniversality of the cross-over exponent phgr at the adsorption transition predicted by Janssen and Lyssy, but not for d = 2. There, we find phgr = 0.480(4) instead of the conjectured phgr = 1/2. Finally, we discuss the collapse of animals and trees, arguing that our present version of the algorithm is also efficient for some of the models studied in this context, but showing that it is not very efficient for the 'classical' model for collapsing animals.

Non-animal photosafety assessment approaches for cosmetics based on the photochemical and photobiochemical properties.

PubMed

Onoue, Satomi; Suzuki, Gen; Kato, Masashi; Hirota, Morihiko; Nishida, Hayato; Kitagaki, Masato; Kouzuki, Hirokazu; Yamada, Shizuo

2013-12-01

The main purpose of the present study was to establish a non-animal photosafety assessment approach for cosmetics using in vitro photochemical and photobiochemical screening systems. Fifty-one cosmetics, pharmaceutics and other chemicals were selected as model chemicals on the basis of animal and/or clinical photosafety information. The model chemicals were assessed in terms of photochemical properties by UV/VIS spectral analysis, reactive oxygen species (ROS) assay and 3T3 neutral red uptake phototoxicity testing (3T3 NRU PT). Most phototoxins exhibited potent UV/VIS absorption with molar extinction coefficients of over 1000M(-1)cm(-1), although false-negative prediction occurred for 2 cosmetic phototoxins owing to weak UV/VIS absorption. Among all the cosmetic ingredients, ca. 42% of tested chemicals were non-testable in the ROS assay because of low water solubility; thereby, micellar ROS (mROS) assay using a solubilizing surfactant was employed for follow-up screening. Upon combination use of ROS and mROS assays, the individual specificity was 88.2%, and the positive and negative predictivities were estimated to be 94.4% and 100%, respectively. In the 3T3 NRU PT, 3 cosmetics and 4 drugs were incorrectly predicted not to be phototoxic, although some of them were typical photoallergens. Thus, these in vitro screening systems individually provide false predictions; however, a systematic tiered approach using these assays could provide reliable photosafety assessment without any false-negatives. The combined use of in vitro assays might enable simple and fast non-animal photosafety evaluation of cosmetic ingredients. Copyright © 2013 Elsevier Ltd. All rights reserved.

Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks.

PubMed

Ehret, A; Hochstuhl, D; Krattenmacher, N; Tetens, J; Klein, M S; Gronwald, W; Thaller, G

2015-01-01

Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

How animal models of leukaemias have already benefited patients.

PubMed

Ablain, Julien; Nasr, Rihab; Zhu, Jun; Bazarbachi, Ali; Lallemand-Breittenbach, Valérie; de Thé, Hugues

2013-04-01

The relative genetic simplicity of leukaemias, the development of which likely relies on a limited number of initiating events has made them ideal for disease modelling, particularly in the mouse. Animal models provide incomparable insights into the mechanisms of leukaemia development and allow exploration of the molecular pillars of disease maintenance, an aspect often biased in cell lines or ex vivo systems. Several of these models, which faithfully recapitulate the characteristics of the human disease, have been used for pre-clinical purposes and have been instrumental in predicting therapy response in patients. We plea for a wider use of genetically defined animal models in the design of clinical trials, with a particular focus on reassessment of existing cancer or non-cancer drugs, alone or in combination. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world

PubMed Central

McDannald, Michael A.; Takahashi, Yuji K.; Lopatina, Nina; Pietras, Brad W.; Jones, Josh L.; Schoenbaum, Geoffrey

2012-01-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. PMID:22487030

Using Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals.

PubMed

Walley, Rosalind; Sherington, John; Rastrick, Joe; Detrait, Eric; Hanon, Etienne; Watt, Gillian

2016-05-01

Whilst innovative Bayesian approaches are increasingly used in clinical studies, in the preclinical area Bayesian methods appear to be rarely used in the reporting of pharmacology data. This is particularly surprising in the context of regularly repeated in vivo studies where there is a considerable amount of data from historical control groups, which has potential value. This paper describes our experience with introducing Bayesian analysis for such studies using a Bayesian meta-analytic predictive approach. This leads naturally either to an informative prior for a control group as part of a full Bayesian analysis of the next study or using a predictive distribution to replace a control group entirely. We use quality control charts to illustrate study-to-study variation to the scientists and describe informative priors in terms of their approximate effective numbers of animals. We describe two case studies of animal models: the lipopolysaccharide-induced cytokine release model used in inflammation and the novel object recognition model used to screen cognitive enhancers, both of which show the advantage of a Bayesian approach over the standard frequentist analysis. We conclude that using Bayesian methods in stable repeated in vivo studies can result in a more effective use of animals, either by reducing the total number of animals used or by increasing the precision of key treatment differences. This will lead to clearer results and supports the "3Rs initiative" to Refine, Reduce and Replace animals in research. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A general scaling law reveals why the largest animals are not the fastest.

PubMed

Hirt, Myriam R; Jetz, Walter; Rall, Björn C; Brose, Ulrich

2017-08-01

Speed is the fundamental constraint on animal movement, yet there is no general consensus on the determinants of maximum speed itself. Here, we provide a general scaling model of maximum speed with body mass, which holds across locomotion modes, ecosystem types and taxonomic groups. In contrast to traditional power-law scaling, we predict a hump-shaped relationship resulting from a finite acceleration time for animals, which explains why the largest animals are not the fastest. This model is strongly supported by extensive empirical data (474 species, with body masses ranging from 30 μg to 100 tonnes) from terrestrial as well as aquatic ecosystems. Our approach unravels a fundamental constraint on the upper limit of animal movement, thus enabling a better understanding of realized movement patterns in nature and their multifold ecological consequences.

The patch distributed producer-scrounger game.

PubMed

Ohtsuka, Yasunori; Toquenaga, Yukihiko

2009-09-21

Grouping in animals is ubiquitous and thought to provide group members antipredatory advantages and foraging efficiency. However, parasitic foraging strategy often emerges in a group. The optimal parasitic policy has given rise to the producer-scrounger (PS) game model, in which producers search for food patches, and scroungers parasitize the discovered patches. The N-persons PS game model constructed by Vickery et al. (1991. Producers, scroungers, and group foraging. American Naturalist 137, 847-863) predicts the evolutionarily stable strategy (ESS) of frequency of producers (q;) that depends on the advantage of producers and the number of foragers in a group. However, the model assumes that the number of discovered patches in one time unit never exceeds one. In reality, multiple patches could be found in one time unit. In the present study, we relax this assumption and assumed that the number of discovered patches depends on the producers' variable encounter rate with patches (lambda). We show that q; strongly depends on lambda within a feasible range, although it still depends on the advantage of producer and the number of foragers in a group. The basic idea of PS game is the same as the information sharing (parasitism), because scroungers are also thought to parasitize informations of locations of food patches. Horn (1968) indicated the role of information-parasitism in animal aggregation (Horn, H.S., 1968. The adaptive significance of colonial nesting in the Brewer's blackbird (euphagus cyanocephalus). Ecology 49, 682-646). Our modified PS game model shows the same prediction as the Horn's graphical animal aggregation model; the proportion of scroungers will increase or animals should adopt colonial foraging when resource is spatiotemporally clumped, but scroungers will decrease or animals should adopt territorial foraging if the resource is evenly distributed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Jordan N.; Hinderliter, Paul M.; Timchalk, Charles

Sensitivity to chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to computationally predict disposition of CPF and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, age-dependent body weight was calculated from a generalized Gompertz function, and compartments (liver, brain, fat, blood, diaphragm, rapid, and slow) were scaled based on body weight from polynomial functions on a fractional body weight basis. Bloodmore » flows among compartments were calculated as a constant flow per compartment volume. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Model simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ≥ 0.55 mg/kg of chlorpyrifos (significantly higher than environmental exposure levels), 6 mo old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent oral doses of chlorpyrifos. At lower doses that are more relevant to environmental exposures, the model predicts that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict CPF disposition and biological response over various postnatal life-stages.« less

Statistics of lattice animals

NASA Astrophysics Data System (ADS)

Hsu, Hsiao-Ping; Nadler, Walder; Grassberger, Peter

2005-07-01

The scaling behavior of randomly branched polymers in a good solvent is studied in two to nine dimensions, modeled by lattice animals on simple hypercubic lattices. For the simulations, we use a biased sequential sampling algorithm with re-sampling, similar to the pruned-enriched Rosenbluth method (PERM) used extensively for linear polymers. We obtain high statistics of animals with up to several thousand sites in all dimension 2⩽d⩽9. The partition sum (number of different animals) and gyration radii are estimated. In all dimensions we verify the Parisi-Sourlas prediction, and we verify all exactly known critical exponents in dimensions 2, 3, 4, and ⩾8. In addition, we present the hitherto most precise estimates for growth constants in d⩾3. For clusters with one site attached to an attractive surface, we verify the superuniversality of the cross-over exponent at the adsorption transition predicted by Janssen and Lyssy.

Electromagnetic Model Reliably Predicts Radar Scattering Characteristics of Airborne Organisms

NASA Astrophysics Data System (ADS)

Mirkovic, Djordje; Stepanian, Phillip M.; Kelly, Jeffrey F.; Chilson, Phillip B.

2016-10-01

The radar scattering characteristics of aerial animals are typically obtained from controlled laboratory measurements of a freshly harvested specimen. These measurements are tedious to perform, difficult to replicate, and typically yield only a small subset of the full azimuthal, elevational, and polarimetric radio scattering data. As an alternative, biological applications of radar often assume that the radar cross sections of flying animals are isotropic, since sophisticated computer models are required to estimate the 3D scattering properties of objects having complex shapes. Using the method of moments implemented in the WIPL-D software package, we show for the first time that such electromagnetic modeling techniques (typically applied to man-made objects) can accurately predict organismal radio scattering characteristics from an anatomical model: here the Brazilian free-tailed bat (Tadarida brasiliensis). The simulated scattering properties of the bat agree with controlled measurements and radar observations made during a field study of bats in flight. This numerical technique can produce the full angular set of quantitative polarimetric scattering characteristics, while eliminating many practical difficulties associated with physical measurements. Such a modeling framework can be applied for bird, bat, and insect species, and will help drive a shift in radar biology from a largely qualitative and phenomenological science toward quantitative estimation of animal densities and taxonomic identification.

Electromagnetic Model Reliably Predicts Radar Scattering Characteristics of Airborne Organisms

PubMed Central

Mirkovic, Djordje; Stepanian, Phillip M.; Kelly, Jeffrey F.; Chilson, Phillip B.

2016-01-01

The radar scattering characteristics of aerial animals are typically obtained from controlled laboratory measurements of a freshly harvested specimen. These measurements are tedious to perform, difficult to replicate, and typically yield only a small subset of the full azimuthal, elevational, and polarimetric radio scattering data. As an alternative, biological applications of radar often assume that the radar cross sections of flying animals are isotropic, since sophisticated computer models are required to estimate the 3D scattering properties of objects having complex shapes. Using the method of moments implemented in the WIPL-D software package, we show for the first time that such electromagnetic modeling techniques (typically applied to man-made objects) can accurately predict organismal radio scattering characteristics from an anatomical model: here the Brazilian free-tailed bat (Tadarida brasiliensis). The simulated scattering properties of the bat agree with controlled measurements and radar observations made during a field study of bats in flight. This numerical technique can produce the full angular set of quantitative polarimetric scattering characteristics, while eliminating many practical difficulties associated with physical measurements. Such a modeling framework can be applied for bird, bat, and insect species, and will help drive a shift in radar biology from a largely qualitative and phenomenological science toward quantitative estimation of animal densities and taxonomic identification. PMID:27762292

Electromagnetic Model Reliably Predicts Radar Scattering Characteristics of Airborne Organisms.

PubMed

Mirkovic, Djordje; Stepanian, Phillip M; Kelly, Jeffrey F; Chilson, Phillip B

2016-10-20

The radar scattering characteristics of aerial animals are typically obtained from controlled laboratory measurements of a freshly harvested specimen. These measurements are tedious to perform, difficult to replicate, and typically yield only a small subset of the full azimuthal, elevational, and polarimetric radio scattering data. As an alternative, biological applications of radar often assume that the radar cross sections of flying animals are isotropic, since sophisticated computer models are required to estimate the 3D scattering properties of objects having complex shapes. Using the method of moments implemented in the WIPL-D software package, we show for the first time that such electromagnetic modeling techniques (typically applied to man-made objects) can accurately predict organismal radio scattering characteristics from an anatomical model: here the Brazilian free-tailed bat (Tadarida brasiliensis). The simulated scattering properties of the bat agree with controlled measurements and radar observations made during a field study of bats in flight. This numerical technique can produce the full angular set of quantitative polarimetric scattering characteristics, while eliminating many practical difficulties associated with physical measurements. Such a modeling framework can be applied for bird, bat, and insect species, and will help drive a shift in radar biology from a largely qualitative and phenomenological science toward quantitative estimation of animal densities and taxonomic identification.

Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

USGS Publications Warehouse

van Toor, Mariëlle L.; Kranstauber, Bart; Newman, Scott H.; Prosser, Diann J.; Takekawa, John Y.; Technitis, Georgios; Weibel, Robert; Wikelski, Martin; Safi, Kamran

2018-01-01

Context High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance. Objectives We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range. Methods We used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature. Results Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species. Conclusions We show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds.

PubMed

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-12-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal-habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities.

Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis.

PubMed

Vanni, Michael J; McIntyre, Peter B

2016-12-01

The metabolic theory of ecology (MTE) and ecological stoichiometry (ES) are both prominent frameworks for understanding energy and nutrient budgets of organisms. We tested their separate and joint power to predict nitrogen (N) and phosphorus (P) excretion rates of ectothermic aquatic invertebrate and vertebrate animals (10,534 observations worldwide). MTE variables (body size, temperature) performed better than ES variables (trophic guild, vertebrate classification, body N:P) in predicting excretion rates, but the best models included variables from both frameworks. Size scaling coefficients were significantly lower than predicted by MTE (<0.75), were lower for P than N, and varied greatly among species. Contrary to expectations under ES, vertebrates excreted both N and P at higher rates than invertebrates despite having more nutrient-rich bodies, and primary consumers excreted as much nutrients as carnivores despite having nutrient-poor diets. Accounting for body N:P hardly improved upon predictions from treating vertebrate classification categorically. We conclude that basic data on body size, water temperature, trophic guild, and vertebrate classification are sufficient to make general estimates of nutrient excretion rates for any animal taxon or aquatic ecosystem. Nonetheless, dramatic interspecific variation in size-scaling coefficients and counter-intuitive patterns with respect to diet and body composition underscore the need for field data on consumption and egestion rates. Together, MTE and ES provide a powerful conceptual basis for interpreting and predicting nutrient recycling rates of aquatic animals worldwide. © 2016 by the Ecological Society of America.

Association with humans and seasonality interact to reverse predictions for animal space use.

PubMed

Laver, Peter N; Alexander, Kathleen A

2018-01-01

Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association with humans influenced space use patterns that diverged from historically-accepted predictions. There is growing need to explicitly incorporate human-animal interactions into ecological theory and research. Our results and methodology may contribute to understanding effects of anthropogenic landscape change on wildlife populations.

Practical implications for genetic modeling in the genomics era for the dairy industry

USDA-ARS?s Scientific Manuscript database

Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

Animals and the 3Rs in toxicology research and testing: The way forward.

PubMed

Stokes, W S

2015-12-01

Despite efforts to eliminate the use of animals in testing and the availability of many accepted alternative methods, animals are still widely used for toxicological research and testing. While research using in vitro and computational models has dramatically increased in recent years, such efforts have not yet measurably impacted animal use for regulatory testing and are not likely to do so for many years or even decades. Until regulatory authorities have accepted test methods that can totally replace animals and these are fully implemented, large numbers of animals will continue to be used and many will continue to experience significant pain and distress. In order to positively impact the welfare of these animals, accepted alternatives must be implemented, and efforts must be directed at eliminating pain and distress and reducing animal numbers. Animal pain and distress can be reduced by earlier predictive humane endpoints, pain-relieving medications, and supportive clinical care, while sequential testing and routine use of integrated testing and decision strategies can reduce animal numbers. Applying advances in science and technology to the development of scientifically sound alternative testing models and strategies can improve animal welfare and further reduce and replace animal use. © The Author(s) 2015.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate.

PubMed

White, R R; Roman-Garcia, Y; Firkins, J L; VandeHaar, M J; Armentano, L E; Weiss, W P; McGill, T; Garnett, R; Hanigan, M D

2017-05-01

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis

PubMed Central

Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.

2015-01-01

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

The potential for behavioral thermoregulation to buffer "cold-blooded" animals against climate warming.

PubMed

Kearney, Michael; Shine, Richard; Porter, Warren P

2009-03-10

Increasing concern about the impacts of global warming on biodiversity has stimulated extensive discussion, but methods to translate broad-scale shifts in climate into direct impacts on living animals remain simplistic. A key missing element from models of climatic change impacts on animals is the buffering influence of behavioral thermoregulation. Here, we show how behavioral and mass/energy balance models can be combined with spatial data on climate, topography, and vegetation to predict impacts of increased air temperature on thermoregulating ectotherms such as reptiles and insects (a large portion of global biodiversity). We show that for most "cold-blooded" terrestrial animals, the primary thermal challenge is not to attain high body temperatures (although this is important in temperate environments) but to stay cool (particularly in tropical and desert areas, where ectotherm biodiversity is greatest). The impact of climate warming on thermoregulating ectotherms will depend critically on how changes in vegetation cover alter the availability of shade as well as the animals' capacities to alter their seasonal timing of activity and reproduction. Warmer environments also may increase maintenance energy costs while simultaneously constraining activity time, putting pressure on mass and energy budgets. Energy- and mass-balance models provide a general method to integrate the complexity of these direct interactions between organisms and climate into spatial predictions of the impact of climate change on biodiversity. This methodology allows quantitative organism- and habitat-specific assessments of climate change impacts.

The potential for behavioral thermoregulation to buffer “cold-blooded” animals against climate warming

PubMed Central

Kearney, Michael; Shine, Richard; Porter, Warren P.

2009-01-01

Increasing concern about the impacts of global warming on biodiversity has stimulated extensive discussion, but methods to translate broad-scale shifts in climate into direct impacts on living animals remain simplistic. A key missing element from models of climatic change impacts on animals is the buffering influence of behavioral thermoregulation. Here, we show how behavioral and mass/energy balance models can be combined with spatial data on climate, topography, and vegetation to predict impacts of increased air temperature on thermoregulating ectotherms such as reptiles and insects (a large portion of global biodiversity). We show that for most “cold-blooded” terrestrial animals, the primary thermal challenge is not to attain high body temperatures (although this is important in temperate environments) but to stay cool (particularly in tropical and desert areas, where ectotherm biodiversity is greatest). The impact of climate warming on thermoregulating ectotherms will depend critically on how changes in vegetation cover alter the availability of shade as well as the animals' capacities to alter their seasonal timing of activity and reproduction. Warmer environments also may increase maintenance energy costs while simultaneously constraining activity time, putting pressure on mass and energy budgets. Energy- and mass-balance models provide a general method to integrate the complexity of these direct interactions between organisms and climate into spatial predictions of the impact of climate change on biodiversity. This methodology allows quantitative organism- and habitat-specific assessments of climate change impacts. PMID:19234117

Hazard assessment of nitrosamine and nitramine by-products of amine-based CCS: alternative approaches.

PubMed

Buist, H E; Devito, S; Goldbohm, R A; Stierum, R H; Venhorst, J; Kroese, E D

2015-04-01

Carbon capture and storage (CCS) technologies are considered vital and economic elements for achieving global CO2 reduction targets, and is currently introduced worldwide (for more information on CCS, consult for example the websites of the International Energy Agency (http://www.iea.org/topics/ccs/) and the Global CCS Institute (http://www.globalccsinstitute.com/)). One prominent CCS technology, the amine-based post-combustion process, may generate nitrosamines and their related nitramines as by-products, the former well known for their potential mutagenic and carcinogenic properties. In order to efficiently assess the carcinogenic potency of any of these by-products this paper reviews and discusses novel prediction approaches consuming less time, money and animals than the traditionally applied 2-year rodent assay. For this, available animal carcinogenicity studies with N-nitroso compounds and nitramines have been used to derive carcinogenic potency values, that were subsequently used to assess the predictive performance of alternative prediction approaches for these chemicals. Promising cancer prediction models are the QSARs developed by the Helguera group, in vitro transformation assays, and the in vivo initiation-promotion, and transgenic animal assays. All these models, however, have not been adequately explored for this purpose, as the number of N-nitroso compounds investigated is yet too limited, and therefore further testing with relevant N-nitroso compounds is needed. Copyright © 2015. Published by Elsevier Inc.

Use of population pharmacokinetic modeling and Monte Carlo simulation to capture individual animal variability in the prediction of flunixin withdrawal times in cattle.

PubMed

Wu, H; Baynes, R E; Leavens, T; Tell, L A; Riviere, J E

2013-06-01

The objective of this study was to develop a population pharmacokinetic (PK) model and predict tissue residues and the withdrawal interval (WDI) of flunixin in cattle. Data were pooled from published PK studies in which flunixin was administered through various dosage regimens to diverse populations of cattle. A set of liver data used to establish the regulatory label withdrawal time (WDT) also were used in this study. Compartmental models with first-order absorption and elimination were fitted to plasma and liver concentrations by a population PK modeling approach. Monte Carlo simulations were performed with the population mean and variabilities of PK parameters to predict liver concentrations of flunixin. The PK of flunixin was described best by a 3-compartment model with an extra liver compartment. The WDI estimated in this study with liver data only was the same as the label WDT. However, a longer WDI was estimated when both plasma and liver data were included in the population PK model. This study questions the use of small groups of healthy animals to determine WDTs for drugs intended for administration to large diverse populations. This may warrant a reevaluation of the current procedure for establishing WDT to prevent violative residues of flunixin. © 2012 Blackwell Publishing Ltd.

Building the bridge between animal movement and population dynamics.

PubMed

Morales, Juan M; Moorcroft, Paul R; Matthiopoulos, Jason; Frair, Jacqueline L; Kie, John G; Powell, Roger A; Merrill, Evelyn H; Haydon, Daniel T

2010-07-27

While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through 'spatially informed' movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission-fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra- and interspecific interaction.

Metabolic theory predicts animal self-thinning.

PubMed

Jonsson, Tomas

2017-05-01

The metabolic theory of ecology (MTE) predicts observed patterns in ecology based on metabolic rates of individuals. The theory is influential but also criticized for a lack of firm empirical evidence confirming MTE's quantitative predictions of processes, e.g. outcome of competition, at population or community level. Self-thinning is a well-known population level phenomenon among plants, but a much less studied phenomenon in animal populations and no consensus exists on what a universal thinning slope for animal populations might be, or if it exists. The goal of this study was to use animal self-thinning as a tool to test population-level predictions from MTE, by analysing (i) if self-thinning can be induced in populations of house crickets (Acheta domesticus) and (ii) if the resulting thinning trajectories can be predicted from metabolic theory, using estimates of the species-specific metabolic rate of A. domesticus. I performed a laboratory study where the growth of A. domesticus was followed, from hatching until emergence as adults, in 71 cohorts of five different starting densities. Ninety-six per cent of all cohorts in the three highest starting densities showed evidence of self-thinning, with estimated thinning slopes in general being remarkably close to that expected under metabolic constraints: A cross-sectional analysis of all data showing evidence of self-thinning produced an ordinary least square (OLS) slope of -1·11, exactly that predicted from specific metabolic allometry of A. domesticus. This result is furthermore supported by longitudinal analyses, allowing for independent responses within cohorts, producing a mean OLS slope across cohorts of -1·13 and a fixed effect linear mixed effects models slope of -1·09. Sensitivity analysis showed that these results are robust to how the criterion for on-going self-thinning was defined. Finally, also as predicted by metabolic theory, temperature had a negative effect on the thinning intercept, producing an estimate of the activation energy identical to that suggested by MTE. This study demonstrates a direct link between the metabolic rate of individuals and a population-level ecological process and as such provides strong support for research that aims to integrate body mass, via its effect on metabolism, consumption and competition, into models of populations and communities. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Perception of mind and dehumanization: Human, animal, or machine?

PubMed

Morera, María D; Quiles, María N; Correa, Ana D; Delgado, Naira; Leyens, Jacques-Philippe

2016-08-02

Dehumanization is reached through several approaches, including the attribute-based model of mind perception and the metaphor-based model of dehumanization. We performed two studies to find different (de)humanized images for three targets: Professional people, Evil people, and Lowest of the low. In Study 1, we examined dimensions of mind, expecting the last two categories to be dehumanized through denial of agency (Lowest of the low) or experience (Evil people), compared with humanized targets (Professional people). Study 2 aimed to distinguish these targets using metaphors. We predicted that Evil and Lowest of the low targets would suffer mechanistic and animalistic dehumanization, respectively; our predictions were confirmed, but the metaphor-based model nuanced these results: animalistic and mechanistic dehumanization were shown as overlapping rather than independent. Evil persons were perceived as "killing machines" and "predators." Finally, Lowest of the low were not animalized but considered human beings. We discuss possible interpretations. © 2016 International Union of Psychological Science.

Responses to Novelty and Vulnerability to Cocaine Addiction: Contribution of a Multi-Symptomatic Animal Model

PubMed Central

Belin, David; Deroche-Gamonet, Véronique

2012-01-01

Epidemiological studies have revealed striking associations between several distinct behavioral/personality traits and drug addiction, with a large emphasis on the sensation-seeking trait and the associated impulsive dimension of personality. However, in human studies, it is difficult to identify whether personality/behavioral traits actually contribute to increased vulnerability to drug addiction or reflect psychobiological adaptations to chronic drug exposure. Here we show how animal models, including the first multi-symptomatic model of addiction in the rat, have contributed to a better understanding of the relationships between different subdimensions of the sensation-seeking trait and different stages of the development of cocaine addiction, from vulnerability to initiation of cocaine self-administration to the transition to compulsive drug intake. We argue that sensation seeking predicts vulnerability to use cocaine, whereas novelty seeking, akin to high impulsivity, predicts instead vulnerability to shift from controlled to compulsive cocaine use, that is, addiction. PMID:23125204

In vitro cytotoxicity testing of 30 reference chemicals to predict acute human and animal toxicity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barile, F.A.; Arjun, S.; Borges, L.

1991-03-11

This study was conducted in cooperation with the Scandinavian Society of Cell Toxicology, as part of the Multicenter Evaluation for In Vitro Cytotoxicity (MEIC), and was designed to develop an in vitro model for predicting acute human and animal toxicity. The technique relies on the ability of cultured transformed rat lung epithelial cells (L2) to incorporate radiolabled amino acids into newly synthesized proteins in the absence or presence of increasing doses of the test chemical, during a 24-hr incubation. IC50 values were extrapolated from the dose-response curves after linear regression analysis. Human toxic blood concentrations estimated from rodent LD50 valuesmore » suggest that our experimental IC50's are in close correlation with the former. Validation of the data by the MEIC committee shows that our IC50 values predicted human lethal dosage as efficient as rodent LD50's. It is anticipated that this and related procedures may supplement or replace currently used animal protocols for predicting human toxicity.« less

Metabolic network modeling with model organisms.

PubMed

Yilmaz, L Safak; Walhout, Albertha Jm

2017-02-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. Published by Elsevier Ltd.

Metabolic network modeling with model organisms

PubMed Central

Yilmaz, L. Safak; Walhout, Albertha J.M.

2017-01-01

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions. PMID:28088694

Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

Multivariate models for prediction of human skin sensitization hazard.

PubMed

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2017-03-01

One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens™ assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Is the use of animals in biomedical research still necessary in 2002? Unfortunately, "yes".

PubMed

Festing, Michael F W

2004-06-01

The use of laboratory animals in the year 2002 is essential both to maintain human health and to develop new treatments for the many diseases that still plague humans. The suggestion by Greek and Greek in Sacred Cows and Golden Geese in 2000, that animal experiments are invalid because animals are different from humans, shows clearly that they do not understand the philosophical basis for the use of models in science and every day life. Models only need to resemble the thing being modelled (the target) in a few key respects. A map of Brooklyn Botanic Garden is a useful model, but it differs from the garden in many respects. There are many examples where studies on animals and in vitro alternatives result in accurate predictions of human responses even though the models differ from humans in other ways. In the drug development model, validation is done in clinical trials. Models are also used in the discovery of fundamental processes shared by some, or all, living organisms. The laws of genetics were first discovered by using garden peas, but they are equally applicable to humans. It is because of the ethical, rather than scientific, objections to the use of animals that all scientists are urged to find alternatives according to the principles of reduction, refinement and replacement, laid down by Russell and Burch in 1959.

Emotional Stress and Cardiovascular Complications in Animal Models: A Review of the Influence of Stress Type

PubMed Central

Crestani, Carlos C.

2016-01-01

Emotional stress has been recognized as a modifiable risk factor for cardiovascular diseases. The impact of stress on physiological and psychological processes is determined by characteristics of the stress stimulus. For example, distinct responses are induced by acute vs. chronic aversive stimuli. Additionally, the magnitude of stress responses has been reported to be inversely related to the degree of predictability of the aversive stimulus. Therefore, the purpose of the present review was to discuss experimental research in animal models describing the influence of stressor stimulus characteristics, such as chronicity and predictability, in cardiovascular dysfunctions induced by emotional stress. Regarding chronicity, the importance of cardiovascular and autonomic adjustments during acute stress sessions and cardiovascular consequences of frequent stress response activation during repeated exposure to aversive threats (i.e., chronic stress) is discussed. Evidence of the cardiovascular and autonomic changes induced by chronic stressors involving daily exposure to the same stressor (predictable) vs. different stressors (unpredictable) is reviewed and discussed in terms of the impact of predictability in cardiovascular dysfunctions induced by stress. PMID:27445843

Underwater Sound Propagation Modeling Methods for Predicting Marine Animal Exposure.

PubMed

Hamm, Craig A; McCammon, Diana F; Taillefer, Martin L

2016-01-01

The offshore exploration and production (E&P) industry requires comprehensive and accurate ocean acoustic models for determining the exposure of marine life to the high levels of sound used in seismic surveys and other E&P activities. This paper reviews the types of acoustic models most useful for predicting the propagation of undersea noise sources and describes current exposure models. The severe problems caused by model sensitivity to the uncertainty in the environment are highlighted to support the conclusion that it is vital that risk assessments include transmission loss estimates with statistical measures of confidence.

Modeling low-dose mortality and disease incubation period of inhalational anthrax in the rabbit.

PubMed

Gutting, Bradford W; Marchette, David; Sherwood, Robert; Andrews, George A; Director-Myska, Alison; Channel, Stephen R; Wolfe, Daniel; Berger, Alan E; Mackie, Ryan S; Watson, Brent J; Rukhin, Andrey

2013-07-21

There is a need to advance our ability to conduct credible human risk assessments for inhalational anthrax associated with exposure to a low number of bacteria. Combining animal data with computational models of disease will be central in the low-dose and cross-species extrapolations required in achieving this goal. The objective of the current work was to apply and advance the competing risks (CR) computational model of inhalational anthrax where data was collected from NZW rabbits exposed to aerosols of Ames strain Bacillus anthracis. An initial aim was to parameterize the CR model using high-dose rabbit data and then conduct a low-dose extrapolation. The CR low-dose attack rate was then compared against known low-dose rabbit data as well as the low-dose curve obtained when the entire rabbit dose-response data set was fitted to an exponential dose-response (EDR) model. The CR model predictions demonstrated excellent agreement with actual low-dose rabbit data. We next used a modified CR model (MCR) to examine disease incubation period (the time to reach a fever >40 °C). The MCR model predicted a germination period of 14.5h following exposure to a low spore dose, which was confirmed by monitoring spore germination in the rabbit lung using PCR, and predicted a low-dose disease incubation period in the rabbit between 14.7 and 16.8 days. Overall, the CR and MCR model appeared to describe rabbit inhalational anthrax well. These results are discussed in the context of conducting laboratory studies in other relevant animal models, combining the CR/MCR model with other computation models of inhalational anthrax, and using the resulting information towards extrapolating a low-dose response prediction for man. Published by Elsevier Ltd.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics.

PubMed

Hollings, Tracey; Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics

PubMed Central

Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning. PMID:28837685

Physiologically based pharmacokinetic model for quinocetone in pigs and extrapolation to mequindox.

PubMed

Zhu, Xudong; Huang, Lingli; Xu, Yamei; Xie, Shuyu; Pan, Yuanhu; Chen, Dongmei; Liu, Zhenli; Yuan, Zonghui

2017-02-01

Physiologically based pharmacokinetic (PBPK) models are scientific methods used to predict veterinary drug residues that may occur in food-producing animals, and which have powerful extrapolation ability. Quinocetone (QCT) and mequindox (MEQ) are widely used in China for the prevention of bacterial infections and promoting animal growth, but their abuse causes a potential threat to human health. In this study, a flow-limited PBPK model was developed to simulate simultaneously residue depletion of QCT and its marker residue dideoxyquinocetone (DQCT) in pigs. The model included compartments for blood, liver, kidney, muscle and fat and an extra compartment representing the other tissues. Physiological parameters were obtained from the literature. Plasma protein binding rates, renal clearances and tissue/plasma partition coefficients were determined by in vitro and in vivo experiments. The model was calibrated and validated with several pharmacokinetic and residue-depletion datasets from the literature. Sensitivity analysis and Monte Carlo simulations were incorporated into the PBPK model to estimate individual variation of residual concentrations. The PBPK model for MEQ, the congener compound of QCT, was built through cross-compound extrapolation based on the model for QCT. The QCT model accurately predicted the concentrations of QCT and DQCT in various tissues at most time points, especially the later time points. Correlation coefficients between predicted and measured values for all tissues were greater than 0.9. Monte Carlo simulations showed excellent consistency between estimated concentration distributions and measured data points. The extrapolation model also showed good predictive power. The present models contribute to improve the residue monitoring systems of QCT and MEQ, and provide evidence of the usefulness of PBPK model extrapolation for the same kinds of compounds.

Could gastropods crawl using Newtonian mucus?

NASA Astrophysics Data System (ADS)

Lai, Janice; Vazquez-Torres, Maria; Del Alamo, Juan C.; Rodriguez-Rodriguez, Javier; Lasheras, Juan C.

2010-11-01

The locomotion of terrestrial gastropods is driven by a train of periodic muscle contractions (pedal waves) and relaxations (interwaves) that propagate from their tail to their head (direct waves). We study the locomotion of these animals on smooth flat surfaces by measuring the three-dimensional displacements of the ventral foot surface induced by the passage of the waves. A simple model based on lubrication theory is proposed in accordance with the experimental observations. This model uncovers a new mode of locomotion that works even when the lubricant between the foot and the animal is Newtonian. The model can also be adapted to situations where the animal's foot is in contact with the ground only at discrete points, as is the case when it crawls on a wire mesh or on rough soil surfaces. Furthermore, comparison between the stress exerted by the animal on the substrate and the model predictions allows us to clarify the role of the complex rheology observed in the mucus of terrestrial gastropods.

Predictive animal models of mania: hits, misses and future directions

PubMed Central

Young, Jared W; Henry, Brook L; Geyer, Mark A

2011-01-01

Mania has long been recognized as aberrant behaviour indicative of mental illness. Manic states include a variety of complex and multifaceted symptoms that challenge clear clinical distinctions. Symptoms include over-activity, hypersexuality, irritability and reduced need for sleep, with cognitive deficits recently linked to functional outcome. Current treatments have arisen through serendipity or from other disorders. Hence, treatments are not efficacious for all patients, and there is an urgent need to develop targeted therapeutics. Part of the drug discovery process is the assessment of therapeutics in animal models. Here we review pharmacological, environmental and genetic manipulations developed to test the efficacy of therapeutics in animal models of mania. The merits of these models are discussed in terms of the manipulation used and the facet of mania measured. Moreover, the predictive validity of these models is discussed in the context of differentiating drugs that succeed or fail to meet criteria as approved mania treatments. The multifaceted symptomatology of mania has not been reflected in the majority of animal models, where locomotor activity remains the primary measure. This approach has resulted in numerous false positives for putative treatments. Recent work highlights the need to utilize multivariate strategies to enable comprehensive assessment of affective and cognitive dysfunction. Advances in therapeutic treatment may depend on novel models developed with an integrated approach that includes: (i) a comprehensive battery of tests for different aspects of mania, (ii) utilization of genetic information to establish aetiological validity and (iii) objective quantification of patient behaviour with translational cross-species paradigms. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21410454

Designing a Minimal Intervention Strategy to Control Taenia solium.

PubMed

Lightowlers, Marshall W; Donadeu, Meritxell

2017-06-01

Neurocysticercosis is an important cause of epilepsy in many developing countries. The disease is a zoonosis caused by the cestode parasite Taenia solium. Many potential intervention strategies are available, however none has been able to be implemented and sustained. Here we predict the impact of some T. solium interventions that could be applied to prevent transmission through pigs, the parasite's natural animal intermediate host. These include minimal intervention strategies that are predicted to be effective and likely to be feasible. Logical models are presented which reflect changes in the risk that age cohorts of animals have for their potential to transmit T. solium. Interventions that include a combined application of vaccination, plus chemotherapy in young animals, are the most effective. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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

PubMed

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

2018-02-26

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

Acquisition and extinction in autoshaping.

PubMed

Kakade, Sham; Dayan, Peter

2002-07-01

C. R. Gallistel and J. Gibbon (2000) presented quantitative data on the speed with which animals acquire behavioral responses during autoshaping, together with a statistical model of learning intended to account for them. Although this model captures the form of the dependencies among critical variables, its detailed predictions are substantially at variance with the data. In the present article, further key data on the speed of acquisition are used to motivate an alternative model of learning, in which animals can be interpreted as paying different amounts of attention to stimuli according to estimates of their differential reliabilities as predictors.

Tigers and their prey: Predicting carnivore densities from prey abundance

USGS Publications Warehouse

Karanth, K.U.; Nichols, J.D.; Kumar, N.S.; Link, W.A.; Hines, J.E.

2004-01-01

The goal of ecology is to understand interactions that determine the distribution and abundance of organisms. In principle, ecologists should be able to identify a small number of limiting resources for a species of interest, estimate densities of these resources at different locations across the landscape, and then use these estimates to predict the density of the focal species at these locations. In practice, however, development of functional relationships between abundances of species and their resources has proven extremely difficult, and examples of such predictive ability are very rare. Ecological studies of prey requirements of tigers Panthera tigris led us to develop a simple mechanistic model for predicting tiger density as a function of prey density. We tested our model using data from a landscape-scale long-term (1995-2003) field study that estimated tiger and prey densities in 11 ecologically diverse sites across India. We used field techniques and analytical methods that specifically addressed sampling and detectability, two issues that frequently present problems in macroecological studies of animal populations. Estimated densities of ungulate prey ranged between 5.3 and 63.8 animals per km2. Estimated tiger densities (3.2-16.8 tigers per 100 km2) were reasonably consistent with model predictions. The results provide evidence of a functional relationship between abundances of large carnivores and their prey under a wide range of ecological conditions. In addition to generating important insights into carnivore ecology and conservation, the study provides a potentially useful model for the rigorous conduct of macroecological science.

Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model

PubMed Central

2009-01-01

Background Feed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism) known to play major roles in growth and body composition. Results For validation, we compared our model's predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention), and body composition over a timespan of several months, longer than most of the previously developed predictive models. Conclusion We demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models. PMID:19903354

Prediction of plant lncRNA by ensemble machine learning classifiers.

PubMed

Simopoulos, Caitlin M A; Weretilnyk, Elizabeth A; Golding, G Brian

2018-05-02

In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are identified and functionally verified. This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function.

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

PubMed Central

Su, Guosheng; Christensen, Ole F.; Ostersen, Tage; Henryon, Mark; Lund, Mogens S.

2012-01-01

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions. PMID:23028912

Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology

PubMed Central

Moore, Sean; Shrestha, Sourya; Tomlinson, Kyle W.; Vuong, Holly

2012-01-01

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector–parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46–77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change. PMID:22072451

Experimental animal models of encapsulating peritoneal sclerosis.

PubMed

Hoff, Catherine M

2005-04-01

Encapsulating peritoneal sclerosis (EPS) is an infrequent, but extremely serious complication of long-term peritoneal dialysis. The cause of EPS is unclear, but the low incidence suggests that it is most likely multifactorial. The elucidation of developmental pathways and predictive markers of EPS would facilitate the identification and management of high-risk patients. Animal models are often used to define pathways of disease progression and to test strategies for treatment and prevention in the patient population. Ideally such models could help to define the cause of EPS and its developmental pathways, to facilitate the identification of contributing factors and predictive markers, and to provide a system to test therapeutic strategies. Researchers have studied several rodent models of EPS that rely on chronic chemical irritation (for example, bleach, low-pH solution, chlorhexidine gluconate) to induce peritoneal sclerosis and abdominal encapsulation. Development in all models is progressive, with inflammation giving way to peritoneal fibrosis or sclerosis with accumulating membrane damage, culminating in cocoon formation. Microscopic findings are similar to those proposed as diagnostic criteria for clinical EPS: an initial inflammatory infiltrate and submesothelial thickening, collagen deposition, and activation and proliferation of peritoneal fibroblasts. The potential to block progression of peritoneal sclerosis in these models by anti-inflammatory, antifibrotic, and anti-angiogenic agents, and by inhibitors of the renin-angiotensin system have been demonstrated. Animal models based on clinically relevant risk factors (for example, uremia, peritonitis, and long-term exposure to dialysis solutions) now represent the next step in model development.

Development of an in vitro Hepatocyte Model to Investigate Chemical Mode of Action

EPA Science Inventory

There is a clear need to identify and characterize the potential of liver in vitro models that can be used to replace animals for mode of action analysis. Our goal is to use in vitro models for mode of action prediction which recapitulate critical cellular processes underlying in...

A model to account for the consequences of host nutrition on the outcome of gastrointestinal parasitism in sheep: model evaluation.

PubMed

Vagenas, D; Bishop, S C; Kyriazakis, I

2007-08-01

This paper describes sensitivity analyses and expectations obtained from a mathematical model developed to account for the effects of host nutrition on the consequences of gastrointestinal parasitism in sheep. The scenarios explored included different levels of parasitic challenge at different planes of nutrition, for hosts differing only in their characteristics for growth. The model was able to predict the consequences of host nutrition on the outcome of parasitism, in terms of worm burden, number of eggs excreted per gram faeces and animal performance. The model outputs predict that conclusions on the ability of hosts of different characteristics for growth to cope with parasitism (i.e. resistance) depend on the plane of nutrition. Furthermore, differences in the growth rate of sheep, on their own, are not sufficient to account for differences in the observed resistance of animals. The model forms the basis for evaluating the consequences of differing management strategies and environments, such as breeding for certain traits associated with resistance and nutritional strategies, on the consequences of gastrointestinal parasitism on sheep.

Leatherbacks swimming in silico: modeling and verifying their momentum and heat balance using computational fluid dynamics.

PubMed

Dudley, Peter N; Bonazza, Riccardo; Jones, T Todd; Wyneken, Jeanette; Porter, Warren P

2014-01-01

As global temperatures increase throughout the coming decades, species ranges will shift. New combinations of abiotic conditions will make predicting these range shifts difficult. Biophysical mechanistic niche modeling places bounds on an animal's niche through analyzing the animal's physical interactions with the environment. Biophysical mechanistic niche modeling is flexible enough to accommodate these new combinations of abiotic conditions. However, this approach is difficult to implement for aquatic species because of complex interactions among thrust, metabolic rate and heat transfer. We use contemporary computational fluid dynamic techniques to overcome these difficulties. We model the complex 3D motion of a swimming neonate and juvenile leatherback sea turtle to find power and heat transfer rates during the stroke. We combine the results from these simulations and a numerical model to accurately predict the core temperature of a swimming leatherback. These results are the first steps in developing a highly accurate mechanistic niche model, which can assists paleontologist in understanding biogeographic shifts as well as aid contemporary species managers about potential range shifts over the coming decades.

e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N

2013-05-01

A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance correlation coefficients over 0.80 for annual pasture utilisation, yields of milk and milk solids (MS; fat plus protein), and of 0.69 and 0.48 for LW and BCS, respectively. A simulation of two contrasting dairy systems is presented to show the practical use of the model. The model can be used to explore the effects of feeding level and genetic merit and their interactions for grazing dairy systems, evaluating the trade-offs between profit and the associated risk.

The oxytocin system in drug discovery for autism: Animal models and novel therapeutic strategies

PubMed Central

Modi, Meera E.; Young, Larry J.

2012-01-01

Animal models and behavioral paradigms are critical for elucidating the neural mechanism involved in complex behaviors, including social cognition. Both genotype and phenotype based models have implicated the neuropeptide oxytocin (OT) in the regulation of social behavior. Based on the findings in animal models, alteration of the OT system has been hypothesized to play a role in the social deficits associated with autism and other neuropsychiatric disorders. While the evidence linking the peptide to the etiology of the disorder is not yet conclusive, evidence from multiple animal models suggest modulation of the OT system may be a viable strategy for the pharmacological treatment of social deficits. In this review, we will discuss how animal models have been utilized to understand the role of OT in social cognition and how those findings can be applied to the conceptualization and treatment of the social impairments in ASD. Animal models with genetic alterations of the OT system, like the OT, OT receptor and CD38 knock-out mice, and those with phenotypic variation in social behavior, like BTBR inbred mice and prairie voles, coupled with behavioral paradigms with face and construct validity may prove to have predictive validity for identifying the most efficacious methods of stimulating the OT system to enhance social cognition in humans. The widespread use of strong animal models of social cognition has the potential yield pharmacological, interventions for the treatment social impairments psychiatric disorders. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior. PMID:22206823

Towards a better preclinical model of PTSD: characterizing animals with weak extinction, maladaptive stress responses and low plasma corticosterone.

PubMed

Reznikov, Roman; Diwan, Mustansir; Nobrega, José N; Hamani, Clement

2015-02-01

Most of the available preclinical models of PTSD have focused on isolated behavioural aspects and have not considered individual variations in response to stress. We employed behavioural criteria to identify and characterize a subpopulation of rats that present several features analogous to PTSD-like states after exposure to classical fear conditioning. Outbred Sprague-Dawley rats were segregated into weak- and strong-extinction groups on the basis of behavioural scores during extinction of conditioned fear responses. Animals were subsequently tested for anxiety-like behaviour in the open-field test (OFT), novelty suppressed feeding (NSF) and elevated plus maze (EPM). Baseline plasma corticosterone was measured prior to any behavioural manipulation. In a second experiment, rats underwent OFT, NSF and EPM prior to being subjected to fear conditioning to ascertain whether or not pre-stress levels of anxiety-like behaviours could predict extinction scores. We found that 25% of rats exhibit low extinction rates of conditioned fear, a feature that was associated with increased anxiety-like behaviour across multiple tests in comparison to rats showing strong extinction. In addition, weak-extinction animals showed low levels of corticosterone prior to fear conditioning, a variable that seemed to predict extinction recall scores. In a separate experiment, anxiety measures taken prior to fear conditioning were not predictive of a weak-extinction phenotype, suggesting that weak-extinction animals do not show detectable traits of anxiety in the absence of a stressful experience. These findings suggest that extinction impairment may be used to identify stress-vulnerable rats, thus providing a useful model for elucidating mechanisms and investigating potential treatments for PTSD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Testing strategies for embryo-fetal toxicity of human pharmaceuticals. Animal models vs. in vitro approaches: a workshop report.

PubMed

van der Laan, Jan Willem; Chapin, Robert E; Haenen, Bert; Jacobs, Abigail C; Piersma, Aldert

2012-06-01

Reproductive toxicity testing is characterized by high animal use. For registration of pharmaceutical compounds, developmental toxicity studies are usually conducted in both rat and rabbits. Efforts have been underway for a long time to design alternatives to animal use. Implementation has lagged, partly because of uncertainties about the applicability domain of the alternatives. The reproductive cycle is complex and not all mechanisms of development can be mimicked in vitro. Therefore, efforts are underway to characterize the available alternative tests with regard to the mechanism of action they include. One alternative test is the mouse embryonic stem cell test (EST), which has been studied since the late 1990s. It is a genuine 3R "alternative" assay as it is essentially animal-free. A meeting was held to review the state-of-the-art of various in vitro models for prediction of developmental toxicity. Although the predictivity of individual assays is improving, a battery of several assays is likely to have even higher predictivity, which is necessary for regulatory acceptance. The workshop concluded that an important first step is a thorough survey of the existing rat and rabbit studies, to fully characterize the frequency of responses and the types of effects seen. At the same time, it is important to continue the optimization of in vitro assays. As more experience accumulates, the optimal conditions, assay structure, and applicability of the alternative assays are expected to emerge. Copyright © 2012 Elsevier Inc. All rights reserved.

Tissue engineering of the bladder--reality or myth? A systematic review.

PubMed

Sloff, Marije; Simaioforidis, Vasileios; de Vries, Rob; Oosterwijk, Egbert; Feitz, Wout

2014-10-01

We systematically reviewed preclinical studies in the literature to evaluate the potential of tissue engineering of the bladder. Study outcomes were compared to the available clinical evidence to assess the feasibility of tissue engineering for future clinical use. Preclinical studies of tissue engineering for bladder augmentation were identified through a systematic search of PubMed and Embase™ from January 1, 1980 to January 1, 2014. Primary studies in English were included if bladder reconstruction after partial cystectomy was performed using a tissue engineered biomaterial in any animal species, with cystometric bladder capacity as an outcome measure. Outcomes were compared to clinical studies available at http://www.clinicaltrials.gov and published clinical studies. A total of 28 preclinical studies are included, demonstrating remarkable heterogeneity in study characteristics and design. Studies in which preoperative bladder volumes were compared to postoperative volumes were considered the most clinically relevant (18 studies). Bladder augmentation through tissue engineering resulted in a normal bladder volume in healthy animals, with the influence of a cellular component being negligible. Furthermore, experiments in large animal models (pigs and dogs) approximated the desired bladder volume more accurately than in smaller species. The initial clinical experience was based on seemingly predictive healthy animal models with a promising outcome. Unfortunately these results were not substantiated in all clinical trials, revealing dissimilar outcomes in different clinical/disease backgrounds. Thus, the translational predictability of a model using healthy animals might be questioned. Through this systematic approach we present an unbiased overview of all published preclinical studies investigating the effect of bladder tissue engineering on cystometric bladder capacity. Preclinical research in healthy animals appears to show the feasibility of bladder augmentation by tissue engineering. However, in view of the disappointing clinical results based on healthy animal models new approaches should also be evaluated in preclinical models using dysfunctional/diseased bladders. This endeavor may aid in the development of clinically applicable tissue engineered bladder augmentation with satisfactory long-term outcome. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Plasma Retention and Systemic Kinetics of 90Sr Intramuscularly Injected in Female Nonhuman Primates

DOE PAGES

Poudel, Deepesh; Klumpp, John A.; Bertelli, Luiz; ...

2017-08-01

Thirteen female Rhesus macaques were intramuscularly injected with 90Sr(NO 3) 2 diluted in sodium citrate solution. The biokinetic data from these animals were compared against the predictions of the NCRP 156 wound models combined with the ICRP systemic models. We observed observed that the activities measured in plasma of these nonhuman primates (NHPs) were consistently lower than those predicted by the default human biokinetic models. The urinary excretion from the NHPs at times immediately after injection was much greater than that in humans. The fecal excretion rates were found to be in relatively better agreement with humans. Similarly, the activitiesmore » retained in the skeleton of the NHPs were lower than that in humans. These differences were attributed to the higher calcium diet of the NHPs (0.03 to 0.12 g/d/kg body weight) compared to that of humans. These observations were consistent with the early animal and human studies that showed the effect of calcium on strontium metabolism, specifically urinary excretion. Strontium is preferentially filtered at a much higher rate in kidneys than calcium because it is less completely bound to protein than is calcium. Furthermore, these differences, along with large inter-animal variability, should be considered when estimating the behavior of Sr in humans from the metabolic data in animals or vice-versa.« less

Plasma Retention and Systemic Kinetics of 90Sr Intramuscularly Injected in Female Nonhuman Primates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poudel, Deepesh; Klumpp, John A.; Bertelli, Luiz

Thirteen female Rhesus macaques were intramuscularly injected with 90Sr(NO 3) 2 diluted in sodium citrate solution. The biokinetic data from these animals were compared against the predictions of the NCRP 156 wound models combined with the ICRP systemic models. We observed observed that the activities measured in plasma of these nonhuman primates (NHPs) were consistently lower than those predicted by the default human biokinetic models. The urinary excretion from the NHPs at times immediately after injection was much greater than that in humans. The fecal excretion rates were found to be in relatively better agreement with humans. Similarly, the activitiesmore » retained in the skeleton of the NHPs were lower than that in humans. These differences were attributed to the higher calcium diet of the NHPs (0.03 to 0.12 g/d/kg body weight) compared to that of humans. These observations were consistent with the early animal and human studies that showed the effect of calcium on strontium metabolism, specifically urinary excretion. Strontium is preferentially filtered at a much higher rate in kidneys than calcium because it is less completely bound to protein than is calcium. Furthermore, these differences, along with large inter-animal variability, should be considered when estimating the behavior of Sr in humans from the metabolic data in animals or vice-versa.« less

Describing temporal variation in reticuloruminal pH using continuous monitoring data.

PubMed

Denwood, M J; Kleen, J L; Jensen, D B; Jonsson, N N

2018-01-01

Reticuloruminal pH has been linked to subclinical disease in dairy cattle, leading to considerable interest in identifying pH observations below a given threshold. The relatively recent availability of continuously monitored data from pH boluses gives new opportunities for characterizing the normal patterns of pH over time and distinguishing these from abnormal patterns using more sensitive and specific methods than simple thresholds. We fitted a series of statistical models to continuously monitored data from 93 animals on 13 farms to characterize normal variation within and between animals. We used a subset of the data to relate deviations from the normal pattern to the productivity of 24 dairy cows from a single herd. Our findings show substantial variation in pH characteristics between animals, although animals within the same farm tended to show more consistent patterns. There was strong evidence for a predictable diurnal variation in all animals, and up to 70% of the observed variation in pH could be explained using a simple statistical model. For the 24 animals with available production information, there was also a strong association between productivity (as measured by both milk yield and dry matter intake) and deviations from the expected diurnal pattern of pH 2 d before the productivity observation. In contrast, there was no association between productivity and the occurrence of observations below a threshold pH. We conclude that statistical models can be used to account for a substantial proportion of the observed variability in pH and that future work with continuously monitored pH data should focus on deviations from a predictable pattern rather than the frequency of observations below an arbitrary pH threshold. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Animal movement in the absence of predation: environmental drivers of movement strategies in a partial migration system

USGS Publications Warehouse

Bastille-Rousseau, Guillaume; Gibbs, James P.; Yackulic, Charles B.; Frair, Jacqueline L.; Cabrera, Fredy; Rousseau, Louis-Philippe

2016-01-01

Animal movement strategies including migration, dispersal, nomadism, and residency are shaped by broad-scale spatial-temporal structuring of the environment, including factors such as the degrees of spatial variation, seasonality and inter-annual predictability. Animal movement strategies, in turn, interact with the characteristics of individuals and the local distribution of resources to determine local patterns of resource selection with complex and poorly understood implications for animal fitness. Here we present a multi-scale investigation of animal movement strategies and resource selection. We consider the degree to which spatial variation, seasonality, and inter-annual predictability in resources drive migration patterns among different taxa and how movement strategies in turn shape local resource selection patterns. We focus on adult Galapagos giant tortoises Chelonoidis spp. as a model system since they display many movement strategies and evolved in the absence of predators of adults. Specifically, our analysis is based on 63 individuals among four taxa tracked on three islands over six years and almost 106 tortoise re-locations. Tortoises displayed a continuum of movement strategies from migration to sedentarism that were linked to the spatio-temporal scale and predictability of resource distributions. Movement strategies shaped patterns of resource selection. Specifically, migratory individuals displayed stronger selection toward areas where resources were more predictable among years than did non-migratory individuals, which indicates a selective advantage for migrants in seasonally structured, more predictable environments. Our analytical framework combines large-scale predictions for movement strategies, based on environmental structuring, with finer-scale analysis of space-use. Integrating different organizational levels of analysis provides a deeper understanding of the eco-evolutionary dynamics at play in the emergence and maintenance of migration and the critical role of resource predictability. Our results highlight that assessing the potential benefits of differential behavioral responses first requires an understanding of the interactions among movement strategies, resource selection and individual characteristics.

Are animal models predictive for human postmortem muscle protein degradation?

PubMed

Ehrenfellner, Bianca; Zissler, Angela; Steinbacher, Peter; Monticelli, Fabio C; Pittner, Stefan

2017-11-01

A most precise determination of the postmortem interval (PMI) is a crucial aspect in forensic casework. Although there are diverse approaches available to date, the high heterogeneity of cases together with the respective postmortal changes often limit the validity and sufficiency of many methods. Recently, a novel approach for time since death estimation by the analysis of postmortal changes of muscle proteins was proposed. It is however necessary to improve the reliability and accuracy, especially by analysis of possible influencing factors on protein degradation. This is ideally investigated on standardized animal models that, however, require legitimization by a comparison of human and animal tissue, and in this specific case of protein degradation profiles. Only if protein degradation events occur in comparable fashion within different species, respective findings can sufficiently be transferred from the animal model to application in humans. Therefor samples from two frequently used animal models (mouse and pig), as well as forensic cases with representative protein profiles of highly differing PMIs were analyzed. Despite physical and physiological differences between species, western blot analysis revealed similar patterns in most of the investigated proteins. Even most degradation events occurred in comparable fashion. In some other aspects, however, human and animal profiles depicted distinct differences. The results of this experimental series clearly indicate the huge importance of comparative studies, whenever animal models are considered. Although animal models could be shown to reflect the basic principles of protein degradation processes in humans, we also gained insight in the difficulties and limitations of the applicability of the developed methodology in different mammalian species regarding protein specificity and methodic functionality.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

FDA-approved drugs that are spermatotoxic in animals and the utility of animal testing for human risk prediction.

PubMed

Rayburn, Elizabeth R; Gao, Liang; Ding, Jiayi; Ding, Hongxia; Shao, Jun; Li, Haibo

2018-02-01

This study reviews FDA-approved drugs that negatively impact spermatozoa in animals, as well as how these findings reflect on observations in human male gametes. The FDA drug warning labels included in the DailyMed database and the peer-reviewed literature in the PubMed database were searched for information to identify single-ingredient, FDA-approved prescription drugs with spermatotoxic effects. A total of 235 unique, single-ingredient, FDA-approved drugs reported to be spermatotoxic in animals were identified in the drug labels. Forty-nine of these had documented negative effects on humans in either the drug label or literature, while 31 had no effect or a positive impact on human sperm. For the other 155 drugs that were spermatotoxic in animals, no human data was available. The current animal models are not very effective for predicting human spermatotoxicity, and there is limited information available about the impact of many drugs on human spermatozoa. New approaches should be designed that more accurately reflect the findings in men, including more studies on human sperm in vitro and studies using other systems (ex vivo tissue culture, xenograft models, in silico studies, etc.). In addition, the present data is often incomplete or reported in a manner that prevents interpretation of their clinical relevance. Changes should be made to the requirements for pre-clinical testing, drug surveillance, and the warning labels of drugs to ensure that the potential risks to human fertility are clearly indicated.

Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates

PubMed Central

Chambert, Thierry; Rotella, Jay J; Higgs, Megan D

2014-01-01

The investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. Individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark–recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of Tuljapurkar et al. (Ecology Letters, 12, 93, 2009) on dynamic heterogeneity. Model-selection approaches based on information criteria or Bayes factors have been used to address this question. Here, we suggest that, in addition to model-selection, model-checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. Specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. Posterior predictive checking is a straightforward and flexible approach for performing model checks in a Bayesian framework that is based on comparisons of observed data to model-generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. If discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. We illustrate this approach using analyses of vital rates with long-term mark–recapture data for Weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest. We show how posterior predictive checks can be used to strengthen inferences in ecological studies. We demonstrate the application of this method on analyses dealing with the question of individual reproductive heterogeneity in a population of Antarctic pinnipeds. PMID:24834335

An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus

USGS Publications Warehouse

Beatty, William; Jay, Chadwick V.; Fischbach, Anthony S.

2016-01-01

State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.

Continuous real-time in vivo measurement of cerebral nitric oxide supports theoretical predictions of an irreversible switching in cerebral ROS after sufficient exposure to external toxins.

PubMed

Finnerty, Niall J; O'Riordan, Saidhbhe L; Lowry, John P; Cloutier, Mathieu; Wellstead, Peter

2013-01-01

Mathematical models of the interactions between alphasynuclein (αS) and reactive oxygen species (ROS) predict a systematic and irreversible switching to damagingly high levels of ROS after sufficient exposure to risk factors associated with Parkinson's disease (PD). We tested this prediction by continuously monitoring real-time changes in neurochemical levels over periods of several days in animals exposed to a toxin known to cause Parkinsonian symptoms. Nitric oxide (NO) sensors were implanted in the brains of freely moving rats and the NO levels continuously recorded while the animals were exposed to paraquat (PQ) injections of various amounts and frequencies. Long-term, real-time measurement of NO in a cohort of animals showed systematic switching in levels when PQ injections of sufficient size and frequency were administered. The experimental observations of changes in NO imply a corresponding switching in endogenous ROS levels and support theoretical predictions of an irreversible change to damagingly high levels of endogenous ROS when PD risks are sufficiently large. Our current results only consider one form of PD risk, however, we are sufficiently confident in them to conclude that: (i) continuous long-term measurement of neurochemical dynamics provide a novel way to measure the temporal change and system dynamics which determine Parkinsonian damage, and (ii) the bistable feedback switching predicted by mathematical modelling seems to exist and that a deeper analysis of its characteristics would provide a way of understanding the pathogenic mechanisms that initiate Parkinsonian cell damage.

Separability of drag and thrust in undulatory animals and machines

PubMed Central

Bale, Rahul; Shirgaonkar, Anup A.; Neveln, Izaak D.; Bhalla, Amneet Pal Singh; MacIver, Malcolm A.; Patankar, Neelesh A.

2014-01-01

For nearly a century, researchers have tried to understand the swimming of aquatic animals in terms of a balance between the forward thrust from swimming movements and drag on the body. Prior approaches have failed to provide a separation of these two forces for undulatory swimmers such as lamprey and eels, where most parts of the body are simultaneously generating drag and thrust. We nonetheless show that this separation is possible, and delineate its fundamental basis in undulatory swimmers. Our approach unifies a vast diversity of undulatory aquatic animals (anguilliform, sub-carangiform, gymnotiform, bal-istiform, rajiform) and provides design principles for highly agile bioinspired underwater vehicles. This approach has practical utility within biology as well as engineering. It is a predictive tool for use in understanding the role of the mechanics of movement in the evolutionary emergence of morphological features relating to locomotion. For example, we demonstrate that the drag-thrust separation framework helps to predict the observed height of the ribbon fin of electric knifefish, a diverse group of neotropical fish which are an important model system in sensory neurobiology. We also show how drag-thrust separation leads to models that can predict the swimming velocity of an organism or a robotic vehicle. PMID:25491270

Schedule-induced polydipsia: a rat model of obsessive-compulsive disorder.

PubMed

Platt, Brian; Beyer, Chad E; Schechter, Lee E; Rosenzweig-Lipson, Sharon

2008-04-01

Obsessive-compulsive disorder (OCD) is difficult to model in animals due to the involvement of both mental (obsessions) and physical (compulsions) symptoms. Due to limitations of using animals to evaluate obsessions, OCD models are limited to evaluation of the compulsive and repetitive behaviors of animals. Of these, models of adjunctive behaviors offer the most value in regard to predicting efficacy of anti-OCD drugs in the clinic. Adjunctive behaviors are those that are maintained indirectly by the variables that control another behavior, rather than directly by their own typical controlling variables. Schedule-induced polydipsia (SIP) is an adjunctive model in which rats exhibit exaggerated drinking behavior (polydipsia) when presented with food pellets under a fixed-time schedule. The polydipsic response is an excessive manifestation of a normal behavior (drinking), providing face validity to the model. Furthermore, clinically effective drugs for the treatment of OCD decrease SIP. This protocol describes a rat SIP model of OCD and provides preclinical data for drugs that decrease polydipsia and are clinically effective in the treatment of OCD.

Drug administration in animal studies of cardiac arrest does not reflect human clinical experience

PubMed Central

Reynolds, Joshua C.; Rittenberger, Jon C.; Menegazzi, James J.

2007-01-01

Introduction To date, there is no evidence showing a benefit from any advanced cardiac life support (ACLS) medication in out-of-hospital cardiac arrest (OOHCA), despite animal data to the contrary. One explanation may be a difference in the time to first drug administration. Our previous work has shown the mean time to first drug administration in clinical trials is 19.4 minutes. We hypothesized that the average time to drug administration in large animal experiments occurs earlier than in OOHCA clinical trials. Methods We conducted a literature review between 1990 and 2006 in MEDLINE using the following MeSH headings: swine, dogs, resuscitation, heart arrest, EMS, EMT, ambulance, ventricular fibrillation, drug therapy, epinephrine, vasopressin, amiodarone, lidocaine, magnesium, and sodium bicarbonate. We reviewed the abstracts of 331 studies and 197 full manuscripts. Exclusion criteria included: non-peer reviewed, all without primary animal data, and traumatic models. From these, we identified 119 papers that contained unique information on time to medication administration. The data are reported as mean, ranges, and 95% confidence intervals. Mean time to first drug administration in animal laboratory studies and clinical trials was compared with a t-test. Regression analysis was performed to determine if time to drug predicted ROSC. Results Mean time to first drug administration in 2378 animals was 9.5 minutes (range 3.0–28.0; 95% CI around mean 2.78, 16.22). This is less than the time reported in clinical trials (19.4 min, p<0.001). Time to drug predicted ROSC (Odds Ratio 0.844; 95% CI 0.738, 0.966). Conclusion Shorter drug delivery time in animal models of cardiac arrest may be one reason for the failure of animal studies to translate successfully into the clinical arena. PMID:17360097

Risk factors for disclosure of additional tuberculous cattle in attested-clear herds that had one animal with a confirmed lesion of tuberculosis at slaughter during 2003 in Ireland.

PubMed

Olea-Popelka, F J; Costello, E; White, P; McGrath, G; Collins, J D; O'Keeffe, J; Kelton, D F; Berke, O; More, S; Martin, S W

2008-06-15

All the Irish cattle herds considered "clear" of bovine tuberculosis (BTB) having a single animal with a tuberculous lesion at slaughter during 2003 were identified. We performed a descriptive and logistic regression analysis to investigate whether selected risk factors had an association with the result of the herd test immediately after the tuberculous lesion was found ("Factory Lesion Test", FLT). At the FLT, only 19.7% (n=338) of these 1713 herds had 1 or more standard reactors. The lesioned animal was home-bred in 46% of the "source" herds; these herds had an increased risk (23.4%) of having at least 1 standard reactor animal relative to herds with a purchased-lesioned animal (16.6%) (RR=1.41). Our logistic models identified a number of important risk factors; two that appeared most important in predicting the FLT outcome were the time spent (residency) by the lesioned animal in the "source" herd, and the presence, or not, of the lesioned animal in a previous BTB episode in either the "source" herd, or the seller's herd in the case the lesioned animal was purchased. Our models fit the data well based on the Hosmer-Lemeshow test, however their sensitivity and specificity were very low (57% and 61% respectively). Surveillance of the cattle population for BTB using lesions found at slaughter is an essential component of an overall control program. Nonetheless, due to the poor predictability of the variables we measured, complete herd investigations are needed to help explain the FLT outcome of a herd.

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation.

PubMed

Saatchi, Mahdi; McClure, Mathew C; McKay, Stephanie D; Rolf, Megan M; Kim, JaeWoo; Decker, Jared E; Taxis, Tasia M; Chapple, Richard H; Ramey, Holly R; Northcutt, Sally L; Bauck, Stewart; Woodward, Brent; Dekkers, Jack C M; Fernando, Rohan L; Schnabel, Robert D; Garrick, Dorian J; Taylor, Jeremy F

2011-11-28

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

PubMed Central

2011-01-01

Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy. PMID:22122853

Prevalidation of an Acute Inhalation Toxicity Test Using the EpiAirway In Vitro Human Airway Model

PubMed Central

Jackson, George R.; Maione, Anna G.; Klausner, Mitchell

2018-01-01

Abstract Introduction: Knowledge of acute inhalation toxicity potential is important for establishing safe use of chemicals and consumer products. Inhalation toxicity testing and classification procedures currently accepted within worldwide government regulatory systems rely primarily on tests conducted in animals. The goal of the current work was to develop and prevalidate a nonanimal (in vitro) test for determining acute inhalation toxicity using the EpiAirway™ in vitro human airway model as a potential alternative for currently accepted animal tests. Materials and Methods: The in vitro test method exposes EpiAirway tissues to test chemicals for 3 hours, followed by measurement of tissue viability as the test endpoint. Fifty-nine chemicals covering a broad range of toxicity classes, chemical structures, and physical properties were evaluated. The in vitro toxicity data were utilized to establish a prediction model to classify the chemicals into categories corresponding to the currently accepted Globally Harmonized System (GHS) and the Environmental Protection Agency (EPA) system. Results: The EpiAirway prediction model identified in vivo rat-based GHS Acute Inhalation Toxicity Category 1–2 and EPA Acute Inhalation Toxicity Category I–II chemicals with 100% sensitivity and specificity of 43.1% and 50.0%, for GHS and EPA acute inhalation toxicity systems, respectively. The sensitivity and specificity of the EpiAirway prediction model for identifying GHS specific target organ toxicity-single exposure (STOT-SE) Category 1 human toxicants were 75.0% and 56.5%, respectively. Corrosivity and electrophilic and oxidative reactivity appear to be the predominant mechanisms of toxicity for the most highly toxic chemicals. Conclusions: These results indicate that the EpiAirway test is a promising alternative to the currently accepted animal tests for acute inhalation toxicity. PMID:29904643

Prevalidation of an Acute Inhalation Toxicity Test Using the EpiAirway In Vitro Human Airway Model.

PubMed

Jackson, George R; Maione, Anna G; Klausner, Mitchell; Hayden, Patrick J

2018-06-01

Introduction: Knowledge of acute inhalation toxicity potential is important for establishing safe use of chemicals and consumer products. Inhalation toxicity testing and classification procedures currently accepted within worldwide government regulatory systems rely primarily on tests conducted in animals. The goal of the current work was to develop and prevalidate a nonanimal ( in vitro ) test for determining acute inhalation toxicity using the EpiAirway™ in vitro human airway model as a potential alternative for currently accepted animal tests. Materials and Methods: The in vitro test method exposes EpiAirway tissues to test chemicals for 3 hours, followed by measurement of tissue viability as the test endpoint. Fifty-nine chemicals covering a broad range of toxicity classes, chemical structures, and physical properties were evaluated. The in vitro toxicity data were utilized to establish a prediction model to classify the chemicals into categories corresponding to the currently accepted Globally Harmonized System (GHS) and the Environmental Protection Agency (EPA) system. Results: The EpiAirway prediction model identified in vivo rat-based GHS Acute Inhalation Toxicity Category 1-2 and EPA Acute Inhalation Toxicity Category I-II chemicals with 100% sensitivity and specificity of 43.1% and 50.0%, for GHS and EPA acute inhalation toxicity systems, respectively. The sensitivity and specificity of the EpiAirway prediction model for identifying GHS specific target organ toxicity-single exposure (STOT-SE) Category 1 human toxicants were 75.0% and 56.5%, respectively. Corrosivity and electrophilic and oxidative reactivity appear to be the predominant mechanisms of toxicity for the most highly toxic chemicals. Conclusions: These results indicate that the EpiAirway test is a promising alternative to the currently accepted animal tests for acute inhalation toxicity.

Mathematical modeling and simulation in animal health. Part I: Moving beyond pharmacokinetics.

PubMed

Riviere, J E; Gabrielsson, J; Fink, M; Mochel, J

2016-06-01

The application of mathematical modeling to problems in animal health has a rich history in the form of pharmacokinetic modeling applied to problems in veterinary medicine. Advances in modeling and simulation beyond pharmacokinetics have the potential to streamline and speed-up drug research and development programs. To foster these goals, a series of manuscripts will be published with the following goals: (i) expand the application of modeling and simulation to issues in veterinary pharmacology; (ii) bridge the gap between the level of modeling and simulation practiced in human and veterinary pharmacology; (iii) explore how modeling and simulation concepts can be used to improve our understanding of common issues not readily addressed in human pharmacology (e.g. breed differences, tissue residue depletion, vast weight ranges among adults within a single species, interspecies differences, small animal species research where data collection is limited to sparse sampling, availability of different sampling matrices); and (iv) describe how quantitative pharmacology approaches could help understanding key pharmacokinetic and pharmacodynamic characteristics of a drug candidate, with the goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans, enabling critical decision making, and eventually bringing safe and effective medicines to patients. This study introduces these concepts and introduces new approaches to modeling and simulation as well as clearly articulate basic assumptions and good practices. The driving force behind these activities is to create predictive models that are based on solid physiological and pharmacological principles as well as adhering to the limitations that are fundamental to applying mathematical and statistical models to biological systems. © 2015 John Wiley & Sons Ltd.

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Imputation approaches for animal movement modeling

USGS Publications Warehouse

Scharf, Henry; Hooten, Mevin B.; Johnson, Devin S.

2017-01-01

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.

Animal personality and state-behaviour feedbacks: a review and guide for empiricists.

PubMed

Sih, Andrew; Mathot, Kimberley J; Moirón, María; Montiglio, Pierre-Olivier; Wolf, Max; Dingemanse, Niels J

2015-01-01

An exciting area in behavioural ecology focuses on understanding why animals exhibit consistent among-individual differences in behaviour (animal personalities). Animal personality has been proposed to emerge as an adaptation to individual differences in state variables, leading to the question of why individuals differ consistently in state. Recent theory emphasizes the role that positive feedbacks between state and behaviour can play in producing consistent among-individual covariance between state and behaviour, hence state-dependent personality. We review the role of feedbacks in recent models of adaptive personalities, and provide guidelines for empirical testing of model assumptions and predictions. We discuss the importance of the mediating effects of ecology on these feedbacks, and provide a roadmap for including state-behaviour feedbacks in behavioural ecology research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Animal models of binge drinking, current challenges to improve face validity.

PubMed

Jeanblanc, Jérôme; Rolland, Benjamin; Gierski, Fabien; Martinetti, Margaret P; Naassila, Mickael

2018-05-05

Binge drinking (BD), i.e., consuming a large amount of alcohol in a short period of time, is an increasing public health issue. Though no clear definition has been adopted worldwide the speed of drinking seems to be a keystone of this behavior. Developing relevant animal models of BD is a priority for gaining a better characterization of the neurobiological and psychobiological mechanisms underlying this dangerous and harmful behavior. Until recently, preclinical research on BD has been conducted mostly using forced administration of alcohol, but more recent studies used scheduled access to alcohol, to model more voluntary excessive intakes, and to achieve signs of intoxications that mimic the human behavior. The main challenges for future research are discussed regarding the need of good face validity, construct validity and predictive validity of animal models of BD. Copyright © 2018 Elsevier Ltd. All rights reserved.

Benefits of the destinations, not costs of the journeys, shape partial migration patterns.

PubMed

Yackulic, Charles B; Blake, Stephen; Bastille-Rousseau, Guillaume

2017-07-01

The reasons that lead some animals to seasonally migrate, and others to remain in the same area year-round, are poorly understood. Associations between traits, such as body size, and migration provide clues. For example, larger species and individuals are more likely to migrate. One explanation for this size bias in migration is that larger animals are capable of moving faster (movement hypothesis). However, body size is linked to many other biological processes. For instance, the energetic balances of larger animals are generally more sensitive to variation in food density because of body size effects on foraging and metabolism and this sensitivity could drive migratory decisions (forage hypothesis). Identifying the primary selective forces that drive migration ultimately requires quantifying fitness impacts over the full annual migratory cycle. Here, we develop a full annual migratory cycle model from metabolic and foraging theory to compare the importance of the forage and movement hypotheses. We parameterize the model for Galapagos tortoises, which were recently discovered to be size-dependent altitudinal migrants. The model predicts phenomena not included in model development including maximum body sizes, the body size at which individuals begin to migrate, and the seasonal timing of migration and these predictions generally agree with available data. Scenarios strongly support the forage hypothesis over the movement hypothesis. Furthermore, male Galapagos tortoises on Santa Cruz Island would be unable to grow to their enormous sizes without access to both highlands and lowlands. Whereas recent research has focused on links between traits and the migratory phases of the migratory cycle, we find that effects of body size on the non-migratory phases are far more important determinants of the propensity to migrate. Larger animals are more sensitive to changing forage conditions than smaller animals with implications for maintenance of migration and body size in the face of environmental change. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics.

PubMed

Zhang, Liping; Wang, Li; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2017-03-04

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)₄ model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends.

Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

PubMed Central

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. PMID:26441629

Food-web models predict species abundances in response to habitat change.

PubMed

Gotelli, Nicholas J; Ellison, Aaron M

2006-10-01

Plant and animal population sizes inevitably change following habitat loss, but the mechanisms underlying these changes are poorly understood. We experimentally altered habitat volume and eliminated top trophic levels of the food web of invertebrates that inhabit rain-filled leaves of the carnivorous pitcher plant Sarracenia purpurea. Path models that incorporated food-web structure better predicted population sizes of food-web constituents than did simple keystone species models, models that included only autecological responses to habitat volume, or models including both food-web structure and habitat volume. These results provide the first experimental confirmation that trophic structure can determine species abundances in the face of habitat loss.

Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts

USGS Publications Warehouse

Conn, Paul B.; Johnson, Devin S.; Ver Hoef, Jay M.; Hooten, Mevin B.; London, Joshua M.; Boveng, Peter L.

2015-01-01

Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal (Phoca largha) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.

A multimodal detection model of dolphins to estimate abundance validated by field experiments.

PubMed

Akamatsu, Tomonari; Ura, Tamaki; Sugimatsu, Harumi; Bahl, Rajendar; Behera, Sandeep; Panda, Sudarsan; Khan, Muntaz; Kar, S K; Kar, C S; Kimura, Satoko; Sasaki-Yamamoto, Yukiko

2013-09-01

Abundance estimation of marine mammals requires matching of detection of an animal or a group of animal by two independent means. A multimodal detection model using visual and acoustic cues (surfacing and phonation) that enables abundance estimation of dolphins is proposed. The method does not require a specific time window to match the cues of both means for applying mark-recapture method. The proposed model was evaluated using data obtained in field observations of Ganges River dolphins and Irrawaddy dolphins, as examples of dispersed and condensed distributions of animals, respectively. The acoustic detection probability was approximately 80%, 20% higher than that of visual detection for both species, regardless of the distribution of the animals in present study sites. The abundance estimates of Ganges River dolphins and Irrawaddy dolphins fairly agreed with the numbers reported in previous monitoring studies. The single animal detection probability was smaller than that of larger cluster size, as predicted by the model and confirmed by field data. However, dense groups of Irrawaddy dolphins showed difference in cluster sizes observed by visual and acoustic methods. Lower detection probability of single clusters of this species seemed to be caused by the clumped distribution of this species.

Difficulties associated with predicting forage intake by grazing beef cows

USDA-ARS?s Scientific Manuscript database

The current National Research Council (NRC) model is based on a single equation that relates dry matter intake (DMI) to metabolic size and net energy density of the diet offered and was a significant improvement over previous models. However, observed DMI by grazing animals can be conceptualized by...

Estimation of the upper bound of predictive performance for alternative models that use in vivo reference data (OpenTox USA 2017)

EPA Science Inventory

The number of chemicals with limited toxicological information for chemical safety decision-making has accelerated alternative model development, which often are evaluated via referencing animal toxicology studies. In vivo studies are generally considered the standard for hazard ...

Estimation of Wildlife Hazard Levels Using Interspecies Correlation Models and Standard Laboratory Rodent Toxicity Data

EPA Science Inventory

Toxicity data from laboratory rodents are widely available and frequently used in human health assessments as an animal model. We explore the possibility of using single rodent acute toxicity values to predict chemical toxicity to a diversity of wildlife species and to estimate ...

Investigating Complexity Using Excel and Visual Basic.

ERIC Educational Resources Information Center

Zetie, K. P.

2001-01-01

Shows how some of the simple ideas in complexity can be investigated using a spreadsheet and a macro written in Visual Basic. Shows how the sandpile model of Bak, Chao, and Wiesenfeld can be simulated and animated. The model produces results that cannot easily be predicted from its properties. (Author/MM)

A 3D analysis of oxygen transfer in a low-cost micro-bioreactor for animal cell suspension culture.

PubMed

Yu, P; Lee, T S; Zeng, Y; Low, H T

2007-01-01

A 3D numerical model was developed to study the flow field and oxygen transport in a micro-bioreactor with a rotating magnetic bar on the bottom to mix the culture medium. The Reynolds number (Re) was kept in the range of 100-716 to ensure laminar environment for animal cell culture. The volumetric oxygen transfer coefficient (k(L)a) was determined from the oxygen concentration distribution. It was found that the effect of the cell consumption on k(L)a could be negligible. A correlation was proposed to predict the liquid-phase oxygen transfer coefficient (k(Lm)) as a function of Re. The overall oxygen transfer coefficient (k(L)) was obtained by the two-resistance model. Another correlation, within an error of 15%, was proposed to estimate the minimum oxygen concentration to avoid cell hypoxia. By combination of the correlations, the maximum cell density, which the present micro-bioreactor could support, was predicted to be in the order of 10(12) cells m(-3). The results are comparable with typical values reported for animal cell growth in mechanically stirred bioreactors.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.

PubMed

Sweeney, Timothy E; Lofgren, Shane; Khatri, Purvesh; Rogers, Angela J

2017-08-01

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P < 1E -16 ). Neutrophil signatures are enriched in both animal and human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics.

PubMed

Ngo, L; Ho, H; Hunter, P; Quinn, K; Thomson, A; Pearson, G

2016-02-01

Post-mortem measurements (cold weight, grade and external carcass linear dimensions) as well as live animal data (age, breed, sex) were used to predict ovine primal and retail cut weights for 792 lamb carcases. Significant levels of variance could be explained using these predictors. The predictive power of those measurements on primal and retail cut weights was studied by using the results from principal component analysis and the absolute value of the t-statistics of the linear regression model. High prediction accuracy for primal cut weight was achieved (adjusted R(2) up to 0.95), as well as moderate accuracy for key retail cut weight: tenderloins (adj-R(2)=0.60), loin (adj-R(2)=0.62), French rack (adj-R(2)=0.76) and rump (adj-R(2)=0.75). The carcass cold weight had the best predictive power, with the accuracy increasing by around 10% after including the next three most significant variables. Copyright © 2015 Elsevier Ltd. All rights reserved.

Animating climate model data

NASA Astrophysics Data System (ADS)

DaPonte, John S.; Sadowski, Thomas; Thomas, Paul

2006-05-01

This paper describes a collaborative project conducted by the Computer Science Department at Southern Connecticut State University and NASA's Goddard Institute for Space Science (GISS). Animations of output from a climate simulation math model used at GISS to predict rainfall and circulation have been produced for West Africa from June to September 2002. These early results have assisted scientists at GISS in evaluating the accuracy of the RM3 climate model when compared to similar results obtained from satellite imagery. The results presented below will be refined to better meet the needs of GISS scientists and will be expanded to cover other geographic regions for a variety of time frames.

Animal models of serotonergic psychedelics.

PubMed

Hanks, James B; González-Maeso, Javier

2013-01-16

The serotonin 5-HT(2A) receptor is the major target of psychedelic drugs such as lysergic acid diethylamide (LSD), mescaline, and psilocybin. Serotonergic psychedelics induce profound effects on cognition, emotion, and sensory processing that often seem uniquely human. This raises questions about the validity of animal models of psychedelic drug action. Nonetheless, recent findings suggest behavioral abnormalities elicited by psychedelics in rodents that predict such effects in humans. Here we review the behavioral effects induced by psychedelic drugs in rodent models, discuss the translational potential of these findings, and define areas where further research is needed to better understand the molecular mechanisms and neuronal circuits underlying their neuropsychological effects.

Animal Models of Serotonergic Psychedelics

PubMed Central

2012-01-01

The serotonin 5-HT2A receptor is the major target of psychedelic drugs such as lysergic acid diethylamide (LSD), mescaline, and psilocybin. Serotonergic psychedelics induce profound effects on cognition, emotion, and sensory processing that often seem uniquely human. This raises questions about the validity of animal models of psychedelic drug action. Nonetheless, recent findings suggest behavioral abnormalities elicited by psychedelics in rodents that predict such effects in humans. Here we review the behavioral effects induced by psychedelic drugs in rodent models, discuss the translational potential of these findings, and define areas where further research is needed to better understand the molecular mechanisms and neuronal circuits underlying their neuropsychological effects. PMID:23336043

Stochastic feeding dynamics arise from the need for information and energy.

PubMed

Scholz, Monika; Dinner, Aaron R; Levine, Erel; Biron, David

2017-08-29

Animals regulate their food intake in response to the available level of food. Recent observations of feeding dynamics in small animals showed feeding patterns of bursts and pauses, but their function is unknown. Here, we present a data-driven decision-theoretical model of feeding in Caenorhabditis elegans Our central assumption is that food intake serves a dual purpose: to gather information about the external food level and to ingest food when the conditions are good. The model recapitulates experimentally observed feeding patterns. It naturally implements trade-offs between speed versus accuracy and exploration versus exploitation in responding to a dynamic environment. We find that the model predicts three distinct regimes in responding to a dynamical environment, with a transition region where animals respond stochastically to periodic signals. This stochastic response accounts for previously unexplained experimental data.

Antidepressant-like effects of methanol extract of Hibiscus tiliaceus flowers in mice

PubMed Central

2012-01-01

Background Hibiscus tiliaceus L. (Malvaceae) is used in postpartum disorders. Our purpose was to examine the antidepressant, anxiolytic and sedative actions of the methanol extract of H. tiliaceus flowers using animal models. Methods Adult male Swiss albino mice were treated with saline, standard drugs or methanol extract of H. tiliaceus and then subjected to behavioral tests. The forced swimming and tail suspension tests were used as predictive animal models of antidepressant activity, where the time of immobility was considered. The animals were submitted to the elevated plus-maze and ketamine-induced sleeping time to assess anxiolytic and sedative activities, respectively. Results Methanol extract of H. tiliaceus significantly decreased the duration of immobility in both animal models of antidepressant activity, forced swimming and tail suspension tests. This extract did not potentiate the effect of ketamine-induced hypnosis, as determined by the time to onset and duration of sleeping time. Conclusion Our results indicate an antidepressant-like profile of action for the extract of Hibiscus tiliaceus without sedative side effect. PMID:22494845

New Breed of Mice May Improve Accuracy for Preclinical Testing of Cancer Drugs | Poster

Cancer.gov

A new breed of lab animals, dubbed “glowing head mice,” may do a better job than conventional mice in predicting the success of experimental cancer drugs—while also helping to meet an urgent need for more realistic preclinical animal models. The mice were developed to tolerate often-used light-emitting molecules, such as luciferase from fireflies and green fluorescent protein

Medications Development for the Treatment of Alcohol Use Disorder: Insights into the Predictive Value of Animal and Human Laboratory Models

PubMed Central

Yardley, Megan M.; Ray, Lara A.

2016-01-01

Development of effective treatments for alcohol use disorder (AUD) represents an important public health goal. This review provides a summary of completed preclinical and clinical studies testing pharmacotherapies for treatment of AUD. We discuss opportunities for improving the translation from preclinical findings to clinical trial outcomes, focusing on the validity and predictive value of animal and human laboratory models of AUD. Specifically, while preclinical studies of medications development have offered important insights into the neurobiology of the disorder and alcohol's molecular targets, limitations include the lack of standardized methods and streamlined processes whereby animal studies can readily inform human studies. Behavioral pharmacology studies provide a less expensive and valuable opportunity to assess the feasibility of a pharmacotherapy prior to initiating larger scale clinical trials by providing insights into the mechanism of the drug, which can then inform recruitment, analyses, and assessments. Summary tables are provided to illustrate the wide range of preclinical, human laboratory, and clinical studies of medications development for alcoholism. Taken together, this review highlights the challenges associated with animal paradigms, human laboratory studies and clinical trials with the overarching goal of advancing treatment development and highlighting opportunities to bridge the gap between preclinical and clinical research. PMID:26833803

The human skin/chick chorioallantoic membrane model accurately predicts the potency of cosmetic allergens.

PubMed

Slodownik, Dan; Grinberg, Igor; Spira, Ram M; Skornik, Yehuda; Goldstein, Ronald S

2009-04-01

The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin.

Direct and indirect genetic and fine-scale location effects on breeding date in song sparrows.

PubMed

Germain, Ryan R; Wolak, Matthew E; Arcese, Peter; Losdat, Sylvain; Reid, Jane M

2016-11-01

Quantifying direct and indirect genetic effects of interacting females and males on variation in jointly expressed life-history traits is central to predicting microevolutionary dynamics. However, accurately estimating sex-specific additive genetic variances in such traits remains difficult in wild populations, especially if related individuals inhabit similar fine-scale environments. Breeding date is a key life-history trait that responds to environmental phenology and mediates individual and population responses to environmental change. However, no studies have estimated female (direct) and male (indirect) additive genetic and inbreeding effects on breeding date, and estimated the cross-sex genetic correlation, while simultaneously accounting for fine-scale environmental effects of breeding locations, impeding prediction of microevolutionary dynamics. We fitted animal models to 38 years of song sparrow (Melospiza melodia) phenology and pedigree data to estimate sex-specific additive genetic variances in breeding date, and the cross-sex genetic correlation, thereby estimating the total additive genetic variance while simultaneously estimating sex-specific inbreeding depression. We further fitted three forms of spatial animal model to explicitly estimate variance in breeding date attributable to breeding location, overlap among breeding locations and spatial autocorrelation. We thereby quantified fine-scale location variances in breeding date and quantified the degree to which estimating such variances affected the estimated additive genetic variances. The non-spatial animal model estimated nonzero female and male additive genetic variances in breeding date (sex-specific heritabilities: 0·07 and 0·02, respectively) and a strong, positive cross-sex genetic correlation (0·99), creating substantial total additive genetic variance (0·18). Breeding date varied with female, but not male inbreeding coefficient, revealing direct, but not indirect, inbreeding depression. All three spatial animal models estimated small location variance in breeding date, but because relatedness and breeding location were virtually uncorrelated, modelling location variance did not alter the estimated additive genetic variances. Our results show that sex-specific additive genetic effects on breeding date can be strongly positively correlated, which would affect any predicted rates of microevolutionary change in response to sexually antagonistic or congruent selection. Further, we show that inbreeding effects on breeding date can also be sex specific and that genetic effects can exceed phenotypic variation stemming from fine-scale location-based variation within a wild population. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Scaling of swim speed and stroke frequency in geometrically similar penguins: they swim optimally to minimize cost of transport

PubMed Central

Sato, Katsufumi; Shiomi, Kozue; Watanabe, Yuuki; Watanuki, Yutaka; Takahashi, Akinori; Ponganis, Paul J.

2010-01-01

It has been predicted that geometrically similar animals would swim at the same speed with stroke frequency scaling with mass−1/3. In the present study, morphological and behavioural data obtained from free-ranging penguins (seven species) were compared. Morphological measurements support the geometrical similarity. However, cruising speeds of 1.8–2.3 m s−1 were significantly related to mass0.08 and stroke frequencies were proportional to mass−0.29. These scaling relationships do not agree with the previous predictions for geometrically similar animals. We propose a theoretical model, considering metabolic cost, work against mechanical forces (drag and buoyancy), pitch angle and dive depth. This new model predicts that: (i) the optimal swim speed, which minimizes the energy cost of transport, is proportional to (basal metabolic rate/drag)1/3 independent of buoyancy, pitch angle and dive depth; (ii) the optimal speed is related to mass0.05; and (iii) stroke frequency is proportional to mass−0.28. The observed scaling relationships of penguins support these predictions, which suggest that breath-hold divers swam optimally to minimize the cost of transport, including mechanical and metabolic energy during dive. PMID:19906666

Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

NASA Astrophysics Data System (ADS)

Chen, Huanhuan; Yao, Xin

Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.

Model-based learning and the contribution of the orbitofrontal cortex to the model-free world.

PubMed

McDannald, Michael A; Takahashi, Yuji K; Lopatina, Nina; Pietras, Brad W; Jones, Josh L; Schoenbaum, Geoffrey

2012-04-01

Learning is proposed to occur when there is a discrepancy between reward prediction and reward receipt. At least two separate systems are thought to exist: one in which predictions are proposed to be based on model-free or cached values; and another in which predictions are model-based. A basic neural circuit for model-free reinforcement learning has already been described. In the model-free circuit the ventral striatum (VS) is thought to supply a common-currency reward prediction to midbrain dopamine neurons that compute prediction errors and drive learning. In a model-based system, predictions can include more information about an expected reward, such as its sensory attributes or current, unique value. This detailed prediction allows for both behavioral flexibility and learning driven by changes in sensory features of rewards alone. Recent evidence from animal learning and human imaging suggests that, in addition to model-free information, the VS also signals model-based information. Further, there is evidence that the orbitofrontal cortex (OFC) signals model-based information. Here we review these data and suggest that the OFC provides model-based information to this traditional model-free circuitry and offer possibilities as to how this interaction might occur. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle.

PubMed

Eaglen, Sophie A E; Coffey, Mike P; Woolliams, John A; Wall, Eileen

2012-07-28

The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle. Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (-maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models. On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (-maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (-maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence. For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (-maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions.

Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle

PubMed Central

2012-01-01

Background The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle. Methods Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (−maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models. Results and discussion On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (−maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (−maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence. Conclusions For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (−maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions. PMID:22839757

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

NASA Astrophysics Data System (ADS)

Li, Lin; Yang, Yong; Wang, Hongbin; Dong, Jing; Zhao, Yujun; He, Jianbin; Fan, Honggang

Geographic information system (GIS) is a computer application system, which possesses the ability of manipulating spatial information and has been used in many fields related with the spatial information management. Many methods and models have been established for analyzing animal diseases distribution models and temporal-spatial transmission models. Great benefits have been gained from the application of GIS in animal disease epidemiology. GIS is now a very important tool in animal disease epidemiological research. Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. In this paper, we analyzed the cow anthrax spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow anthrax in this district in combination of spatial statistical analysis and GIS. The Cow anthrax is biogeochemical disease, and its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of anthrax cow for monitoring and the prevention and control of anthrax has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness. The GIS computer application software ArcGIS9.1 was used to overcome the lack of data of sampling sites.Using ArcGIS 9.1 and GEODA to analyze the cow anthrax spatial distribution of district A. we gained some conclusions about cow anthrax' density: (1) there is a spatial clustering model. (2) there is an intensely spatial autocorrelation. We established a prediction model to estimate the anthrax distribution based on the spatial characteristic of the density of cow anthrax. Comparing with the true distribution, the prediction model has a well coincidence and is feasible to the application. The method using a GIS tool facilitates can be implemented significantly in the cow anthrax monitoring and investigation, and the space statistics - related prediction model provides a fundamental use for other study on space-related animal diseases.

Theoretical considerations on maximum running speeds for large and small animals.

PubMed

Fuentes, Mauricio A

2016-02-07

Mechanical equations for fast running speeds are presented and analyzed. One of the equations and its associated model predict that animals tend to experience larger mechanical stresses in their limbs (muscles, tendons and bones) as a result of larger stride lengths, suggesting a structural restriction entailing the existence of an absolute maximum possible stride length. The consequence for big animals is that an increasingly larger body mass implies decreasing maximal speeds, given that the stride frequency generally decreases for increasingly larger animals. Another restriction, acting on small animals, is discussed only in preliminary terms, but it seems safe to assume from previous studies that for a given range of body masses of small animals, those which are bigger are faster. The difference between speed scaling trends for large and small animals implies the existence of a range of intermediate body masses corresponding to the fastest animals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

PubMed Central

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-01-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal–habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities. PMID:25558353

Computational Prediction of Alzheimer’s and Parkinson’s Disease MicroRNAs in Domestic Animals

PubMed Central

Wang, Hai Yang; Lin, Zi Li; Yu, Xian Feng; Bao, Yuan; Cui, Xiang-Shun; Kim, Nam-Hyung

2016-01-01

As the most common neurodegenerative diseases, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two of the main health concerns for the elderly population. Recently, microRNAs (miRNAs) have been used as biomarkers of infectious, genetic, and metabolic diseases in humans but they have not been well studied in domestic animals. Here we describe a computational biology study in which human AD- and PD-associated miRNAs (ADM and PDM) were utilized to predict orthologous miRNAs in the following domestic animal species: dog, cow, pig, horse, and chicken. In this study, a total of 121 and 70 published human ADM and PDM were identified, respectively. Thirty-seven miRNAs were co-regulated in AD and PD. We identified a total of 105 unrepeated human ADM and PDM that had at least one 100% identical animal homolog, among which 81 and 54 showed 100% sequence identity with 241 and 161 domestic animal miRNAs, respectively. Over 20% of the total mature horse miRNAs (92) showed perfect matches to AD/PD-associated miRNAs. Pigs, dogs, and cows have similar numbers of AD/PD-associated miRNAs (63, 62, and 59). Chickens had the least number of perfect matches (34). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses suggested that humans and dogs are relatively similar in the functional pathways of the five selected highly conserved miRNAs. Taken together, our study provides the first evidence for better understanding the miRNA-AD/PD associations in domestic animals, and provides guidance to generate domestic animal models of AD/PD to replace the current rodent models. PMID:26954182

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models.

PubMed

Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N

2014-12-01

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. © 2014 Blackwell Verlag GmbH.

Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.

PubMed

Aliloo, Hassan; Pryce, Jennie E; González-Recio, Oscar; Cocks, Benjamin G; Hayes, Ben J

2016-02-01

Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.

The interstitial stem cells in Hydractinia and their role in regeneration.

PubMed

Gahan, James M; Bradshaw, Brian; Flici, Hakima; Frank, Uri

2016-10-01

Hydractinia species have been animal models in developmental biology and comparative immunology for over a century, but are having a renaissance due to the establishment of modern genetic and genomic tools by the growing community of researchers utilizing them. Hydractinia has a predictable and accessible life cycle and its stem cell system, known as interstitial- or i-cells has been a paradigm for animal stem cells since the late 1800s. In adult Hydractinia, i-cells continuously provide progenitors to sustain clonal growth, tissue homeostasis, sexual reproduction and regeneration. We review recent developments in stem cell and regeneration research centered on this animal. Hydractinia joins an established team of cnidarian genetic models in times of rapid progress in these disciplines. While each animal is particularly suited to specific experimental settings, jointly they can provide an integrative insight into the diversity of animal stem cell systems, how they drive regeneration, and how they evolved. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies.

PubMed

Andrade, E L; Bento, A F; Cavalli, J; Oliveira, S K; Freitas, C S; Marcon, R; Schwanke, R C; Siqueira, J M; Calixto, J B

2016-10-24

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking.

PubMed

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.

The Cynomolgus Macaque Natural History Model of Pneumonic Tularemia for Predicting Clinical Efficacy Under the Animal Rule

PubMed Central

Guina, Tina; Lanning, Lynda L.; Omland, Kristian S.; Williams, Mark S.; Wolfraim, Larry A.; Heyse, Stephen P.; Houchens, Christopher R.; Sanz, Patrick; Hewitt, Judith A.

2018-01-01

Francisella tularensis is a highly infectious Gram-negative bacterium that is the etiologic agent of tularemia in animals and humans and a Tier 1 select agent. The natural incidence of pneumonic tularemia worldwide is very low; therefore, it is not feasible to conduct clinical efficacy testing of tularemia medical countermeasures (MCM) in human populations. Development and licensure of tularemia therapeutics and vaccines need to occur under the Food and Drug Administration's (FDA's) Animal Rule under which efficacy studies are conducted in well-characterized animal models that reflect the pathophysiology of human disease. The Tularemia Animal Model Qualification (AMQ) Working Group is seeking qualification of the cynomolgus macaque (Macaca fascicularis) model of pneumonic tularemia under Drug Development Tools Qualification Programs with the FDA based upon the results of studies described in this manuscript. Analysis of data on survival, average time to death, average time to fever onset, average interval between fever and death, and bacteremia; together with summaries of clinical signs, necropsy findings, and histopathology from the animals exposed to aerosolized F. tularensis Schu S4 in five natural history studies and one antibiotic efficacy study form the basis for the proposed cynomolgus macaque model. Results support the conclusion that signs of pneumonic tularemia in cynomolgus macaques exposed to 300–3,000 colony forming units (cfu) aerosolized F. tularensis Schu S4, under the conditions described herein, and human pneumonic tularemia cases are highly similar. Animal age, weight, and sex of animals challenged with 300–3,000 cfu Schu S4 did not impact fever onset in studies described herein. This study summarizes critical parameters and endpoints of a well-characterized cynomolgus macaque model of pneumonic tularemia and demonstrates this model is appropriate for qualification, and for testing efficacy of tularemia therapeutics under Animal Rule. PMID:29670861

Modeling Heat-Transfer in Animal Habitats in the Shuttle Orbiter Middeck

NASA Technical Reports Server (NTRS)

Eodice, Michael T.; Sun, Sid (Technical Monitor)

2000-01-01

A mathematical model has been developed to evaluate the heat transfer characteristics of an Animal Enclosure Module (AEM) in the microgravity environment. The AEM is a spaceflight habitat that provides life support for up to six rodents in the Space Shuttle Middeck. Currently, temperatures within the AEM are recorded in real time using a solid state data recorder; however, the data are only available for analysis post-flight. This temperature information is useful for characterizing the thermal environment of the AEM for researchers, but is unavailable during flight operations. Because animal health in microgravity is directly linked to the thermal environment, the ability to predict internal AEM temperatures is extremely useful to life science researchers. NASA flight crews typically carry hand-held temperature measurement devices which allow them to provide ground researchers with near real time readings of AEM inlet temperature; however, higher priority operations limit the frequency at which these measurements can be made and subsequently downlinked. The mathematical model developed allows users to predict internal cage volume temperatures based on knowledge of the ambient air temperature entering the AEM air intake ports. Additionally, an average convective heat transfer coefficient for the AEM has been determined to provide engineers with the requisite information to facilitate future design improvements and product upgrades. The model has been validated using empirical data from a series of three Space Shuttle missions.

Developing and applying habitat models using forest inventory data: an example using a terrestrial salamander

Treesearch

Hartwell H. Welsh Jr; Jeffrey R. Dunk; William J. Zielinski

2004-01-01

We provide a framework for developing predictive species habitat models using preexisting vegetation, physical, and spatial data in association with animal sampling data. The resulting models are used to evaluate questions relevant to species conservation, in particular, comparing occurrence estimates in reserved and unreserved lands. We used an information–theoretic...

Development of a process-based model to predict pathogen budgets for the Sydney drinking water catchment.

PubMed

Ferguson, Christobel M; Croke, Barry F W; Beatson, Peter J; Ashbolt, Nicholas J; Deere, Daniel A

2007-06-01

In drinking water catchments, reduction of pathogen loads delivered to reservoirs is an important priority for the management of raw source water quality. To assist with the evaluation of management options, a process-based mathematical model (pathogen catchment budgets - PCB) is developed to predict Cryptosporidium, Giardia and E. coli loads generated within and exported from drinking water catchments. The model quantifies the key processes affecting the generation and transport of microorganisms from humans and animals using land use and flow data, and catchment specific information including point sources such as sewage treatment plants and on-site systems. The resultant pathogen catchment budgets (PCB) can be used to prioritize the implementation of control measures for the reduction of pathogen risks to drinking water. The model is applied in the Wingecarribee catchment and used to rank those sub-catchments that would contribute the highest pathogen loads in dry weather, and in intermediate and large wet weather events. A sensitivity analysis of the model identifies that pathogen excretion rates from animals and humans, and manure mobilization rates are significant factors determining the output of the model and thus warrant further investigation.

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

NASA Technical Reports Server (NTRS)

Williams, J. R.; Zhang, Y.; Zhou, H.; Osman, M.; Cha, D.; Kavet, R.; Cuccinotta, F.; Dicello, J. F.; Dillehay, L. E.

1999-01-01

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.

Predictive modeling of developmental toxicity using EPA’s Virtual Embryo

EPA Science Inventory

Standard practice in prenatal developmental toxicology involves testing chemicals in pregnant laboratory animals of two species, typically rats and rabbits, exposed during organogenesis and evaluating for fetal growth retardation, structural malformations, and prenatal death just...

A new tool for estimating phosphorus loss from cattle barnyards and outdoor lots

USDA-ARS?s Scientific Manuscript database

Phosphorus (P) loss from agriculture can compromise quality of receiving water bodies. For cattle farms, P can be lost from cropland, pastures, and outdoor animal lots. We developed a new model that predicts annual runoff, total solids loss, and total and dissolved P loss from cattle lots. The model...

Feed-forward and generalized regression neural networks in modeling feeding behavior of pigs in the grow-finishing phase

USDA-ARS?s Scientific Manuscript database

Feeding patterns in group-housed grow-finishing pigs have been investigated for use in management decisions, identifying sick animals, and determining genetic differences within a herd. Development of models to predict swine feeding behaviour has been limited due the large number of potential enviro...

The devil is in the dispersers: Predictions of landscape connectivity change with demography

Treesearch

Nicholas B. Elliot; Samuel A. Cushman; David W. Macdonald; Andrew J. Loveridge

2014-01-01

Concern about the effects of habitat fragmentation has led to increasing interest in dispersal and connectivity modelling. Most modern techniques for connectivity modelling have resistance surfaces as their foundation. However, resistance surfaces for animal movement are frequently estimated without considering dispersal, despite being the principal natural mechanism...

Satellite imaging and vector-borne diseases: the approach of the French National Space Agency (CNES).

PubMed

Marechal, Fabienne; Ribeiro, Nathalie; Lafaye, Murielle; Güell, Antonio

2008-11-01

Tele-epidemiology consists in studying human and animal epidemic, the spread of which is closely tied to environmental factors, using data from earth-orbiting satellites. By combining various data originated from satellites such as SPOT (vegetation indexes), Meteosat (winds and cloud masses) and other Earth observation data from Topex/Poseidon and Envisat (wave height, ocean temperature and colour) with hydrology data (number and distribution of lakes, water levels in rivers and reservoirs) and clinical data from humans and animals (clinical cases and serum use), predictive mathematical models can be constructed. A number of such approaches have been tested in the last three years. In Senegal, for example, Rift Valley fever epidemics are being monitored using a predictive model based on the rate at which water holes dry out after the rainy season, which affects the number of mosquito eggs which carry the virus.

Interhost dispersal alters microbiome assembly and can overwhelm host innate immunity in an experimental zebrafish model.

PubMed

Burns, Adam R; Miller, Elizabeth; Agarwal, Meghna; Rolig, Annah S; Milligan-Myhre, Kathryn; Seredick, Steve; Guillemin, Karen; Bohannan, Brendan J M

2017-10-17

The diverse collections of microorganisms associated with humans and other animals, collectively referred to as their "microbiome," are critical for host health, but the mechanisms that govern their assembly are poorly understood. This has made it difficult to identify consistent host factors that explain variation in microbiomes across hosts, despite large-scale sampling efforts. While ecological theory predicts that the movement, or dispersal, of individuals can have profound and predictable consequences on community assembly, its role in the assembly of animal-associated microbiomes remains underexplored. Here, we show that dispersal of microorganisms among hosts can contribute substantially to microbiome variation, and is able to overwhelm the effects of individual host factors, in an experimental test of ecological theory. We manipulated dispersal among wild-type and immune-deficient myd88 knockout zebrafish and observed that interhost dispersal had a large effect on the diversity and composition of intestinal microbiomes. Interhost dispersal was strong enough to overwhelm the effects of host factors, largely eliminating differences between wild-type and immune-deficient hosts, regardless of whether dispersal occurred within or between genotypes, suggesting dispersal can independently alter the ecology of microbiomes. Our observations are consistent with a predictive model that assumes metacommunity dynamics and are likely mediated by dispersal-related microbial traits. These results illustrate the importance of microbial dispersal to animal microbiomes and motivate its integration into the study of host-microbe systems.

Tissue Engineering in Animal Models for Urinary Diversion: A Systematic Review

PubMed Central

Sloff, Marije; de Vries, Rob; Geutjes, Paul; IntHout, Joanna; Ritskes-Hoitinga, Merel

2014-01-01

Tissue engineering and regenerative medicine (TERM) approaches may provide alternatives for gastrointestinal tissue in urinary diversion. To continue to clinically translatable studies, TERM alternatives need to be evaluated in (large) controlled and standardized animal studies. Here, we investigated all evidence for the efficacy of tissue engineered constructs in animal models for urinary diversion. Studies investigating this subject were identified through a systematic search of three different databases (PubMed, Embase and Web of Science). From each study, animal characteristics, study characteristics and experimental outcomes for meta-analyses were tabulated. Furthermore, the reporting of items vital for study replication was assessed. The retrieved studies (8 in total) showed extreme heterogeneity in study design, including animal models, biomaterials and type of urinary diversion. All studies were feasibility studies, indicating the novelty of this field. None of the studies included appropriate control groups, i.e. a comparison with the classical treatment using GI tissue. The meta-analysis showed a trend towards successful experimentation in larger animals although no specific animal species could be identified as the most suitable model. Larger animals appear to allow a better translation to the human situation, with respect to anatomy and surgical approaches. It was unclear whether the use of cells benefits the formation of a neo urinary conduit. The reporting of the methodology and data according to standardized guidelines was insufficient and should be improved to increase the value of such publications. In conclusion, animal models in the field of TERM for urinary diversion have probably been chosen for reasons other than their predictive value. Controlled and comparative long term animal studies, with adequate methodological reporting are needed to proceed to clinical translatable studies. This will aid in good quality research with the reduction in the use of animals and an increase in empirical evidence of biomedical research. PMID:24964011

Food-Web Models Predict Species Abundances in Response to Habitat Change

PubMed Central

Gotelli, Nicholas J; Ellison, Aaron M

2006-01-01

Plant and animal population sizes inevitably change following habitat loss, but the mechanisms underlying these changes are poorly understood. We experimentally altered habitat volume and eliminated top trophic levels of the food web of invertebrates that inhabit rain-filled leaves of the carnivorous pitcher plant Sarracenia purpurea. Path models that incorporated food-web structure better predicted population sizes of food-web constituents than did simple keystone species models, models that included only autecological responses to habitat volume, or models including both food-web structure and habitat volume. These results provide the first experimental confirmation that trophic structure can determine species abundances in the face of habitat loss. PMID:17002518

Non-animal Replacements for Acute Toxicity Testing.

PubMed

Barker-Treasure, Carol; Coll, Kevin; Belot, Nathalie; Longmore, Chris; Bygrave, Karl; Avey, Suzanne; Clothier, Richard

2015-07-01

Current approaches to predicting adverse effects in humans from acute toxic exposure to cosmetic ingredients still heavily necessitate the use of animals under EU legislation, particularly in the context of the REACH system, when cosmetic ingredients are also destined for use in other industries. These include the LD50 test, the Up-and-Down Procedure and the Fixed Dose Procedure, which are regarded as having notable scientific deficiencies and low transferability to humans. By expanding on previous in vitro tests, such as the animal cell-based 3T3 Neutral Red Uptake (NRU) assay, this project aims to develop a truly animal-free predictive test for the acute toxicity of cosmetic ingredients in humans, by using human-derived cells and a prediction model that does not rely on animal data. The project, funded by Innovate UK, will incorporate the NRU assay with human dermal fibroblasts in animal product-free culture, to generate an in vitro protocol that can be validated as an accepted replacement for the currently available in vivo tests. To date, the project has successfully completed an assessment of the robustness and reproducibility of the method, by using sodium lauryl sulphate (SLS) as a positive control, and displaying analogous results to those of the original studies with mouse 3T3 cells. Currently, the testing of five known ingredients from key groups (a surfactant, a preservative, a fragrance, a colour and an emulsifier) is under way. The testing consists of initial range-finding runs followed by three valid runs of a main experiment with the appropriate concentration ranges, to generate IC50 values. Expanded blind trials of 20 ingredients will follow. Early results indicate that this human cell-based test holds the potential to replace aspects of in vivo animal acute toxicity testing, particularly with reference to cosmetic ingredients. 2015 FRAME.

Modelling the Effect of Diet Composition on Enteric Methane Emissions across Sheep, Beef Cattle and Dairy Cows

PubMed Central

Bell, Matt; Eckard, Richard; Moate, Peter J.; Yan, Tianhai

2016-01-01

Simple Summary Enteric methane emissions produced by ruminant livestock has gained global interest due to methane being a potent greenhouse gas and ruminants being a significant source of emissions. In the absence of measurements, prediction models can facilitate the estimation of enteric methane emissions from ruminant livestock and aid investigation of mitigation options. This study developed a practical method using feed analysis information for predicting enteric methane emissions from sheep, beef cattle and dairy cows fed diets encompassing a wide range of nutrient concentrations. Abstract Enteric methane (CH4) is a by-product from fermentation of feed consumed by ruminants, which represents a nutritional loss and is also considered a contributor to climate change. The aim of this research was to use individual animal data from 17 published experiments that included sheep (n = 288), beef cattle (n = 71) and dairy cows (n = 284) to develop an empirical model to describe enteric CH4 emissions from both cattle and sheep, and then evaluate the model alongside equations from the literature. Data were obtained from studies in the United Kingdom (UK) and Australia, which measured enteric CH4 emissions from individual animals in calorimeters. Animals were either fed solely forage or a mixed ration of forage with a compound feed. The feed intake of sheep was restricted to a maintenance amount of 875 g of DM per day (maintenance level), whereas beef cattle and dairy cows were fed to meet their metabolizable energy (ME) requirement (i.e., production level). A linear mixed model approach was used to develop a multiple linear regression model to predict an individual animal’s CH4 yield (g CH4/kg dry matter intake) from the composition of its diet. The diet components that had significant effects on CH4 yield were digestible organic matter (DOMD), ether extract (EE) (both g/kg DM) and feeding level above maintenance intake: CH4 (g/kg DM intake) = 0.046 (±0.001) × DOMD − 0.113 (±0.023) × EE − 2.47 (±0.29) × (feeding level − 1), with concordance correlation coefficient (CCC) = 0.655 and RMSPE = 14.0%. The predictive ability of the model developed was as reliable as other models assessed from the literature. These components can be used to predict effects of diet composition on enteric CH4 yield from sheep, beef and dairy cattle from feed analysis information. PMID:27618107

Translational approaches to obsessive-compulsive disorder: from animal models to clinical treatment

PubMed Central

Fineberg, NA; Chamberlain, SR; Hollander, E; Boulougouris, V; Robbins, TW

2011-01-01

Obsessive-compulsive disorder (OCD) is characterized by obsessions (intrusive thoughts) and compulsions (repetitive ritualistic behaviours) leading to functional impairment. Accumulating evidence links these conditions with underlying dysregulation of fronto-striatal circuitry and monoamine systems. These abnormalities represent key targets for existing and novel treatment interventions. However, the brain bases of these conditions and treatment mechanisms are still not fully elucidated. Animal models simulating the behavioural and clinical manifestations of the disorder show great potential for augmenting our understanding of the pathophysiology and treatment of OCD. This paper provides an overview of what is known about OCD from several perspectives. We begin by describing the clinical features of OCD and the criteria used to assess the validity of animal models of symptomatology; namely, face validity (phenomenological similarity between inducing conditions and specific symptoms of the human phenomenon), predictive validity (similarity in response to treatment) and construct validity (similarity in underlying physiological or psychological mechanisms). We then survey animal models of OC spectrum conditions within this framework, focusing on (i) ethological models; (ii) genetic and pharmacological models; and (iii) neurobehavioural models. We also discuss their advantages and shortcomings in relation to their capacity to identify potentially efficacious new compounds. It is of interest that there has been rather little evidence of ‘false alarms’ for therapeutic drug effects in OCD models which actually fail in the clinic. While it is more difficult to model obsessive cognition than compulsive behaviour in experimental animals, it is feasible to infer cognitive inflexibility in certain animal paradigms. Finally, key future neurobiological and treatment research areas are highlighted. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21486280

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

PubMed Central

Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian

2018-01-01

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908

Modeling behavioral thermoregulation in a climate change sentinel.

PubMed

Moyer-Horner, Lucas; Mathewson, Paul D; Jones, Gavin M; Kearney, Michael R; Porter, Warren P

2015-12-01

When possible, many species will shift in elevation or latitude in response to rising temperatures. However, before such shifts occur, individuals will first tolerate environmental change and then modify their behavior to maintain heat balance. Behavioral thermoregulation allows animals a range of climatic tolerances and makes predicting geographic responses under future warming scenarios challenging. Because behavioral modification may reduce an individual's fecundity by, for example, limiting foraging time and thus caloric intake, we must consider the range of behavioral options available for thermoregulation to accurately predict climate change impacts on individual species. To date, few studies have identified mechanistic links between an organism's daily activities and the need to thermoregulate. We used a biophysical model, Niche Mapper, to mechanistically model microclimate conditions and thermoregulatory behavior for a temperature-sensitive mammal, the American pika (Ochotona princeps). Niche Mapper accurately simulated microclimate conditions, as well as empirical metabolic chamber data for a range of fur properties, animal sizes, and environmental parameters. Niche Mapper predicted pikas would be behaviorally constrained because of the need to thermoregulate during the hottest times of the day. We also showed that pikas at low elevations could receive energetic benefits by being smaller in size and maintaining summer pelage during longer stretches of the active season under a future warming scenario. We observed pika behavior for 288 h in Glacier National Park, Montana, and thermally characterized their rocky, montane environment. We found that pikas were most active when temperatures were cooler, and at sites characterized by high elevations and north-facing slopes. Pikas became significantly less active across a suite of behaviors in the field when temperatures surpassed 20°C, which supported a metabolic threshold predicted by Niche Mapper. In general, mechanistic predictions and empirical observations were congruent. This research is unique in providing both an empirical and mechanistic description of the effects of temperature on a mammalian sentinel of climate change, the American pika. Our results suggest that previously underinvestigated characteristics, specifically fur properties and body size, may play critical roles in pika populations' response to climate change. We also demonstrate the potential importance of considering behavioral thermoregulation and microclimate variability when predicting animal responses to climate change.

Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics

PubMed Central

Zhang, Liping; Wang, Li; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2017-01-01

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)4 model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends. PMID:28273856

Tumor immunology viewed from alternative animal models—the Xenopus story

PubMed Central

Banach, Maureen; Robert, Jacques

2017-01-01

a) Purpose of review Nonmammalian comparative animal models are important not only to gain fundamental evolutionary understanding of the complex interactions of tumors with the immune system, but also to better predict the applicability of novel immunotherapeutic approaches to humans. After reviewing recent advances in developing alternative models, we focus on the amphibian Xenopus laevis and its usefulness in deciphering the perplexing roles of MHC class I-like molecules and innate (i)T cells in tumor immunity. b) Recent findings Experiments using MHC-defined inbred and cloned animals, tumor cell lines, effective reagents, sequenced genomes, and adapted gene editing techniques in Xenopus, have revealed that the critical involvement of class I-like molecules and iT cells in tumor immunity has been conserved during evolution. c) Summary Comparative studies with the X. laevis tumor immunity model can contribute to the development of better and more efficient cancer immunotherapies. PMID:28944105

Long-range Acoustic Interactions in Insect Swarms - An Adaptive Gravity Model

NASA Astrophysics Data System (ADS)

Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. We consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges' acoustic sensing, we show that our ``adaptive gravity'' model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions open a new class of equations of motion, which may appear in other biological contexts.

Integrated Decision Strategies for Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Kleinstreuer, Nicole; Paris, Michael; Lehmann, David M.; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Lowit, Anna; Allen, David; Casey, Warren

2016-01-01

One of the top priorities of ICCVAM is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by OECD. Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico, and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens assay. Data for six physicochemical properties that may affect skin penetration were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89–96% for the test set and 96–99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. PMID:26851134

Modeling the ecologic niche of plague in sylvan and domestic animal hosts to delineate sources of human exposure in the western United States

PubMed Central

Haseeb, MA

2015-01-01

Plague has been established in the western United States (US) since 1900 following the West Coast introduction of commensal rodents infected with Yersinia pestis via early industrial shipping. Over the last century, plague ecology has transitioned through cycles of widespread human transmission, urban domestic transmission among commensal rodents, and ultimately settled into the predominantly sylvan foci that remain today where it is maintained alternatively by enzootic and epizootic transmission. While zoonotic transmission to humans is much less common in modern times, significant plague risk remains in parts of the western US. Moreover, risk to some threatened species that are part of the epizootic cycle can be quite substantive. This investigation attempted to predict the risk of plague across the western US by modeling the ecologic niche of plague in sylvan and domestic animals identified between 2000 and 2015. A Maxent machine learning algorithm was used to predict this niche based on climate, altitude, land cover, and the presence of an important enzootic species, Peromyscus maniculatus. This model demonstrated good predictive ability (AUC = 86%) and identified areas of high risk in central Colorado, north-central New Mexico, and southwestern and northeastern California. The presence of P. maniculatus, altitude, precipitation during the driest and wettest quarters, and distance to artificial surfaces, all contributed substantively to maximizing the gain function. These findings add to the known landscape epidemiology and infection ecology of plague in the western US and may suggest locations of particular risk to be targeted for wild and domestic animal intervention. PMID:26713244

Modeling the ecologic niche of plague in sylvan and domestic animal hosts to delineate sources of human exposure in the western United States.

PubMed

Walsh, Michael; Haseeb, M A

2015-01-01

Plague has been established in the western United States (US) since 1900 following the West Coast introduction of commensal rodents infected with Yersinia pestis via early industrial shipping. Over the last century, plague ecology has transitioned through cycles of widespread human transmission, urban domestic transmission among commensal rodents, and ultimately settled into the predominantly sylvan foci that remain today where it is maintained alternatively by enzootic and epizootic transmission. While zoonotic transmission to humans is much less common in modern times, significant plague risk remains in parts of the western US. Moreover, risk to some threatened species that are part of the epizootic cycle can be quite substantive. This investigation attempted to predict the risk of plague across the western US by modeling the ecologic niche of plague in sylvan and domestic animals identified between 2000 and 2015. A Maxent machine learning algorithm was used to predict this niche based on climate, altitude, land cover, and the presence of an important enzootic species, Peromyscus maniculatus. This model demonstrated good predictive ability (AUC = 86%) and identified areas of high risk in central Colorado, north-central New Mexico, and southwestern and northeastern California. The presence of P. maniculatus, altitude, precipitation during the driest and wettest quarters, and distance to artificial surfaces, all contributed substantively to maximizing the gain function. These findings add to the known landscape epidemiology and infection ecology of plague in the western US and may suggest locations of particular risk to be targeted for wild and domestic animal intervention.

Antibiotics in Animal Products

NASA Astrophysics Data System (ADS)

Falcão, Amílcar C.

The administration of antibiotics to animals to prevent or treat diseases led us to be concerned about the impact of these antibiotics on human health. In fact, animal products could be a potential vehicle to transfer drugs to humans. Using appropri ated mathematical and statistical models, one can predict the kinetic profile of drugs and their metabolites and, consequently, develop preventive procedures regarding drug transmission (i.e., determination of appropriate withdrawal periods). Nevertheless, in the present chapter the mathematical and statistical concepts for data interpretation are strictly given to allow understanding of some basic pharma-cokinetic principles and to illustrate the determination of withdrawal periods

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335

Rainfall-runoff model for prediction of waterborne viral contamination in a small river catchment

NASA Astrophysics Data System (ADS)

Gelati, E.; Dommar, C.; Lowe, R.; Polcher, J.; Rodó, X.

2013-12-01

We present a lumped rainfall-runoff model aimed at providing useful information for the prediction of waterborne viral contamination in small rivers. Viral contamination of water bodies may occur because of the discharge of sewage effluents and of surface runoff over areas affected by animal waste loads. Surface runoff is caused by precipitation that cannot infiltrate due to its intensity and to antecedent soil water content. It may transport animal feces to adjacent water bodies and cause viral contamination. We model streamflow by separating it into two components: subsurface flow, which is produced by infiltrated precipitation; and surface runoff. The model estimates infiltrated and non-infiltrated precipitation and uses impulse-response functions to compute the corresponding fractions of streamflow. The developed methodologies are applied to the Glafkos river, whose catchment extends for 102 km2 and includes the city of Patra. Streamflow and precipitation observations are available at a daily time resolution. Waterborne virus concentration measurements were performed approximately every second week from the beginning of 2011 to mid 2012. Samples were taken at several locations: in river water upstream of Patras and in the urban area; in sea water at the river outlet and approximately 2 km south-west of Patras; in sewage effluents before and after treatment. The rainfall-runoff model was calibrated and validated using observed streamflow and precipitation data. The model contribution to waterborne viral contamination prediction was benchmarked by analyzing the virus concentration measurements together with the estimated surface runoff values. The presented methodology may be a first step towards the development of waterborne viral contamination alert systems. Predicting viral contamination of water bodies would benefit sectors such as water supply and tourism.

Evaluation of non-animal methods for assessing skin sensitisation hazard: A Bayesian Value-of-Information analysis.

PubMed

Leontaridou, Maria; Gabbert, Silke; Van Ierland, Ekko C; Worth, Andrew P; Landsiedel, Robert

2016-07-01

This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP). 2016 FRAME.

Where is the skepticism in animal metacognition?

PubMed Central

Crystal, Jonathon D.

2015-01-01

The comparative analysis of metacognition may answer fundamental questions about the evolution of cognition. Although a substantial amount of research has been directed toward this goal in the last two decades, the recent development of quantitative non-metacognition models has raised questions about the existence of metacognition in nonhumans. Kornell (2013) proposes that advances in animal metacognition may be made by following emerging trends in human metacognition research, namely that animal metacognition may take the form of drawing inferences from metacognitive cues without directly assessing the strength of memories directly. A problem with this approach is noted. Because the metacognitive status of certainty judgments in animals is at the center of the dispute in the field, demonstrations of the inferential view would not provide evidence that putative metacognitive cues are indeed based on metacognition. I argue that any preparation that claims to tap into metacognition needs to be tested against leading non-metacognition hypotheses such as Le Pelley's (2012) reinforcement-learning model. Progress in animal metacognition will come from the development of new assessment techniques that offer predictions contrary to non-metacognition hypotheses. Animal metacognition will advance by applying skepticism about methods and interpretation while letting the animals (and their data) settle the debate. PMID:24866006

Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals.

PubMed

Fernando, Rohan L; Cheng, Hao; Golden, Bruce L; Garrick, Dorian J

2016-12-08

Two types of models have been used for single-step genomic prediction and genome-wide association studies that include phenotypes from both genotyped animals and their non-genotyped relatives. The two types are breeding value models (BVM) that fit breeding values explicitly and marker effects models (MEM) that express the breeding values in terms of the effects of observed or imputed genotypes. MEM can accommodate a wider class of analyses, including variable selection or mixture model analyses. The order of the equations that need to be solved and the inverses required in their construction vary widely, and thus the computational effort required depends upon the size of the pedigree, the number of genotyped animals and the number of loci. We present computational strategies to avoid storing large, dense blocks of the MME that involve imputed genotypes. Furthermore, we present a hybrid model that fits a MEM for animals with observed genotypes and a BVM for those without genotypes. The hybrid model is computationally attractive for pedigree files containing millions of animals with a large proportion of those being genotyped. We demonstrate the practicality on both the original MEM and the hybrid model using real data with 6,179,960 animals in the pedigree with 4,934,101 phenotypes and 31,453 animals genotyped at 40,214 informative loci. To complete a single-trait analysis on a desk-top computer with four graphics cards required about 3 h using the hybrid model to obtain both preconditioned conjugate gradient solutions and 42,000 Markov chain Monte-Carlo (MCMC) samples of breeding values, which allowed making inferences from posterior means, variances and covariances. The MCMC sampling required one quarter of the effort when the hybrid model was used compared to the published MEM. We present a hybrid model that fits a MEM for animals with genotypes and a BVM for those without genotypes. Its practicality and considerable reduction in computing effort was demonstrated. This model can readily be extended to accommodate multiple traits, multiple breeds, maternal effects, and additional random effects such as polygenic residual effects.

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grant, Claire, E-mail: claire.grant@astrazeneca.com; Ewart, Lorna; Muthas, Daniel

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility ofmore » integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive “pica” behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. - Highlights: • Integrated pre-clinical data can be used to predict clinical nausea and vomiting. • Data integrated from standard toxicology studies is sufficient to make a prediction. • The use of the nausea algorithm developed by Parkinson (2012) aids the prediction. • Additional pre-clinical studies can be used to confirm and quantify the risk.« less

Systematized water content calculation in cartilage using T1-mapping MR estimations: design and validation of a mathematical model.

PubMed

Shiguetomi-Medina, J M; Ramirez-Gl, J L; Stødkilde-Jørgensen, H; Møller-Madsen, B

2017-09-01

Up to 80 % of cartilage is water; the rest is collagen fibers and proteoglycans. Magnetic resonance (MR) T1-weighted measurements can be employed to calculate the water content of a tissue using T1 mapping. In this study, a method that translates T1 values into water content data was tested statistically. To develop a predictive equation, T1 values were obtained for tissue-mimicking gelatin samples. 1.5 T MRI was performed using inverse angle phase and an inverse sequence at 37 (±0.5) °C. Regions of interest were manually delineated and the mean T1 value was estimated in arbitrary units. Data were collected and modeled using linear regression. To validate the method, articular cartilage from six healthy pigs was used. The experiment was conducted in accordance with the Danish Animal Experiment Committee. Double measurements were performed for each animal. Ex vivo, all water in the tissue was extracted by lyophilization, thus allowing the volume of water to be measured. This was then compared with the predicted water content via Lin's concordance correlation coefficient at the 95 % confidence level. The mathematical model was highly significant when compared to a null model (p < 0.0001). 97.3 % of the variation in water content can be explained by absolute T1 values. Percentage water content could be predicted as 0.476 + (T1 value) × 0.000193 × 100 %. We found that there was 98 % concordance between the actual and predicted water contents. The results of this study demonstrate that MR data can be used to predict percentage water contents of cartilage samples. 3 (case-control study).

Predictive factors for the regression of canine transmissible venereal tumor during vincristine therapy.

PubMed

Scarpelli, Karime C; Valladão, Maria L; Metze, Konradin

2010-03-01

Canine transmissible venereal tumor (CTVT) is a neoplasm transmitted by transplantation. Monochemotherapy with vincristine is considered to be effective, but treatment time until complete clinical remission may vary. The aim of this study was to determine which clinical data at diagnosis could predict the responsiveness of CTVT to vincristine chemotherapy. One hundred dogs with CTVT entered this prospective study. The animals were treated with vincristine sulfate (0.025 mg/kg) at weekly intervals until the tumor had macroscopically disappeared. The time to complete remission was recorded. A multivariate Cox regression model indicated that larger tumor mass, increased age and therapy during hot and rainy months were independent significant unfavorable predictive factors retarding remission, whereas sex, weight, status as owned dog or breed were of no predictive relevance. Further studies are necessary to investigate whether these results are due to changes in immunological response mechanisms in animals with a diminished immune surveillance, resulting in delays in tumor regression. 2008 Elsevier Ltd. All rights reserved.

Zoning the territory of the Republic of Kazakhstan as to the risk of rabies among various categories of animals.

PubMed

Abdrakhmanov, Sarsenbay K; Sultanov, Akhmetzhan A; Beisembayev, Kanatzhan K; Korennoy, Fedor I; Кushubaev, Dosym B; Каdyrov, Ablaikhan S

2016-05-31

This paper presents the zoning of the territory of the Republic of Kazakhstan with respect to the risk of rabies outbreaks in domestic and wild animals considering environmental and climatic conditions. The national database of rabies outbreaks in Kazakhstan in the period 2003-2014 has been accessed in order to find which zones are consistently most exposed to the risk of rabies in animals. The database contains information on the cases in demes of farm livestock, domestic animals and wild animals. To identify the areas with the highest risk of outbreaks, we applied the maximum entropy modelling method. Designated outbreaks were used as input presence data, while the bioclim set of ecological and climatic variables, together with some geographic factors, were used as explanatory variables. The model demonstrated a high predictive ability. The area under the curve for farm livestock was 0.782, for domestic animals -0.859 and for wild animals - 0.809. Based on the model, the map of integral risk was designed by following four categories: negligible risk (disease-free or favourable zone), low risk (surveillance zone), medium risk (vaccination zone), and high risk (unfavourable zone). The map was produced to allow developing a set of preventive measures and is expected to contribute to a better distribution of supervisory efforts from the veterinary service of the country.

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

Estimation of sex-specific survival from capture-recapture data when sex is not always known

USGS Publications Warehouse

Nichols, J.D.; Kendall, W.L.; Hines, J.E.; Spendelow, J.A.

2004-01-01

Many animals lack obvious sexual dimorphism, making assignment of sex difficult even for observed or captured animals. For many such species it is possible to assign sex with certainty only at some occasions; for example, when they exhibit certain types of behavior. A common approach to handling this situation in capture-recapture studies has been to group capture histories into those of animals eventually identified as male and female and those for which sex was never known. Because group membership is dependent on the number of occasions at which an animal was caught or observed (known sex animals, on average, will have been observed at more occasions than unknown-sex animals), survival estimates for known-sex animals will be positively biased, and those for unknown animals will be negatively biased. In this paper, we develop capture-recapture models that incorporate sex ratio and sex assignment parameters that permit unbiased estimation in the face of this sampling problem. We demonstrate the magnitude of bias in the traditional capture-recapture approach to this sampling problem, and we explore properties of estimators from other ad hoc approaches. The model is then applied to capture-recapture data for adult Roseate Terns (Sterna dougallii) at Falkner Island, Connecticut, 1993-2002. Sex ratio among adults in this population favors females, and we tested the hypothesis that this population showed sex-specific differences in adult survival. Evidence was provided for higher survival of adult females than males, as predicted. We recommend use of this modeling approach for future capture-recapture studies in which sex cannot always be assigned to captured or observed animals. We also place this problem in the more general context of uncertainty in state classification in multistate capture-recapture models.

[Animal experimentation in the discovery and production of veterinary vaccines].

PubMed

Audonnet, J Ch; Lechenet, J; Verschuere, B

2007-08-01

Veterinary vaccine research, development and production facilities must aim to improve animal welfare, respond to public concerns and meet regulatory requirements, while at the same time fulfilling their objective of producing evermore effective and safer vaccines. The use of animal experimentation for the development of new veterinary vaccines is inevitable, as no in vitro model can predict a candidate vaccine's ability to induce protection in the target species. Against the backdrop of ethical and regulatory constraints, constant progress is being made in creating the best possible conditions for animal experimentation. Keeping up to date with the constant changes in the field of animal ethics requires a particular effort on the part of the pharmaceutical industry, which must make careful changes to product registration documentation in accordance with each new development.

Treatment with escitalopram but not desipramine decreases escape latency times in a learned helplessness model using juvenile rats.

PubMed

Reed, Abbey L; Anderson, Jeffrey C; Bylund, David B; Petty, Frederick; El Refaey, Hesham; Happe, H Kevin

2009-08-01

The pharmacological treatment of depression in children and adolescents is different from that of adults due to the lack of efficacy of certain antidepressants in the pediatric age group. Our current understanding of why these differences occur is very limited. To develop more effective treatments, a juvenile animal model of depression was tested to validate it as a possible model to specifically study pediatric depression. Procedures for use with juvenile rats at postnatal day (PND) 21 and 28 were adapted from the adult learned helplessness model in which, 24 h after exposure to inescapable stress, animals are unable to remove themselves from an easily escapable stressor. Rats were treated for 7 days with either the selective serotonin reuptake inhibitor escitalopram at 10 mg/kg or the tricyclic antidepressant desipramine at 3, 10, or 15 mg/kg to determine if treatment could decrease escape latency times. Escitalopram treatment was effective at decreasing escape latency times in all ages tested. Desipramine treatment did not decrease escape latency times for PND 21 rats, but did decrease times for PND 28 and adult animals. The learned helplessness model with PND 21 rats predicts the efficacy of escitalopram and the lack of efficacy of desipramine seen in the treatment of pediatric depression. These findings suggest that the use of PND 21 rats in a modified learned helplessness procedure may be a valuable model of human pediatric depression that can predict pediatric antidepressant efficacy and be used to study antidepressant mechanisms involved in pediatric depression.

Laterality defects are influenced by timing of treatments and animal model.

PubMed

Vandenberg, Laura N

2012-01-01

The timing of when the embryonic left-right (LR) axis is first established and the mechanisms driving this process are subjects of strong debate. While groups have focused on the role of cilia in establishing the LR axis during gastrula and neurula stages, many animals appear to orient the LR axis prior to the appearance of, or without the benefit of, motile cilia. Because of the large amount of data available in the published literature and the similarities in the type of data collected across laboratories, I have examined relationships between the studies that do and do not implicate cilia, the choice of animal model, the kinds of LR patterning defects observed, and the penetrance of LR phenotypes. I found that treatments affecting cilia structure and motility had a higher penetrance for both altered gene expression and improper organ placement compared to treatments that affect processes in early cleavage stage embryos. I also found differences in penetrance that could be attributed to the animal models used; the mouse is highly prone to LR randomization. Additionally, the data were examined to address whether gene expression can be used to predict randomized organ placement. Using regression analysis, gene expression was found to be predictive of organ placement in frogs, but much less so in the other animals examined. Together, these results challenge previous ideas about the conservation of LR mechanisms, with the mouse model being significantly different from fish, frogs, and chick in almost every aspect examined. Additionally, this analysis indicates that there may be missing pieces in the molecular pathways that dictate how genetic information becomes organ positional information in vertebrates; these gaps will be important for future studies to identify, as LR asymmetry is not only a fundamentally fascinating aspect of development but also of considerable biomedical importance. Copyright © 2011 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.

Benefits of the destinations, not costs of the journeys, shape partial migration patterns

USGS Publications Warehouse

Yackulic, Charles B.; Blake, Stephen; Bastille-Rousseau, Guillaume

2017-01-01

1. The reasons that lead some animals to seasonally migrate, and others to remain in the same area year-round, are poorly understood. Associations between traits, such as body size, and migration provide clues. For example, larger species and individuals are more likely to migrate.2. One explanation for this size bias in migration is that larger animals are capable of moving faster (movement hypothesis). However, body size is linked to many other biological processes. For instance, the energetic balances of larger animals are generally more sensitive to variation in food density because of body size effects on foraging and metabolism and this sensitivity could drive migratory decisions (forage hypothesis).3. Identifying the primary selective forces that drive migration ultimately requires quantifying fitness impacts over the full annual migratory cycle. Here, we develop a full annual migratory cycle model from metabolic and foraging theory to compare the importance of the forage and movement hypotheses. We parameterize the model for Galapagos tortoises, which were recently discovered to be size-dependent altitudinal migrants.4. The model predicts phenomena not included in model development including maximum body sizes, the body size at which individuals begin to migrate, and the seasonal timing of migration and these predictions generally agree with available data. Scenarios strongly support the forage hypothesis over the movement hypothesis. Furthermore, male Galapagos tortoises on Santa Cruz Island would be unable to grow to their enormous sizes without access to both highlands and lowlands.5. Whereas recent research has focused on links between traits and the migratory phases of the migratory cycle, we find that effects of body size on the non-migratory phases are far more important determinants of the propensity to migrate. Larger animals are more sensitive to changing forage conditions than smaller animals with implications for maintenance of migration and body size in the face of environmental change.

The importance of individual variation in the dynamics of animal collective movements.

PubMed

Del Mar Delgado, Maria; Miranda, Maria; Alvarez, Silvia J; Gurarie, Eliezer; Fagan, William F; Penteriani, Vincenzo; di Virgilio, Agustina; Morales, Juan Manuel

2018-05-19

Animal collective movements are a key example of a system that links two clearly defined levels of organization: the individual and the group. Most models investigating collective movements have generated coherent collective behaviours without the inclusion of individual variability. However, new individual-based models, together with emerging empirical information, emphasize that within-group heterogeneity may strongly influence collective movement behaviour. Here we (i) review the empirical evidence for individual variation in animal collective movements, (ii) explore how theoretical investigations have represented individual heterogeneity when modelling collective movements and (iii) present a model to show how within-group heterogeneity influences the collective properties of a group. Our review underscores the need to consider variability at the level of the individual to improve our understanding of how individual decision rules lead to emergent movement patterns, and also to yield better quantitative predictions of collective behaviour.This article is part of the theme issue 'Collective movement ecology'. © 2018 The Author(s).

Schmallenberg virus: Predicting within-herd seroprevalence using bulk-tank milk antibody titres and exploring individual animal antibody titres using empirical distribution functions (EDF).

PubMed

Collins, Á B; Grant, J; Barrett, D; Doherty, M L; Hallinan, A; Mee, J F

2017-08-01

Schmallenberg virus (SBV) is transmitted by Culicoides spp. biting midges and can cause abortions and congenital malformations in ruminants and milk drop in dairy cattle. Estimating true within-herd seroprevalence is an essential component of efficient and cost-effective SBV surveillance programs. The objectives of this study were: (1) determine the correlation between bulk-tank milk (BTM)-ELISA results and within-herd seroprevalence, (2) evaluate the ability of BTM-ELISA results to predict within-herd seroprevalence and (3) explore the distributions of individual animal serology results using novel statistical methodology. BTM samples (n=24) and blood samples (n=4019) collected from all lactating cows contributing to the BTM in 26 Irish dairy herds (58-444 cows/herd) in 2014 located in a region exposed to SBV in 2012/2013, were analysed for SBV-specific antibodies using IDVet ® ELISA kits. The correlation between BTM-ELISA results and within-herd seroprevalence was determined by calculating Pearson's correlation coefficient. Linear regression models were used to assess the ability of BTM-ELISA results to predict within-herd seroprevalence. The distributions of individual animal serology results were explored by determining the empirical distribution functions (EDF) of the individual animal serum ELISA results in each herd. EDFs were compared pairwise across herds, using the Kolmogorov-Smirnov statistical test. Herds with similar BTM-ELISA results, herds with similar within-herd seroprevalence and herds with similar mean-herd serology ELISA results were stratified in order to explore their respective paired-herd EDF comparisons. Statistical significance was set at p<0.05. Twenty-two herds were BTM-ELISA-positive (within-herd seroprevalence 30.6-100%) and two herds were BTM-ELISA-negative (within-herd seroprevalence 10.7 and 16.2%) indicating BTM-ELISA-negative herds can have seropositive animals present. BTM-ELISA results were highly correlated (r=0.807, p<0.0001) with, and predictive of (R 2 =0.832, p<0.0001) of within-herd seroprevalence. Predictions were most accurate for upper-range BTM-ELISA antibody titres, while they were less accurate at higher and lower antibody titres. This is likely a result of the overall high within-herd seroprevalence. In herds with similar BTM-ELISA results 82% of the paired-herd EDF comparisons were significantly different. In herds with similar within-herd seroprevalence and in herds with similar mean-herd serology ELISA results, 46% and 47% of the paired-herd EDF comparisons were significantly different, respectively. These results demonstrate that BTM antibody titres are highly predictive of within-herd seroprevalence in an SBV exposed region. Furthermore, exploring the serum EDFs revealed that the variation observed in the predicted within-herd seroprevalence in the regression models is likely a result of individual animal variation in serum antibody titres in these herds. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of radiation necrosis in a rodent model using magnetic resonance imaging apparent transverse relaxation (R_{2}^{*} )

NASA Astrophysics Data System (ADS)

Belliveau, Jean-Guy; Jensen, Michael D.; Stewart, James M. P.; Solovey, Igor; Klassen, L. Martyn; Bauman, Glenn S.; Menon, Ravi S.

2018-02-01

Background and purpose. Radiation necrosis remains an irreversible long-term side-effect following radiotherapy to the brain. The ability to predict areas that could ultimately develop into necrosis could lead to prevention and management of radiation necrosis. Materials and Methods. Fischer 344 rats were irradiated using two platforms (micro-CT irradiator and x-Rad 225 IGRT) with radiation up to 30 Gy for the micro-CT and 40 Gy for the xRAD-224 to half the brain. Animals were subsequently imaged using a 9.4 T MRI scanner every 2-4 weeks for up to 28 weeks using a 7-echo gradient echo sequence. The apparent transverse relaxation constant (R2* ) was calculated and retrospectively analyzed. Results. Animals irradiated with the low-dose rate micro-CT did not exhibit any symptoms or imaging changes associated with RN. Animals irradiated with the xRAD-225 exhibited imaging changes consistent with RN at week 24. Analysis of the R2* coefficient within the lesion and hippocampus shows the potential for detection of RN up to 10 weeks prior to morphological changes. Conclusions. The ability to predict areas of RN and increases of R2* within the hippocampus provides a method for long-term monitoring and prediction of RN.

Sound scattering by several zooplankton groups. II. Scattering models.

PubMed

Stanton, T K; Chu, D; Wiebe, P H

1998-01-01

Mathematical scattering models are derived and compared with data from zooplankton from several gross anatomical groups--fluidlike, elastic shelled, and gas bearing. The models are based upon the acoustically inferred boundary conditions determined from laboratory backscattering data presented in part I of this series [Stanton et al., J. Acoust. Soc. Am. 103, 225-235 (1998)]. The models use a combination of ray theory, modal-series solution, and distorted wave Born approximation (DWBA). The formulations, which are inherently approximate, are designed to include only the dominant scattering mechanisms as determined from the experiments. The models for the fluidlike animals (euphausiids in this case) ranged from the simplest case involving two rays, which could qualitatively describe the structure of target strength versus frequency for single pings, to the most complex case involving a rough inhomogeneous asymmetrically tapered bent cylinder using the DWBA-based formulation which could predict echo levels over all angles of incidence (including the difficult region of end-on incidence). The model for the elastic shelled body (gastropods in this case) involved development of an analytical model which takes into account irregularities and discontinuities of the shell. The model for gas-bearing animals (siphonophores) is a hybrid model which is composed of the summation of the exact solution to the gas sphere and the approximate DWBA-based formulation for arbitrarily shaped fluidlike bodies. There is also a simplified ray-based model for the siphonophore. The models are applied to data involving single pings, ping-to-ping variability, and echoes averaged over many pings. There is reasonable qualitative agreement between the predictions and single ping data, and reasonable quantitative agreement between the predictions and variability and averages of echo data.

Developing probabilistic models to predict amphibian site occupancy in a patchy landscape

Treesearch

R. A. Knapp; K.R. Matthews; H. K. Preisler; R. Jellison

2003-01-01

Abstract. Human-caused fragmentation of habitats is threatening an increasing number of animal and plant species, making an understanding of the factors influencing patch occupancy ever more important. The overall goal of the current study was to develop probabilistic models of patch occupancy for the mountain yellow-legged frog (Rana muscosa). This once-common species...

A feed-forward neural network for modeling feeding behavior of group-housed grow-finish pigs with respect to thermal conditions

USDA-ARS?s Scientific Manuscript database

Feeding patterns of pigs in the grow-finish phase have been investigated for use in management decisions, identifying sick animals, and determining genetic differences within a herd. Development of models to predict swine feeding behavior has been limited due the large number of potential environmen...

Genomic prediction in a nuclear population of layers using single-step models.

PubMed

Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

2018-02-01

Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

Peritraumatic startle response predicts the vulnerability to develop PTSD-like behaviors in rats: a model for peritraumatic dissociation

PubMed Central

Dong, Xinwen; Li, Yonghui

2014-01-01

Peritraumatic dissociation, a state characterized by alteration in perception and reduced awareness of surroundings, is considered to be a risk factor for the development of post-traumatic stress disorder (PTSD). However, the predictive ability of peritraumatic dissociation is questioned for the inconsistent results in different time points of assessment. The startle reflex is an objective behavioral measurement of defensive response to abrupt and intense sensory stimulus of surroundings, with potential to be used as an assessment on the dissociative status in both humans and rodents. The present study examined the predictive effect of acoustic startle response (ASR) in different time points around the traumatic event in an animal model of PTSD. The PTSD-like symptoms, including hyperarousal, avoidance, and contextual fear, were assessed 2–3 weeks post-trauma. The results showed that (1) the startle amplitude attenuated immediate after intense footshock in almost half of the stress animals, and (2) the attenuated startle responses at 1 h but not 24 h after stress predicted the development of severe PTSD-like symptoms. These data indicate that the startle alteration at the immediate period after trauma, including 1 h, is more important in PTSD prediction than 24 h after trauma. Our study also suggests that the startle attenuation immediate after intense stress may serve as an objective measurement of peritraumatic dissociation in rats. PMID:24478660

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

PubMed

Hessel, Ellen V S; Staal, Yvonne C M; Piersma, Aldert H

2018-03-13

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area. Given the impressive progress in mechanistic knowledge of human biology and toxicology, the time is right for a conceptual approach for designing testing strategies that cover the integral mechanistic landscape of developmental neurotoxicity. The ontology approach provides a framework for defining this landscape, upon which an integral in silico model for predicting toxicity can be built. It subsequently directs the selection of in vitro assays for rate-limiting events in the biological network, to feed parameter tuning in the model, leading to prediction of the toxicological outcome. Validation of such models requires primary attention to coverage of the biological domain, rather than classical predictive value of individual tests. Proofs of concept for such an approach are already available. The challenge is in mining modern biology, toxicology and chemical information to feed intelligent designs, which will define testing strategies for neurodevelopmental toxicity testing. Copyright © 2018 Elsevier Inc. All rights reserved.

A computational substrate for incentive salience.

PubMed

McClure, Samuel M; Daw, Nathaniel D; Montague, P Read

2003-08-01

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

Predicting responses to climate change requires all life-history stages.

PubMed

Zeigler, Sara

2013-01-01

In Focus: Radchuk, V., Turlure, C. & Schtickzelle, N. (2013) Each life stage matters: the importance of assessing response to climate change over the complete life cycle in butterflies. Journal of Animal Ecology, 82, 275-285. Population-level responses to climate change depend on many factors, including unexpected interactions among life history attributes; however, few studies examine climate change impacts over complete life cycles of focal species. Radchuk, Turlure & Schtickzelle () used experimental and modelling approaches to predict population dynamics for the bog fritillary butterfly under warming scenarios. Although they found that warming improved fertility and survival of all stages with one exception, populations were predicted to decline because overwintering larvae, whose survival declined with warming, were disproportionately important contributors to population growth. This underscores the importance of considering all life history stages in analyses of climate change's effects on population dynamics. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Acylcarnitines profile best predicts survival in horses with atypical myopathy

PubMed Central

Detilleux, Johann; Cello, Christophe; Amory, Hélène; Marcillaud-Pitel, Christel; Richard, Eric; van Galen, Gaby; van Loon, Gunther; Lefère, Laurence; Votion, Dominique-Marie

2017-01-01

Equine atypical myopathy (AM) is caused by hypoglycin A intoxication and is characterized by a high fatality rate. Predictive estimation of survival in AM horses is necessary to prevent unnecessary suffering of animals that are unlikely to survive and to focus supportive therapy on horses with a possible favourable prognosis of survival. We hypothesized that outcome may be predicted early in the course of disease based on the assumption that the acylcarnitine profile reflects the derangement of muscle energetics. We developed a statistical model to prognosticate the risk of death of diseased animals and found that estimation of outcome may be drawn from three acylcarnitines (C2, C10:2 and C18 -carnitines) with a high sensitivity and specificity. The calculation of the prognosis of survival makes it possible to distinguish the horses that will survive from those that will die despite severe signs of acute rhabdomyolysis in both groups. PMID:28846683

NON-INDIGENOUS SPECIES: IMPORTANT BIOLOGICAL STRESSORS

EPA Science Inventory

A model for predicting where certain species will invade next is being developed and tested in cooperation with researchers at the University of Kansas. Human activities have increased the wholesale movement, either accidental or deliberate, of many species of animals and plants ...

Multivariate Models for Prediction of Human Skin Sensitization Hazard.

EPA Science Inventory

One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensiti...

PREDICTIVE PHYSIOLOGICALLY BASED PHARMACOKINETICS MODELING (PBPK) OF PYRETHROID PESTICIDES

EPA Science Inventory

Pyrethroids are a class of neurotoxic pesticides that have many different applications in agriculture, horticulture, and homes, and medicinal uses for animals and humans. Differences in the toxicity of pyrethroids are the result of their pharmacokinetic and/or pharmacodynamic pr...

Stable isotope turnover and half-life in animal tissues: a literature synthesis.

PubMed

Vander Zanden, M Jake; Clayton, Murray K; Moody, Eric K; Solomon, Christopher T; Weidel, Brian C

2015-01-01

Stable isotopes of carbon, nitrogen, and sulfur are used as ecological tracers for a variety of applications, such as studies of animal migrations, energy sources, and food web pathways. Yet uncertainty relating to the time period integrated by isotopic measurement of animal tissues can confound the interpretation of isotopic data. There have been a large number of experimental isotopic diet shift studies aimed at quantifying animal tissue isotopic turnover rate λ (%·day(-1), often expressed as isotopic half-life, ln(2)/λ, days). Yet no studies have evaluated or summarized the many individual half-life estimates in an effort to both seek broad-scale patterns and characterize the degree of variability. Here, we collect previously published half-life estimates, examine how half-life is related to body size, and test for tissue- and taxa-varying allometric relationships. Half-life generally increases with animal body mass, and is longer in muscle and blood compared to plasma and internal organs. Half-life was longest in ecotherms, followed by mammals, and finally birds. For ectotherms, different taxa-tissue combinations had similar allometric slopes that generally matched predictions of metabolic theory. Half-life for ectotherms can be approximated as: ln (half-life) = 0.22*ln (body mass) + group-specific intercept; n = 261, p<0.0001, r2 = 0.63. For endothermic groups, relationships with body mass were weak and model slopes and intercepts were heterogeneous. While isotopic half-life can be approximated using simple allometric relationships for some taxa and tissue types, there is also a high degree of unexplained variation in our models. Our study highlights several strong and general patterns, though accurate prediction of isotopic half-life from readily available variables such as animal body mass remains elusive.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

A Mouse Model to Evaluate the Impact of Species, Sex, and Lipid Load on Lymphatic Drug Transport

PubMed Central

Caliph, Suzanne M.; Nguyen, Gary; Tso, Patrick; Charman, William N.

2014-01-01

Purpose To establish a lymph-cannulated mouse model, and use the model to investigate the impact of lipid dose on exogenous and endogenous lipid recruitment, and drug transport, into the lymph of males versus females. Finally, lymphatic transport and drug absorption in the mouse were compared to other pre-clinical models (rats/dogs). Methods Animals were orally or intraduodenally administered 1.6 mg/kg halofantrine in low or high 14C-lipid doses. For bioavailability calculation, animals were intravenuosly administered halofantrine. Lymph or blood samples were taken and halofantrine, triglyceride, phospholipid and 14C-lipid concentrations measured. Results Lymphatic lipid transport increased linearly with lipid dose, was similar across species and in male/female animals. In contrast, lymphatic transport of halofantrine differed markedly across species (dogs>rats>mice) and plateaued at higher lipid doses. Lower bioavailability appeared responsible for some species differences in halofantrine lymphatic transport; however other systematic differences were involved. Conclusions A contemporary lymph-cannulated mouse model was established which will enable investigation of lymphatic transport in transgenic and disease models. The current study found halofantrine absorption and lymphatic transport are reduced in small animals. Future analyses will investigate mechanisms involved, and if similar trends occur for other drugs, to establish the most relevant model(s) to predict lymphatic transport in humans. PMID:23430484

Experimental Animal Models for Studies on the Mechanisms of Blast-Induced Neurotrauma

PubMed Central

Risling, Mårten; Davidsson, Johan

2012-01-01

A blast injury is a complex type of physical trauma resulting from the detonation of explosive compounds and has become an important issue due to the use of improvised explosive devices (IED) in current military conflicts. Blast-induced neurotrauma (BINT) is a major concern in contemporary military medicine and includes a variety of injuries that range from mild to lethal. Extreme forces and their complex propagation characterize BINT. Modern body protection and the development of armored military vehicles can be assumed to have changed the outcome of BINT. Primary blast injuries are caused by overpressure waves whereas secondary, tertiary, and quaternary blast injuries can have more varied origins such as the impact of fragments, abnormal movements, or heat. The characteristics of the blast wave can be assumed to be significantly different in open field detonations compared to explosions in a confined space, such an armored vehicle. Important parameters include peak pressure, duration, and shape of the pulse. Reflections from walls and armor can make the prediction of effects in individual cases very complex. Epidemiological data do not contain information of the comparative importance of the different blast mechanisms. It is therefore important to generate data in carefully designed animal models. Such models can be selective reproductions of a primary blast, penetrating injuries from fragments, acceleration movements, or combinations of such mechanisms. It is of crucial importance that the physical parameters of the employed models are well characterized so that the experiments can be reproduced in different laboratory settings. Ideally, pressure recordings should be calibrated by using the same equipment in several laboratories. With carefully designed models and thoroughly evaluated animal data it should be possible to achieve a translation of data between animal and clinical data. Imaging and computer simulation represent a possible link between experiments and studies of human cases. However, in order for mathematical simulations to be completely useful, the predictions will most likely have to be validated by detailed data from animal experiments. Some aspects of BINT can conceivably be studied in vitro. However, factors such as systemic response, brain edema, inflammation, vasospasm, or changes in synaptic transmission and behavior must be evaluated in experimental animals. Against this background, it is necessary that such animal experiments are carefully developed imitations of actual components in the blast injury. This paper describes and discusses examples of different designs of experimental models relevant to BINT. PMID:22485104

The ecology and evolution of animal medication: genetically fixed response versus phenotypic plasticity.

PubMed

Choisy, Marc; de Roode, Jacobus C

2014-08-01

Animal medication against parasites can occur either as a genetically fixed (constitutive) or phenotypically plastic (induced) behavior. Taking the tritrophic interaction between the monarch butterfly Danaus plexippus, its protozoan parasite Ophryocystis elektroscirrha, and its food plant Asclepias spp. as a test case, we develop a game-theory model to identify the epidemiological (parasite prevalence and virulence) and environmental (plant toxicity and abundance) conditions that predict the evolution of genetically fixed versus phenotypically plastic forms of medication. Our model shows that the relative benefits (the antiparasitic properties of medicinal food) and costs (side effects of medicine, the costs of searching for medicine, and the costs of plasticity itself) crucially determine whether medication is genetically fixed or phenotypically plastic. Our model suggests that animals evolve phenotypic plasticity when parasite risk (a combination of virulence and prevalence and thus a measure of the strength of parasite-mediated selection) is relatively low to moderately high and genetically fixed medication when parasite risk becomes very high. The latter occurs because at high parasite risk, the costs of plasticity are outweighed by the benefits of medication. Our model provides a simple and general framework to study the conditions that drive the evolution of alternative forms of animal medication.

Emergent Properties of Patch Shapes Affect Edge Permeability to Animals

PubMed Central

Nams, Vilis O.

2011-01-01

Animal travel between habitat patches affects populations, communities and ecosystems. There are three levels of organization of edge properties, and each of these can affect animals. At the lowest level are the different habitats on each side of an edge, then there is the edge itself, and finally, at the highest level of organization, is the geometry or structure of the edge. This study used computer simulations to (1) find out whether effects of edge shapes on animal behavior can arise as emergent properties solely due to reactions to edges in general, without the animals reacting to the shapes of the edges, and to (2) generate predictions to allow field and experimental studies to test mechanisms of edge shape response. Individual animals were modeled traveling inside a habitat patch that had different kinds of edge shapes (convex, concave and straight). When animals responded edges of patches, this created an emergent property of responding to the shape of the edge. The response was mostly to absolute width of the shapes, and not the narrowness of them. When animals were attracted to edges, then they tended to collect in convexities and disperse from concavities, and the opposite happened when animals avoided edges. Most of the responses occurred within a distance of 40% of the perceptual range from the tip of the shapes. Predictions were produced for directionality at various locations and combinations of treatments, to be used for testing edge behavior mechanisms. These results suggest that edge shapes tend to either concentrate or disperse animals, simply because the animals are either attracted to or avoid edges, with an effect as great as 3 times the normal density. Thus edge shape could affect processes like pollination, seed predation and dispersal and predator abundance. PMID:21747965

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

PubMed

Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

2018-06-01

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Using the Animal Model to Accelerate Response to Selection in a Self-Pollinating Crop

PubMed Central

Cowling, Wallace A.; Stefanova, Katia T.; Beeck, Cameron P.; Nelson, Matthew N.; Hargreaves, Bonnie L. W.; Sass, Olaf; Gilmour, Arthur R.; Siddique, Kadambot H. M.

2015-01-01

We used the animal model in S0 (F1) recurrent selection in a self-pollinating crop including, for the first time, phenotypic and relationship records from self progeny, in addition to cross progeny, in the pedigree. We tested the model in Pisum sativum, the autogamous annual species used by Mendel to demonstrate the particulate nature of inheritance. Resistance to ascochyta blight (Didymella pinodes complex) in segregating S0 cross progeny was assessed by best linear unbiased prediction over two cycles of selection. Genotypic concurrence across cycles was provided by pure-line ancestors. From cycle 1, 102/959 S0 plants were selected, and their S1 self progeny were intercrossed and selfed to produce 430 S0 and 575 S2 individuals that were evaluated in cycle 2. The analysis was improved by including all genetic relationships (with crossing and selfing in the pedigree), additive and nonadditive genetic covariances between cycles, fixed effects (cycles and spatial linear trends), and other random effects. Narrow-sense heritability for ascochyta blight resistance was 0.305 and 0.352 in cycles 1 and 2, respectively, calculated from variance components in the full model. The fitted correlation of predicted breeding values across cycles was 0.82. Average accuracy of predicted breeding values was 0.851 for S2 progeny of S1 parent plants and 0.805 for S0 progeny tested in cycle 2, and 0.878 for S1 parent plants for which no records were available. The forecasted response to selection was 11.2% in the next cycle with 20% S0 selection proportion. This is the first application of the animal model to cyclic selection in heterozygous populations of selfing plants. The method can be used in genomic selection, and for traits measured on S0-derived bulks such as grain yield. PMID:25943522

Info-gap robust-satisficing model of foraging behavior: do foragers optimize or satisfice?

PubMed

Carmel, Yohay; Ben-Haim, Yakov

2005-11-01

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Predicting path from undulations for C. elegans using linear and nonlinear resistive force theory

NASA Astrophysics Data System (ADS)

Keaveny, Eric E.; Brown, André E. X.

2017-04-01

A basic issue in the physics of behaviour is the mechanical relationship between an animal and its surroundings. The model nematode C. elegans provides an excellent platform to explore this relationship due to its anatomical simplicity. Nonetheless, the physics of nematode crawling, in which the worm undulates its body to move on a wet surface, is not completely understood and the mathematical models often used to describe this phenomenon are empirical. We confirm that linear resistive force theory, one such empirical model, is effective at predicting a worm’s path from its sequence of body postures for forward crawling, reversing, and turning and for a broad range of different behavioural phenotypes observed in mutant worms. Worms recently isolated from the wild have a higher effective drag anisotropy than the laboratory-adapted strain N2 and most mutant strains. This means the wild isolates crawl with less surface slip, perhaps reflecting more efficient gaits. The drag anisotropies required to fit the observed locomotion data (70  ±  28 for the wild isolates) are significantly larger than the values measured by directly dragging worms along agar surfaces (3-10 in Rabets et al (2014 Biophys. J. 107 1980-7)). A proposed nonlinear extension of the resistive force theory model also provides accurate predictions, but does not resolve the discrepancy between the parameters required to achieve good path prediction and the experimentally measured parameters. We confirm that linear resistive force theory provides a good effective model of worm crawling that can be used in applications such as whole-animal simulations and advanced tracking algorithms, but that the nature of the physical interaction between worms and their most commonly studied laboratory substrate remains unresolved.

Predicting path from undulations for C. elegans using linear and nonlinear resistive force theory.

PubMed

Keaveny, Eric E; Brown, André E X

2017-03-22

A basic issue in the physics of behaviour is the mechanical relationship between an animal and its surroundings. The model nematode C. elegans provides an excellent platform to explore this relationship due to its anatomical simplicity. Nonetheless, the physics of nematode crawling, in which the worm undulates its body to move on a wet surface, is not completely understood and the mathematical models often used to describe this phenomenon are empirical. We confirm that linear resistive force theory, one such empirical model, is effective at predicting a worm's path from its sequence of body postures for forward crawling, reversing, and turning and for a broad range of different behavioural phenotypes observed in mutant worms. Worms recently isolated from the wild have a higher effective drag anisotropy than the laboratory-adapted strain N2 and most mutant strains. This means the wild isolates crawl with less surface slip, perhaps reflecting more efficient gaits. The drag anisotropies required to fit the observed locomotion data (70  ±  28 for the wild isolates) are significantly larger than the values measured by directly dragging worms along agar surfaces (3-10 in Rabets et al (2014 Biophys. J. 107 1980-7)). A proposed nonlinear extension of the resistive force theory model also provides accurate predictions, but does not resolve the discrepancy between the parameters required to achieve good path prediction and the experimentally measured parameters. We confirm that linear resistive force theory provides a good effective model of worm crawling that can be used in applications such as whole-animal simulations and advanced tracking algorithms, but that the nature of the physical interaction between worms and their most commonly studied laboratory substrate remains unresolved.

Insect density-plant density relationships: a modified view of insect responses to resource concentrations.

PubMed

Andersson, Petter; Löfstedt, Christer; Hambäck, Peter A

2013-12-01

Habitat area is an important predictor of spatial variation in animal densities. However, the area often correlates with the quantity of resources within habitats, complicating our understanding of the factors shaping animal distributions. We addressed this problem by investigating densities of insect herbivores in habitat patches with a constant area but varying numbers of plants. Using a mathematical model, predictions of scale-dependent immigration and emigration rates for insects into patches with different densities of host plants were derived. Moreover, a field experiment was conducted where the scaling properties of odour-mediated attraction in relation to the number of odour sources were estimated, in order to derive a prediction of immigration rates of olfactory searchers. The theoretical model predicted that we should expect immigration rates of contact and visual searchers to be determined by patch area, with a steep scaling coefficient, μ = -1. The field experiment suggested that olfactory searchers should show a less steep scaling coefficient, with μ ≈ -0.5. A parameter estimation and analysis of published data revealed a correspondence between observations and predictions, and density-variation among groups could largely be explained by search behaviour. Aphids showed scaling coefficients corresponding to the prediction for contact/visual searchers, whereas moths, flies and beetles corresponded to the prediction for olfactory searchers. As density responses varied considerably among groups, and variation could be explained by a certain trait, we conclude that a general theory of insect responses to habitat heterogeneity should be based on shared traits, rather than a general prediction for all species.

ANIMAL ANALOGIES IN FIRST IMPRESSIONS OF FACES.

PubMed

Zebrowitz, Leslie A; Wadlinger, Heather A; Luevano, Victor X; White, Benjamin M; Xing, Cai; Zhang, Yi

2011-08-01

Analogies between humans and animals based on facial resemblance have a long history. We report evidence for reverse anthropomorphism and the extension of facial stereotypes to lions, foxes, and dogs. In the stereotype extension, more positive traits were attributed to animals judged more attractive than con-specifics; more childlike traits were attributed to those judged more babyfaced. In the reverse anthropomorphism, human faces with more resemblance to lions, ascertained by connectionist modeling of facial metrics, were judged more dominant, cold, and shrewd, controlling attractiveness, babyfaceness, and sex. Faces with more resemblance to Labradors were judged warmer and less shrewd. Resemblance to foxes did not predict impressions. Results for lions and dogs were consistent with trait impressions of these animals and support the species overgeneralization hypothesis that evolutionarily adaptive reactions to particular animals are overgeneralized, with people perceived to have traits associated with animals their faces resemble. Other possible explanations are discussed.

ANIMAL ANALOGIES IN FIRST IMPRESSIONS OF FACES

PubMed Central

Zebrowitz, Leslie A.; Wadlinger, Heather A.; Luevano, Victor X.; White, Benjamin M.; Xing, Cai; Zhang, Yi

2013-01-01

Analogies between humans and animals based on facial resemblance have a long history. We report evidence for reverse anthropomorphism and the extension of facial stereotypes to lions, foxes, and dogs. In the stereotype extension, more positive traits were attributed to animals judged more attractive than con-specifics; more childlike traits were attributed to those judged more babyfaced. In the reverse anthropomorphism, human faces with more resemblance to lions, ascertained by connectionist modeling of facial metrics, were judged more dominant, cold, and shrewd, controlling attractiveness, babyfaceness, and sex. Faces with more resemblance to Labradors were judged warmer and less shrewd. Resemblance to foxes did not predict impressions. Results for lions and dogs were consistent with trait impressions of these animals and support the species overgeneralization hypothesis that evolutionarily adaptive reactions to particular animals are overgeneralized, with people perceived to have traits associated with animals their faces resemble. Other possible explanations are discussed. PMID:25339791

Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases.

PubMed

Kifle, Yimer Wasihun; Goeyvaerts, Nele; Van Kerckhove, Kim; Willem, Lander; Kucharski, Adam; Faes, Christel; Leirs, Herwig; Hens, Niel; Beutels, Philippe

2015-01-01

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0-9 years) and adults (25-54 years) have the highest potential to cause a major zoonotic outbreak.

Animal Ownership and Touching Enrich the Context of Social Contacts Relevant to the Spread of Human Infectious Diseases

PubMed Central

Kifle, Yimer Wasihun; Goeyvaerts, Nele; Van Kerckhove, Kim; Willem, Lander; Faes, Christel; Leirs, Herwig; Hens, Niel; Beutels, Philippe

2015-01-01

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0–9 years) and adults (25–54 years) have the highest potential to cause a major zoonotic outbreak. PMID:26193480

A System Computational Model of Implicit Emotional Learning

PubMed Central

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation. PMID:27378898

A System Computational Model of Implicit Emotional Learning.

PubMed

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

Size-assortative mating and sexual size dimorphism are predictable from simple mechanics of mate-grasping behavior

PubMed Central

2010-01-01

Background A major challenge in evolutionary biology is to understand the typically complex interactions between diverse counter-balancing factors of Darwinian selection for size assortative mating and sexual size dimorphism. It appears that rarely a simple mechanism could provide a major explanation of these phenomena. Mechanics of behaviors can predict animal morphology, such like adaptations to locomotion in animals from various of taxa, but its potential to predict size-assortative mating and its evolutionary consequences has been less explored. Mate-grasping by males, using specialized adaptive morphologies of their forelegs, midlegs or even antennae wrapped around female body at specific locations, is a general mating strategy of many animals, but the contribution of the mechanics of this wide-spread behavior to the evolution of mating behavior and sexual size dimorphism has been largely ignored. Results Here, we explore the consequences of a simple, and previously ignored, fact that in a grasping posture the position of the male's grasping appendages relative to the female's body is often a function of body size difference between the sexes. Using an approach taken from robot mechanics we model coercive grasping of females by water strider Gerris gracilicornis males during mating initiation struggles. We determine that the male optimal size (relative to the female size), which gives the males the highest grasping force, properly predicts the experimentally measured highest mating success. Through field sampling and simulation modeling of a natural population we determine that the simple mechanical model, which ignores most of the other hypothetical counter-balancing selection pressures on body size, is sufficient to account for size-assortative mating pattern as well as species-specific sexual dimorphism in body size of G. gracilicornis. Conclusion The results indicate how a simple and previously overlooked physical mechanism common in many taxa is sufficient to account for, or importantly contribute to, size-assortative mating and its consequences for the evolution of sexual size dimorphism. PMID:21092131

Multivariate Models for Prediction of Skin Sensitization Hazard in Humans

EPA Science Inventory

One of ICCVAM’s highest priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative me...

Prediction of Pathologic Fracture Risk in Activities of Daily Living and Rehabilitation of Patients With Metastatic Breast Carcinoma of the Pelvis and Femur

DTIC Science & Technology

2002-08-01

demonstrated in three-dimensional graphics and animations. This computer model will aid in planning of non-operative or operative management, rehabilitation regimens, nursing programs, and patient education .

Multivariate Models for Prediction of Human Skin Sensitization Hazard

EPA Science Inventory

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative method...

Learned helplessness in the rat: improvements in validity and reliability.

PubMed

Vollmayr, B; Henn, F A

2001-08-01

Major depression has a high prevalence and a high mortality. Despite many years of research little is known about the pathophysiologic events leading to depression nor about the causative molecular mechanisms of antidepressant treatment leading to remission and prevention of relapse. Animal models of depression are urgently needed to investigate new hypotheses. The learned helplessness paradigm initially described by Overmier and Seligman [J. Comp. Physiol. Psychol. 63 (1967) 28] is the most widely studied animal model of depression. Animals are exposed to inescapable shock and subsequently tested for a deficit in acquiring an avoidance task. Despite its excellent validity concerning the construct of etiology, symptomatology and prediction of treatment response [Clin. Neurosci. 1 (1993) 152; Trends Pharmacol. Sci. 12 (1991) 131] there has been little use of the model for the investigation of recent theories on the pathogenesis of depression. This may be due to reported difficulties in reliability of the paradigm [Animal Learn. Behav. 4 (1976) 401; Pharmacol. Biochem. Behav. 36 (1990) 739]. The aim of the current study was therefore to improve parameters for inescapable shock and learned helplessness testing to minimize artifacts and random error and yield a reliable fraction of helpless animals after shock exposure. The protocol uses mild current which induces helplessness only in some of the animals thereby modeling the hypothesis of variable predisposition for depression in different subjects [Psychopharmacol. Bull. 21 (1985) 443; Neurosci. Res. 38 (200) 193]. This allows us to use animals which are not helpless after inescapable shock as a stressed control, but sensitivity, specificity and variability of test results have to be reassessed.

Quantitative systems toxicology

PubMed Central

Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.

2017-01-01

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440

Gastrointestinal absorption of americium-241 by orally exposed swine: comparison of experimental results with predictions of metabolic models.

PubMed

Eisele, G R; Bernard, S R; Nestor, C W

1987-10-01

Two groups of 11-week-old swine (40 miniature and 40 domestic swine) received a single oral administration of 1.9 X 10(8) Bq (5.2 mCi) of 241Am citrate, and groups of eight animals, four of each type, were killed and sampled at 1, 2, 4, 8, 16, 24, 48, 72, and 96 h and 30 days later. Uptake and excretion patterns of the radioactivity appeared to occur in three phases: rapid uptake, rapid excretion, and then a slower excretion. All animals were systematically dissected, and the eviscerated carcasses were autoclaved for separation of bone and muscle. The predominant site of deposition was bone, and autoclaving had little effect on releasing 241Am from either bone or muscle. The maximum fractional gastrointestinal absorption of 1.1 X 10(-3) occurred 8 h after radionuclide administration. The tissue distribution data suggest partitions of 50, 20, and 30%, for bone, liver, and other soft tissues, respectively. Two metabolic models were evaluated: a modified Mewhinney-Griffith model and the ICRP 30 model to compare the biological data with model predictions. All models underestimated the actual early time data, but the fits to the experimental results were better at later times.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Medin, Paul M., E-mail: Paul.medin@utsouthwestern.ed; Boike, Thomas P.

Clinical implementation of spinal radiosurgery has increased rapidly in recent years, but little is known regarding human spinal cord tolerance to single-fraction irradiation. In contrast, preclinical studies in single-fraction spinal cord tolerance have been ongoing since the 1970s. The influences of field length, dose rate, inhomogeneous dose distributions, and reirradiation have all been investigated. This review summarizes literature regarding single-fraction spinal cord tolerance in preclinical models with an emphasis on practical clinical significance. The outcomes of studies that incorporate uniform irradiation are surprisingly consistent among multiple small- and large-animal models. Extensive investigation of inhomogeneous dose distributions in the rat hasmore » demonstrated a significant dose-volume effect while preliminary results from one pig study are contradictory. Preclinical spinal cord dose-volume studies indicate that dose distribution is more critical than the volume irradiated suggesting that neither dose-volume histogram analysis nor absolute volume constraints are effective in predicting complications. Reirradiation data are sparse, but results from guinea pig, rat, and pig studies are consistent with the hypothesis that the spinal cord possesses a large capacity for repair. The mechanisms behind the phenomena observed in spinal cord studies are not readily explained and the ability of dose response models to predict outcomes is variable underscoring the need for further investigation. Animal studies provide insight into the phenomena and mechanisms of radiosensitivity but the true significance of animal studies can only be discovered through clinical trials.« less

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Langevin dynamics encapsulate the microscopic and emergent macroscopic properties of midge swarms

PubMed Central

2018-01-01

In contrast to bird flocks, fish schools and animal herds, midge swarms maintain cohesion but do not possess global order. High-speed imaging techniques are now revealing that these swarms have surprising properties. Here, I show that simple models found on the Langevin equation are consistent with this wealth of recent observations. The models predict correctly that large accelerations, exceeding 10 g, will be common and they predict correctly the coexistence of core condensed phases surrounded by dilute vapour phases. The models also provide new insights into the influence of environmental conditions on swarm dynamics. They predict that correlations between midges increase the strength of the effective force binding the swarm together. This may explain why such correlations are absent in laboratory swarms but present in natural swarms which contend with the wind and other disturbances. Finally, the models predict that swarms have fluid-like macroscopic mechanical properties and will slosh rather than slide back and forth after being abruptly displaced. This prediction offers a promising avenue for future experimentation that goes beyond current quasi-static testing which has revealed solid-like responses. PMID:29298958

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

PubMed Central

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults’ belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking. PMID:27853440

In vivo dose measurement using TLDs and MOSFET dosimeters for cardiac radiosurgery.

PubMed

Gardner, Edward A; Sumanaweera, Thilaka S; Blanck, Oliver; Iwamura, Alyson K; Steel, James P; Dieterich, Sonja; Maguire, Patrick

2012-05-10

In vivo measurements were made of the dose delivered to animal models in an effort to develop a method for treating cardiac arrhythmia using radiation. This treatment would replace RF energy (currently used to create cardiac scar) with ionizing radiation. In the current study, the pulmonary vein ostia of animal models were irradiated with 6 MV X-rays in order to produce a scar that would block aberrant signals characteristic of atrial fibrillation. The CyberKnife radiosurgery system was used to deliver planned treatments of 20-35 Gy in a single fraction to four animals. The Synchrony system was used to track respiratory motion of the heart, while the contractile motion of the heart was untracked. The dose was measured on the epicardial surface near the right pulmonary vein and on the esophagus using surgically implanted TLD dosimeters, or in the coronary sinus using a MOSFET dosimeter placed using a catheter. The doses measured on the epicardium with TLDs averaged 5% less than predicted for those locations, while doses measured in the coronary sinus with the MOSFET sensor nearest the target averaged 6% less than the predicted dose. The measurements on the esophagus averaged 25% less than predicted. These results provide an indication of the accuracy with which the treatment planning methods accounted for the motion of the target, with its respiratory and cardiac components. This is the first report on the accuracy of CyberKnife dose delivery to cardiac targets.

Models in animal collective decision-making: information uncertainty and conflicting preferences

PubMed Central

Conradt, Larissa

2012-01-01

Collective decision-making plays a central part in the lives of many social animals. Two important factors that influence collective decision-making are information uncertainty and conflicting preferences. Here, I bring together, and briefly review, basic models relating to animal collective decision-making in situations with information uncertainty and in situations with conflicting preferences between group members. The intention is to give an overview about the different types of modelling approaches that have been employed and the questions that they address and raise. Despite the use of a wide range of different modelling techniques, results show a coherent picture, as follows. Relatively simple cognitive mechanisms can lead to effective information pooling. Groups often face a trade-off between decision accuracy and speed, but appropriate fine-tuning of behavioural parameters could achieve high accuracy while maintaining reasonable speed. The right balance of interdependence and independence between animals is crucial for maintaining group cohesion and achieving high decision accuracy. In conflict situations, a high degree of decision-sharing between individuals is predicted, as well as transient leadership and leadership according to needs and physiological status. Animals often face crucial trade-offs between maintaining group cohesion and influencing the decision outcome in their own favour. Despite the great progress that has been made, there remains one big gap in our knowledge: how do animals make collective decisions in situations when information uncertainty and conflict of interest operate simultaneously? PMID:23565335

Examining the relationship between childhood animal cruelty motives and recurrent adult violent crimes toward humans.

PubMed

Overton, Joshua C; Hensley, Christopher; Tallichet, Suzanne E

2012-03-01

Few researchers have studied the predictive ability of childhood animal cruelty motives as they are associated with later recurrent violence toward humans. Based on a sample of 180 inmates at one medium- and one maximum-security prison in a Southern state, the present study examines the relationship among several retrospectively identified motives (fun, out of anger, hate for the animal, and imitation) for childhood animal cruelty and the later commission of violent crimes (murder, rape, assault, and robbery) against humans. Almost two thirds of the inmates reported engaging in childhood animal cruelty for fun, whereas almost one fourth reported being motivated either out of anger or imitation. Only one fifth of the respondents reported they had committed acts of animal cruelty because they hated the animal. Regression analyses revealed that recurrent animal cruelty was the only statistically significant variable in the model. Respondents who had committed recurrent childhood animal cruelty were more likely to have had committed recurrent adult violence toward humans. None of the motives for committing childhood animal cruelty had any effect on later violence against humans.

Assessing skin sensitization hazard in mice and men using non-animal test methods.

PubMed

Urbisch, Daniel; Mehling, Annette; Guth, Katharina; Ramirez, Tzutzuy; Honarvar, Naveed; Kolle, Susanne; Landsiedel, Robert; Jaworska, Joanna; Kern, Petra S; Gerberick, Frank; Natsch, Andreas; Emter, Roger; Ashikaga, Takao; Miyazawa, Masaaki; Sakaguchi, Hitoshi

2015-03-01

Sensitization, the prerequisite event in the development of allergic contact dermatitis, is a key parameter in both hazard and risk assessments. The pathways involved have recently been formally described in the OECD adverse outcome pathway (AOP) for skin sensitization. One single non-animal test method will not be sufficient to fully address this AOP and in many cases the use of a battery of tests will be necessary. A number of methods are now fully developed and validated. In order to facilitate acceptance of these methods by both the regulatory and scientific communities, results of the single test methods (DPRA, KeratinoSens, LuSens, h-CLAT, (m)MUSST) as well for a the simple '2 out of 3' ITS for 213 substances have been compiled and qualitatively compared to both animal and human data. The dataset was also used to define different mechanistic domains by probable protein-binding mechanisms. In general, the non-animal test methods exhibited good predictivities when compared to local lymph node assay (LLNA) data and even better predictivities when compared to human data. The '2 out of 3' prediction model achieved accuracies of 90% or 79% when compared to human or LLNA data, respectively and thereby even slightly exceeded that of the LLNA. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

[Non-animal toxicology in the safety testing of chemicals].

PubMed

Heinonen, Tuula; Tähti, Hanna

2013-01-01

There is an urgent need to develop predictive test methods better than animal experiments for assessing the safety of chemical substances to man. According to today's vision this is achieved by using human cell based tissue and organ models. In the new testing strategy the toxic effects are assessed by the changes in the critical parameters of the cellular biochemical routes (AOP, adverse toxic outcome pathway-principle) in the target tissues. In vitro-tests are rapid and effective, and with them automation can be applied. The change in the testing paradigm is supported by all stakeholders: scientists, regulators and people concerned on animal welfare.

Control of African swine fever epidemics in industrialized swine populations.

PubMed

Halasa, Tariq; Bøtner, Anette; Mortensen, Sten; Christensen, Hanne; Toft, Nils; Boklund, Anette

2016-12-25

African swine fever (ASF) is a notifiable infectious disease with a high impact on swine health. The disease is endemic in certain regions in the Baltic countries and has spread to Poland constituting a risk of ASF spread toward Western Europe. Therefore, as part of contingency planning, it is important to explore strategies that can effectively control an epidemic of ASF. In this study, the epidemiological and economic effects of strategies to control the spread of ASF between domestic swine herds were examined using a published model (DTU-DADS-ASF). The control strategies were the basic EU and national strategy (Basic), the basic strategy plus pre-emptive depopulation of neighboring swine herds, and intensive surveillance of herds in the control zones, including testing live or dead animals. Virus spread via wild boar was not modelled. Under the basic control strategy, the median epidemic duration was predicted to be 21days (5th and 95th percentiles; 1-55days), the median number of infected herds was predicted to be 3 herds (1-8), and the total costs were predicted to be €326 million (€256-€442 million). Adding pre-emptive depopulation or intensive surveillance by testing live animals resulted in marginal improvements to the control of the epidemics. However, adding testing of dead animals in the protection and surveillance zones was predicted to be the optimal control scenario for an ASF epidemic in industrialized swine populations without contact to wild boar. This optimal scenario reduced the epidemic duration to 9days (1-38) and the total costs to €294 million (€257-€392 million). Export losses were the driving force of the total costs of the epidemics. Copyright © 2016 Elsevier B.V. All rights reserved.

Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements.

PubMed

Wang, Yiwei; Nickel, Barry; Rutishauser, Matthew; Bryce, Caleb M; Williams, Terrie M; Elkaim, Gabriel; Wilmers, Christopher C

2015-01-01

Accelerometers are useful tools for biologists seeking to gain a deeper understanding of the daily behavior of cryptic species. We describe how we used GPS and tri-axial accelerometer (sampling at 64 Hz) collars to monitor behaviors of free-ranging pumas (Puma concolor), which are difficult or impossible to observe in the wild. We attached collars to twelve pumas in the Santa Cruz Mountains, CA from 2010-2012. By implementing Random Forest models, we classified behaviors in wild pumas based on training data from observations and measurements of captive puma behavior. We applied these models to accelerometer data collected from wild pumas and identified mobile and non-mobile behaviors in captive animals with an accuracy rate greater than 96%. Accuracy remained above 95% even after downsampling our accelerometer data to 16 Hz. We were further able to predict low-acceleration movement behavior (e.g. walking) and high-acceleration movement behavior (e.g. running) with 93.8% and 92% accuracy, respectively. We had difficulty predicting non-movement behaviors such as feeding and grooming due to the small size of our training dataset. Lastly, we used model-predicted and field-verified predation events to quantify acceleration characteristics of puma attacks on large prey. These results demonstrate that accelerometers are useful tools for classifying the behaviors of cryptic medium and large-sized terrestrial mammals in their natural habitats and can help scientists gain deeper insight into their fine-scale behavioral patterns. We also show how accelerometer measurements can provide novel insights on the energetics and predation behavior of wild animals. Lastly we discuss the conservation implications of identifying these behavioral patterns in free-ranging species as natural and anthropogenic landscape features influence animal energy allocation and habitat use.

A systematic review of Rift Valley Fever epidemiology 1931–2014

PubMed Central

Nanyingi, Mark O.; Munyua, Peninah; Kiama, Stephen G.; Muchemi, Gerald M.; Thumbi, Samuel M.; Bitek, Austine O.; Bett, Bernard; Muriithi, Reese M.; Njenga, M. Kariuki

2015-01-01

Background Rift Valley Fever (RVF) is a mosquito-borne viral zoonosis that was first isolated and characterized in 1931 in Kenya. RVF outbreaks have resulted in significant losses through human illness and deaths, high livestock abortions and deaths. This report provides an overview on epidemiology of RVF including ecology, molecular diversity spatiotemporal analysis, and predictive risk modeling. Methodology Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched for relevant RVF publications in repositories of the World Health Organization Library and Information Networks for Knowledge (WHOLIS), U.S Centers for Disease Control and Prevention (CDC), and Food and Agricultural Organization (FAO). Detailed searches were performed in Google Scholar, SpringerLink, and PubMed databases and included conference proceedings and books published from 1931 up to 31st January 2015. Results and discussion A total of 84 studies were included in this review; majority (50%) reported on common human and animal risk factors that included consumption of animal products, contact with infected animals and residing in low altitude areas associated with favorable climatic and ecological conditions for vector emergence. A total of 14 (16%) of the publications described RVF progressive spatial and temporal distribution and the use of risk modeling for timely prediction of imminent outbreaks. Using distribution maps, we illustrated the gradual spread and geographical extent of disease; we also estimated the disease burden using aggregate human mortalities and cumulative outbreak periods for endemic regions. Conclusion This review outlines common risk factors for RVF infections over wider geographical areas; it also emphasizes the role of spatial models in predicting RVF enzootics. It, therefore, explains RVF epidemiological status that may be used for design of targeted surveillance and control programs in endemic countries. PMID:26234531

Coping with daily thermal variability: behavioural performance of an ectotherm model in a warming world.

PubMed

Rojas, José M; Castillo, Simón B; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

2014-01-01

Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24 ± 0, 24 ± 4 and 24 ± 8 °C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, --i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance.

Coping with Daily Thermal Variability: Behavioural Performance of an Ectotherm Model in a Warming World

PubMed Central

Rojas, José M.; Castillo, Simón B.; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

2014-01-01

Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24±0, 24±4 and 24±8°C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, – i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance. PMID:25207653

Future methane emissions from animals

NASA Astrophysics Data System (ADS)

Anastasi, C.; Simpson, V. J.

1993-04-01

The future global emission of CH4 from enteric fermentation in animals has been estimated for cattle, sheep, and buffalo, which together contribute approximately 91% of the total CH4 emitted from domesticated animals at present. A simple model has been used to relate livestock levels to the national human populations for each country involved in breeding the three species included in this analysis. United Nations population predictions to 2025 were then included in the model to estimate future CH4 emissions. A variational analysis was carried out to investigate the effect of future changes in both the land available for grazing and the nutritional content of feedstocks. Results suggest that the total emission of CH4 from enteric fermentation in domestic animals will increase from 84 Tg CH4 per year (Tg = 1012 g) in 1990 to 119 (±12) Tg CH4 yr-1 by 2025. These values correspond to an average rate of increase over the next 35 years of 1.0 Tg CH4 yr-1.

Seal Formation Mechanism Beneath Animal Waste Holding Ponds

NASA Astrophysics Data System (ADS)

Cihan, A.; Tyner, J. S.; Wright, W. C.

2005-12-01

Infiltration of animal waste from holding ponds can cause contamination of groundwater. Typically, the initial flux from a pond decreases rapidly as a seal of animal waste particulates is deposited at the base of the pond. The purpose of this study was to investigate the mechanism of the seal formation. Twenty-four soil columns (10-cm diameter by 43-cm long) were hand-packed with sand, silty loam or clay soils. A 2.3 m column of dairy or swine waste was applied to the top of the each column. The leakage rate from each column was measured with respect to time to analyze the effect of seal formation on different soil textures and animal waste types. We tested our hypothesis that seal growth and the subsequent decrease of leachate production adheres to a filter cake growth model. Said model predicts that the cumulative leakage rate is proportional to the square root of time and to the square root of the height of the waste.

Drag, but not buoyancy, affects swim speed in captive Steller sea lions

PubMed Central

Suzuki, Ippei; Sato, Katsufumi; Fahlman, Andreas; Naito, Yasuhiko; Miyazaki, Nobuyuki; Trites, Andrew W.

2014-01-01

ABSTRACT Swimming at an optimal speed is critical for breath-hold divers seeking to maximize the time they can spend foraging underwater. Theoretical studies have predicted that the optimal swim speed for an animal while transiting to and from depth is independent of buoyancy, but is dependent on drag and metabolic rate. However, this prediction has never been experimentally tested. Our study assessed the effects of buoyancy and drag on the swim speed of three captive Steller sea lions (Eumetopias jubatus) that made 186 dives. Our study animals were trained to dive to feed at fixed depths (10–50 m) under artificially controlled buoyancy and drag conditions. Buoyancy and drag were manipulated using a pair of polyvinyl chloride (PVC) tubes attached to harnesses worn by the sea lions, and buoyancy conditions were designed to fall within the natural range of wild animals (∼12–26% subcutaneous fat). Drag conditions were changed with and without the PVC tubes, and swim speeds were recorded and compared during descent and ascent phases using an accelerometer attached to the harnesses. Generalized linear mixed-effect models with the animal as the random variable and five explanatory variables (body mass, buoyancy, dive depth, dive phase, and drag) showed that swim speed was best predicted by two variables, drag and dive phase (AIC = −139). Consistent with a previous theoretical prediction, the results of our study suggest that the optimal swim speed of Steller sea lions is a function of drag, and is independent of dive depth and buoyancy. PMID:24771620

Selection, calibration, and validation of models of tumor growth.

PubMed

Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

2016-11-01

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

An anisotropic, hyperelastic model for skin: experimental measurements, finite element modelling and identification of parameters for human and murine skin.

PubMed

Groves, Rachel B; Coulman, Sion A; Birchall, James C; Evans, Sam L

2013-02-01

The mechanical characteristics of skin are extremely complex and have not been satisfactorily simulated by conventional engineering models. The ability to predict human skin behaviour and to evaluate changes in the mechanical properties of the tissue would inform engineering design and would prove valuable in a diversity of disciplines, for example the pharmaceutical and cosmetic industries, which currently rely upon experiments performed in animal models. The aim of this study was to develop a predictive anisotropic, hyperelastic constitutive model of human skin and to validate this model using laboratory data. As a corollary, the mechanical characteristics of human and murine skin have been compared. A novel experimental design, using tensile tests on circular skin specimens, and an optimisation procedure were adopted for laboratory experiments to identify the material parameters of the tissue. Uniaxial tensile tests were performed along three load axes on excised murine and human skin samples, using a single set of material parameters for each skin sample. A finite element model was developed using the transversely isotropic, hyperelastic constitutive model of Weiss et al. (1996) and was embedded within a Veronda-Westmann isotropic material matrix, using three fibre families to create anisotropic behaviour. The model was able to represent the nonlinear, anisotropic behaviour of the skin well. Additionally, examination of the optimal material coefficients and the experimental data permitted quantification of the mechanical differences between human and murine skin. Differences between the skin types, most notably the extension of the skin at low load, have highlighted some of the limitations of murine skin as a biomechanical model of the human tissue. The development of accurate, predictive computational models of human tissue, such as skin, to reduce, refine or replace animal models and to inform developments in the medical, engineering and cosmetic fields, is a significant challenge but is highly desirable. Concurrent advances in computer technology and our understanding of human physiology must be utilised to produce more accurate and accessible predictive models, such as the finite element model described in this study. Copyright © 2012 Elsevier Ltd. All rights reserved.

One wouldn't expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events.

PubMed

Heil, Lieke; Kwisthout, Johan; van Pelt, Stan; van Rooij, Iris; Bekkering, Harold

2018-01-01

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to "explain away" prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of "experienced" and "novice" bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration

NASA Astrophysics Data System (ADS)

Bartlam-Brooks, Hattie L. A.; Beck, Pieter S. A.; Bohrer, Gil; Harris, Stephen

2013-12-01

ungulate migrations occurred in most grassland and boreal woodland ecosystems, but many have been lost due to increasing habitat loss and fragmentation. With the rate of environmental change increasing, identifying and prioritizing migration routes for conservation has taken on a new urgency. Understanding the cues that drive long-distance animal movements is critical to predicting the fate of migrations under different environmental change scenarios and how large migratory herbivores will respond to increasing resource heterogeneity and anthropogenic influences. We used an individual-based modeling approach to investigate the influence of environmental conditions, monitored using satellite data, on departure date and movement speed of migrating zebras in Botswana. Daily zebra movements between dry and rainy season ranges were annotated with coincident observations of precipitation from the Tropical Rainfall Measuring Mission data set and Moderate Resolution Imaging Spectroradiometer-derived normalized difference vegetation index (NDVI). An array of increasingly complex movement models representing alternative hypotheses regarding the environmental cues and controls for movement was parameterized and tested. The best and most justified model predicted daily zebra movement as two linear functions of precipitation rate and NDVI and included a modeled departure date as a function of cumulative precipitation. The model was highly successful at replicating both the timing and pace of seven actual migrations observed using GPS telemetry (R2 = 0.914). It shows how zebras rapidly adjust their movement to changing environmental conditions during migration and are able to reverse migration to avoid adverse conditions or exploit renewed resource availability, a nomadic behavior which should lend them a degree of resilience to climate and environmental change. Our results demonstrate how competing individual-based migration models, informed by freely available satellite data, can be used to evaluate the weight of evidence for multiple hypotheses regarding the use of environmental cues in animal movement. This modeling framework can be applied to quantify how animals adapt the timing and pace of their movements to prevailing environmental conditions and to forecast migrations in near real time or under alternative environmental scenarios.

Preparation of a Three-Dimensional Full Thickness Skin Equivalent.

PubMed

Reuter, Christian; Walles, Heike; Groeber, Florian

2017-01-01

In vitro test systems are a promising alternative to animal models. Due to the use of human cells in a three-dimensional arrangement that allows cell-cell or cell-matrix interactions these models may be more predictive for the human situation compared to animal models or two-dimensional cell culture systems. Especially for dermatological research, skin models such as epidermal or full-thickness skin equivalents (FTSE) are used for different applications. Although epidermal models provide highly standardized conditions for risk assessment, FTSE facilitate a cellular crosstalk between the dermal and epidermal layer and thus can be used as more complex models for the investigation of processes such as wound healing, skin development, or infectious diseases. In this chapter, we describe the generation and culture of an FTSE, based on a collagen type I matrix and provide troubleshooting tips for commonly encountered technical problems.

Nonmurine animal models of food allergy.

PubMed

Helm, Ricki M; Ermel, Richard W; Frick, Oscar L

2003-02-01

Food allergy can present as immediate hypersensitivity [manifestations mediated by immunoglobulin (Ig)E], delayed-type hypersensitivity (reactions associated with specific T lymphocytes), and inflammatory reactions caused by immune complexes. For reasons of ethics and efficacy, investigations in humans to determine sensitization and allergic responses of IgE production to innocuous food proteins are not feasible. Therefore, animal models are used a) to bypass the innate tendency to develop tolerance to food proteins and induce specific IgE antibody of sufficient avidity/affinity to cause sensitization and upon reexposure to induce an allergic response, b) to predict allergenicity of novel proteins using characteristics of known food allergens, and c) to treat food allergy by using immunotherapeutic strategies to alleviate life-threatening reactions. The predominant hypothesis for IgE-mediated food allergy is that there is an adverse reaction to exogenous food proteins or food protein fragments, which escape lumen hydrolysis, and in a polarized helper T cell subset 2 (Th2) environment, immunoglobulin class switching to allergen-specific IgE is generated in the immune system of the gastrointestinal-associated lymphoid tissues. Traditionally, the immunologic characterization and toxicologic studies of small laboratory animals have provided the basis for development of animal models of food allergy; however, the natural allergic response in large animals, which closely mimic allergic diseases in humans, can also be useful as models for investigations involving food allergy.

ProTox: a web server for the in silico prediction of rodent oral toxicity

PubMed Central

Drwal, Malgorzata N.; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R.; Preissner, Robert

2014-01-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein–ligand-based pharmacophore models (‘toxicophores’) for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. PMID:24838562

The dog and cat population on Maio Island, Cape Verde: characterisation and prediction based on household survey and remotely sensed imagery.

PubMed

Lopes Antunes, Ana Carolina; Ducheyne, Els; Bryssinckx, Ward; Vieira, Sara; Malta, Manuel; Vaz, Yolanda; Nunes, Telmo; Mintiens, Koen

2015-11-04

The objective was to estimate and characterise the dog and cat population on Maio Island, Cape Verde. Remotely sensed imagery was used to document the number of houses across the island and a household survey was carried out in six administrative areas recording the location of each animal using a global positioning system instrument. Linear statistical models were applied to predict the dog and cat populations based on the number of houses found and according to various levels of data aggregation. In the surveyed localities, a total of 457 dogs and 306 cats were found. The majority of animals had owners and only a few had free access to outdoor activities. The estimated population size was 531 dogs [95% confidence interval (CI): 453-609] and 354 cats (95% CI: 275-431). Stray animals were not a concern on the island in contrast to the rest of the country.

Contribution of domestic production records, Interbull estimated breeding values, and single nucleotide polymorphism genetic markers to the single-step genomic evaluation of milk production.

PubMed

Přibyl, J; Madsen, P; Bauer, J; Přibylová, J; Simečková, M; Vostrý, L; Zavadilová, L

2013-03-01

Estimated breeding values (EBV) for first-lactation milk production of Holstein cattle in the Czech Republic were calculated using a conventional animal model and by single-step prediction of the genomic enhanced breeding value. Two overlapping data sets of milk production data were evaluated: (1) calving years 1991 to 2006, with 861,429 lactations and 1,918,901 animals in the pedigree and (2) calving years 1991 to 2010, with 1,097,319 lactations and 1,906,576 animals in the pedigree. Global Interbull (Uppsala, Sweden) deregressed proofs of 114,189 bulls were used in the analyses. Reliabilities of Interbull values were equivalent to an average of 8.53 effective records, which were used in a weighted analysis. A total of 1,341 bulls were genotyped using the Illumina BovineSNP50 BeadChip V2 (Illumina Inc., San Diego, CA). Among the genotyped bulls were 332 young bulls with no daughters in the first data set but more than 50 daughters (88.41, on average) with performance records in the second data set. For young bulls, correlations of EBV and genomic enhanced breeding value before and after progeny testing, corresponding average expected reliabilities, and effective daughter contributions (EDC) were calculated. The reliability of prediction pedigree EBV of young bulls was 0.41, corresponding to EDC=10.6. Including Interbull deregressed proofs improved the reliability of prediction by EDC=13.4 and including genotyping improved prediction reliability by EDC=6.2. Total average expected reliability of prediction reached 0.67, corresponding to EDC=30.2. The combination of domestic and Interbull sources for both genotyped and nongenotyped animals is valuable for improving the accuracy of genetic prediction in small populations of dairy cattle. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Control Theory and Statistical Generalizations.

ERIC Educational Resources Information Center

Powers, William T.

1990-01-01

Contrasts modeling methods in control theory to the methods of statistical generalizations in empirical studies of human or animal behavior. Presents a computer simulation that predicts behavior based on variables (effort and rewards) determined by the invariable (desired reward). Argues that control theory methods better reflect relationships to…

76 FR 57017 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-15

...) Predict or detect trends in disease occurrence and movement, (4) Understand the risk factors for disease... mathematical models of animal disease to evaluate potential control scenarios, (7) Make recommendation for..., and diagnostic testing needs. Description of Respondents: Business or other for-profit. Number of...

Computational Modeling of Male Reproductive Tract Development for Use in Predictive Toxicology (ASCCT meeting)

EPA Science Inventory

Adverse trends in male reproductive health have been reported for increased rates of testicular germ cell tumor, low semen quality, cryptorchidism, and hypospadias. An association with prenatal environmental exposure has been inferred from human and animal studies underlying male...

Genomic Indicators in the blood predict drug-induced liver injury

EPA Science Inventory

Hepatotoxicity and other forms of liver injury stemming from exposure to toxicants and idiosyncratic drug reactions are major concerns during the drug discovery process. Animal model systems have been utilized in an attempt to extrapolate the risk of harmful agents to humans and...

Empirical comparison between different methods for genomic prediction of number of piglets born alive in moderate sized breeding populations.

PubMed

Fangmann, A; Sharifi, R A; Heinkel, J; Danowski, K; Schrade, H; Erbe, M; Simianer, H

2017-04-01

Currently used multi-step methods to incorporate genomic information in the prediction of breeding values (BV) implicitly involve many assumptions which, if violated, may result in loss of information, inaccuracies and bias. To overcome this, single-step genomic best linear unbiased prediction (ssGBLUP) was proposed combining pedigree, phenotype and genotype of all individuals for genetic evaluation. Our objective was to implement ssGBLUP for genomic predictions in pigs and to compare the accuracy of ssGBLUP with that of multi-step methods with empirical data of moderately sized pig breeding populations. Different predictions were performed: conventional parent average (PA), direct genomic value (DGV) calculated with genomic BLUP (GBLUP), a GEBV obtained by blending the DGV with PA, and ssGBLUP. Data comprised individuals from a German Landrace (LR) and Large White (LW) population. The trait 'number of piglets born alive' (NBA) was available for 182,054 litters of 41,090 LR sows and 15,750 litters from 4534 LW sows. The pedigree contained 174,021 animals, of which 147,461 (26,560) animals were LR (LW) animals. In total, 526 LR and 455 LW animals were genotyped with the Illumina PorcineSNP60 BeadChip. After quality control and imputation, 495 LR (424 LW) animals with 44,368 (43,678) SNP on 18 autosomes remained for the analysis. Predictive abilities, i.e., correlations between de-regressed proofs and genomic BV, were calculated with a five-fold cross validation and with a forward prediction for young genotyped validation animals born after 2011. Generally, predictive abilities for LR were rather small (0.08 for GBLUP, 0.19 for GEBV and 0.18 for ssGBLUP). For LW, ssGBLUP had the greatest predictive ability (0.45). For both breeds, assessment of reliabilities for young genotyped animals indicated that genomic prediction outperforms PA with ssGBLUP providing greater reliabilities (0.40 for LR and 0.32 for LW) than GEBV (0.35 for LR and 0.29 for LW). Grouping of animals according to information sources revealed that genomic prediction had the highest potential benefit for genotyped animals without their own phenotype. Although, ssGBLUP did not generally outperform GBLUP or GEBV, the results suggest that ssGBLUP can be a useful and conceptually convincing approach for practical genomic prediction of NBA in moderately sized LR and LW populations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolfe, M.; Norman, D.

Birds and mammals exposed to waterborne mercury (Hg) and methylmercury (MeHg) were collected and/or sampled at Clear Lake, California, USA, to field test the predictive wildlife criteria model developed for the Great Lakes Water Quality Initiative (GLWQI). Tissue samples collected from sampled animals were analyzed for Hg and organochlorine residues, and for selected physiologic parameters known to be affected by Hg. All mammalian organ tissues analyzed contained less than 12 ppm total Hg, wet weight. All avian tissue samples analyzed contained less than 3 ppm total Hg, wet weight. No evidence of Hg-associated health effects was found. Tissue Hg residuesmore » were compared with water, sediment, and animal food samples to characterize bioaccumulation of mercury in the Clear Lake food web. Total Hg bioaccumulation factors for the Clear Lake site closest to the Hg source were: TL-2: 11,100; TL-3: 31,200; TL-4, 190,000. The results support the final wildlife criterion and suggest that the GLWQI model, with site-specific modifications, is predictive for other Hg-bearing aquatic systems.« less

Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

PubMed

Hill, William G

2014-01-01

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

Understanding the Hows and Whys of Decision-Making: From Expected Utility to Divisive Normalization.

PubMed

Glimcher, Paul

2014-01-01

Over the course of the last century, economists and ethologists have built detailed models from first principles of how humans and animals should make decisions. Over the course of the last few decades, psychologists and behavioral economists have gathered a wealth of data at variance with the predictions of these economic models. This has led to the development of highly descriptive models that can often predict what choices people or animals will make but without offering any insight into why people make the choices that they do--especially when those choices reduce a decision-maker's well-being. Over the course of the last two decades, neurobiologists working with economists and psychologists have begun to use our growing understanding of how the nervous system works to develop new models of how the nervous system makes decisions. The result, a growing revolution at the interdisciplinary border of neuroscience, psychology, and economics, is a new field called Neuroeconomics. Emerging neuroeconomic models stand to revolutionize our understanding of human and animal choice behavior by combining fundamental properties of neurobiological representation with decision-theoretic analyses. In this overview, one class of these models, based on the widely observed neural computation known as divisive normalization, is presented in detail. The work demonstrates not only that a discrete class of computation widely observed in the nervous system is fundamentally ubiquitous, but how that computation shapes behaviors ranging from visual perception to financial decision-making. It also offers the hope of reconciling economic analysis of what choices we should make with psychological observations of the choices we actually do make. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.

Skin sensitisation: the Colipa strategy for developing and evaluating non-animal test methods for risk assessment.

PubMed

Maxwell, Gavin; Aeby, Pierre; Ashikaga, Takao; Bessou-Touya, Sandrine; Diembeck, Walter; Gerberick, Frank; Kern, Petra; Marrec-Fairley, Monique; Ovigne, Jean-Marc; Sakaguchi, Hitoshi; Schroeder, Klaus; Tailhardat, Magali; Teissier, Silvia; Winkler, Petra

2011-01-01

Allergic contact dermatitis is a delayed-type hypersensitivity reaction induced by small reactive chemicals (haptens). Currently, the sensitising potential and potency of new chemicals is usually characterised using data generated via animal studies, such as the local lymph node assay (LLNA). There are, however, increasing public and political concerns regarding the use of animals for the testing of new chemicals. Consequently, the development of in vitro, in chemico or in silico models for predicting the sensitising potential and/or potency of new chemicals is receiving widespread interest. The Colipa Skin Tolerance task force currently collaborates with and/or funds several academic research groups to expand our understanding of the molecular and cellular events occurring during the acquisition of skin sensitisation. Knowledge gained from this research is being used to support the development and evaluation of novel alternative approaches for the identification and characterisation of skin sensitizing chemicals. At present three non-animal test methods (Direct Peptide Reactivity Assay (DPRA), Myeloid U937 Skin Sensitisation Test (MUSST) and human Cell Line Activation Test (hCLAT)) have been evaluated in Colipa interlaboratory ring trials for their potential to predict skin sensitisation potential and were recently submitted to ECVAM for formal pre-validation. Data from all three test methods will now be used to support the study and development of testing strategy approaches for skin sensitiser potency prediction. This publication represents the current viewpoint of the cosmetics industry on the feasibility of replacing the need for animal test data for informing skin sensitisation risk assessment decisions.

How to make predictions about future infectious disease risks

PubMed Central

Woolhouse, Mark

2011-01-01

Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

Animal models of drug addiction.

PubMed

García Pardo, María Pilar; Roger Sánchez, Concepción; De la Rubia Ortí, José Enrique; Aguilar Calpe, María Asunción

2017-09-29

The development of animal models of drug reward and addiction is an essential factor for progress in understanding the biological basis of this disorder and for the identification of new therapeutic targets. Depending on the component of reward to be studied, one type of animal model or another may be used. There are models of reinforcement based on the primary hedonic effect produced by the consumption of the addictive substance, such as the self-administration (SA) and intracranial self-stimulation (ICSS) paradigms, and there are models based on the component of reward related to associative learning and cognitive ability to make predictions about obtaining reward in the future, such as the conditioned place preference (CPP) paradigm. In recent years these models have incorporated methodological modifications to study extinction, reinstatement and reconsolidation processes, or to model specific aspects of addictive behavior such as motivation to consume drugs, compulsive consumption or drug seeking under punishment situations. There are also models that link different reinforcement components or model voluntary motivation to consume (two-bottle choice, or drinking in the dark tests). In short, innovations in these models allow progress in scientific knowledge regarding the different aspects that lead individuals to consume a drug and develop compulsive consumption, providing a target for future treatments of addiction.

Airborne spread of foot-and-mouth disease - model intercomparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gloster, J; Jones, A; Redington, A

2008-09-04

Foot-and-mouth disease is a highly infectious vesicular disease of cloven-hoofed animals caused by foot-and-mouth disease virus. It spreads by direct contact between animals, by animal products (milk, meat and semen), by mechanical transfer on people or fomites and by the airborne route - with the relative importance of each mechanism depending on the particular outbreak characteristics. Over the years a number of workers have developed or adapted atmospheric dispersion models to assess the risk of foot-and-mouth disease virus spread through the air. Six of these models were compared at a workshop hosted by the Institute for Animal Health/Met Office duringmore » 2008. A number of key issues emerged from the workshop and subsequent modelling work: (1) in general all of the models predicted similar directions for 'at risk' livestock with much of the remaining differences strongly related to differences in the meteorological data used; (2) determination of an accurate sequence of events is highly important, especially if the meteorological conditions vary substantially during the virus emission period; and (3) differences in assumptions made about virus release, environmental fate, and subsequent infection can substantially modify the size and location of the downwind risk area. Close relationships have now been established between participants, which in the event of an outbreak of disease could be readily activated to supply advice or modelling support.« less

The role of animal models in the pharmacological evaluation of emerging anti-inflammatory agents for the treatment of COPD.

PubMed

Fox, J Craig; Fitzgerald, Mary F

2009-06-01

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease that has been relatively under researched compared to other inflammatory diseases. Indeed, thus far there have been no anti-inflammatory therapies specifically approved for COPD and the available anti-inflammatory therapies were originally developed for asthma. The challenges facing research in COPD are multi-faceted; the mechanisms underlying the complex and heterogeneous pathology of this disease require unravelling; the role of inflammation in disease progression needs to be confirmed and new drugs with potential to successfully treat COPD need to be identified. Many of the compounds in the clinic today have been identified through the work performed in a range of animal models of COPD. These models have provided us with an understanding of disease pathology and potential mechanistic pathways and have given us the means to prioritise new chemical entities before entry into the clinic. This review will summarise currently available models of COPD and highlight how they have been used to take a first generation of anti-inflammatory therapies for COPD into clinical development. The predictive nature of these animal models will become clear as these therapies are clinically evaluated. The recurring challenge will be to take emerging pre-clinical and clinical data and use it to continually improve animal models so that they remain a valuable tool in the drug discovery process.

Long-range Acoustic Interactions in Insect Swarms: An Adaptive Gravity Model

NASA Astrophysics Data System (ADS)

Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. Here, we consider mating swarms of midges, which interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges' acoustic sensing, we show that our ``adaptive gravity'' model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. The adaptive interactions that we present here open a new class of equations of motion, which may appear in other biological contexts.

Long-range acoustic interactions in insect swarms: an adaptive gravity model

NASA Astrophysics Data System (ADS)

Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.

2016-07-01

The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. Here, we consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges’ acoustic sensing, we show that our ‘adaptive gravity’ model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions that we present here open a new class of equations of motion, which may appear in other biological contexts.

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes.

PubMed

Clark, Samuel A; Hickey, John M; Daetwyler, Hans D; van der Werf, Julius H J

2012-02-09

The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

How Sensitive Are Transdermal Transport Predictions by Microscopic Stratum Corneum Models to Geometric and Transport Parameter Input?

PubMed

Wen, Jessica; Koo, Soh Myoung; Lape, Nancy

2018-02-01

While predictive models of transdermal transport have the potential to reduce human and animal testing, microscopic stratum corneum (SC) model output is highly dependent on idealized SC geometry, transport pathway (transcellular vs. intercellular), and penetrant transport parameters (e.g., compound diffusivity in lipids). Most microscopic models are limited to a simple rectangular brick-and-mortar SC geometry and do not account for variability across delivery sites, hydration levels, and populations. In addition, these models rely on transport parameters obtained from pure theory, parameter fitting to match in vivo experiments, and time-intensive diffusion experiments for each compound. In this work, we develop a microscopic finite element model that allows us to probe model sensitivity to variations in geometry, transport pathway, and hydration level. Given the dearth of experimentally-validated transport data and the wide range in theoretically-predicted transport parameters, we examine the model's response to a variety of transport parameters reported in the literature. Results show that model predictions are strongly dependent on all aforementioned variations, resulting in order-of-magnitude differences in lag times and permeabilities for distinct structure, hydration, and parameter combinations. This work demonstrates that universally predictive models cannot fully succeed without employing experimentally verified transport parameters and individualized SC structures. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Prediction of residual feed intake for first-lactation dairy cows using orthogonal polynomial random regression.

PubMed

Manafiazar, G; McFadden, T; Goonewardene, L; Okine, E; Basarab, J; Li, P; Wang, Z

2013-01-01

Residual Feed Intake (RFI) is a measure of energy efficiency. Developing an appropriate model to predict expected energy intake while accounting for multifunctional energy requirements of metabolic body weight (MBW), empty body weight (EBW), milk production energy requirements (MPER), and their nonlinear lactation profiles, is the key to successful prediction of RFI in dairy cattle. Individual daily actual energy intake and monthly body weight of 281 first-lactation dairy cows from 1 to 305 d in milk were recorded at the Dairy Research and Technology Centre of the University of Alberta (Edmonton, AB, Canada); individual monthly milk yield and compositions were obtained from the Dairy Herd Improvement Program. Combinations of different orders (1-5) of fixed (F) and random (R) factors were fitted using Legendre polynomial regression to model the nonlinear lactation profiles of MBW, EBW, and MPER over 301 d. The F5R3, F5R3, and F5R2 (subscripts indicate the order fitted) models were selected, based on the combination of the log-likelihood ratio test and the Bayesian information criterion, as the best prediction equations for MBW, EBW, and MPER, respectively. The selected models were used to predict daily individual values for these traits. To consider the body reserve changes, the differences of predicted EBW between 2 consecutive days were considered as the EBW change between these days. The smoothed total 301-d actual energy intake was then linearly regressed on the total 301-d predicted traits of MBW, EBW change, and MPER to obtain the first-lactation RFI (coefficient of determination=0.68). The mean of predicted daily average lactation RFI was 0 and ranged from -6.58 to 8.64 Mcal of NE(L)/d. Fifty-one percent of the animals had an RFI value below the mean (efficient) and 49% of them had an RFI value above the mean (inefficient). These results indicate that the first-lactation RFI can be predicted from its component traits with a reasonable coefficient of determination. The predicted RFI could be used in the dairy breeding program to increase profitability by selecting animals that are genetically superior in energy efficiency based on RFI, or through routinely measured traits, which are genetically correlated with RFI. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

WE-AB-207B-10: On Spinal Nerve Toxicity from Single-Session SAbR in Pigs and the Translation of Small Animal NTCP Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hrycushko, B; Medin, P

Purpose: The incidence of peripheral neuropathy has risen with increased utilization of SAbR. There is no consensus regarding the dose-tolerance of the peripheral nervous system. In 2015, we commenced an investigation to test the hypotheses that single-session irradiation to the pig spinal nerves exhibit a similar dose-tolerance as that of the spinal cord and that a dose-length effect exists. This work evaluates the direct application of small animal NTCP models to both large animal spinal cord and preliminary peripheral nerve data. Methods: To date, 16 of 25 Yucatan minipigs have received single-session SAbR to a 1.5cm length and 4 ofmore » 25 have received irradiation to a 0.5cm length of left-sided C6-C8 spinal nerves. Toxicity related gait change has been observed in 13 animals (9 from the long length group and 4 from the short). This preliminary data is overlaid on several dose-response models which have been fit to rodent spinal cord tolerance experiments. Model parameters define a toxicity profile between a completely serial or parallel behaving organ. Adequacy of model application, including how length effects are handled, to published minipig spinal cord dose-response data and to preliminary peripheral nerve response data was evaluated through residual analysis. Results: No rodent-derived dose-response models were directly applicable to all pig data for the different lengths irradiated. Several models fit the long-length irradiated spinal cord data well, with the more serial-like models fitting best. Preliminary data on the short-length irradiation suggests no length effect exists, disproving our hypothesis. Conclusion: Direct application of small-animal NTCP models to pig data suggests dose-length effect predictions from small animal data may not translate clinically. However, the small animal models used have not considered dose heterogeneity and it is expected that including the low-to-mid dose levels in the penumbral region will improve this match. This work was funded by the Cancer Prevention Research Institute of Texas (CPRIT).« less

Human tissue models in cancer research: looking beyond the mouse.

PubMed

Jackson, Samuel J; Thomas, Gareth J

2017-08-01

Mouse models, including patient-derived xenograft mice, are widely used to address questions in cancer research. However, there are documented flaws in these models that can result in the misrepresentation of human tumour biology and limit the suitability of the model for translational research. A coordinated effort to promote the more widespread development and use of 'non-animal human tissue' models could provide a clinically relevant platform for many cancer studies, maximising the opportunities presented by human tissue resources such as biobanks. A number of key factors limit the wide adoption of non-animal human tissue models in cancer research, including deficiencies in the infrastructure and the technical tools required to collect, transport, store and maintain human tissue for lab use. Another obstacle is the long-standing cultural reliance on animal models, which can make researchers resistant to change, often because of concerns about historical data compatibility and losing ground in a competitive environment while new approaches are embedded in lab practice. There are a wide range of initiatives that aim to address these issues by facilitating data sharing and promoting collaborations between organisations and researchers who work with human tissue. The importance of coordinating biobanks and introducing quality standards is gaining momentum. There is an exciting opportunity to transform cancer drug discovery by optimising the use of human tissue and reducing the reliance on potentially less predictive animal models. © 2017. Published by The Company of Biologists Ltd.

The effects of gravity on human walking: a new test of the dynamic similarity hypothesis using a predictive model.

PubMed

Raichlen, David A

2008-09-01

The dynamic similarity hypothesis (DSH) suggests that differences in animal locomotor biomechanics are due mostly to differences in size. According to the DSH, when the ratios of inertial to gravitational forces are equal between two animals that differ in size [e.g. at equal Froude numbers, where Froude = velocity2/(gravity x hip height)], their movements can be made similar by multiplying all time durations by one constant, all forces by a second constant and all linear distances by a third constant. The DSH has been generally supported by numerous comparative studies showing that as inertial forces differ (i.e. differences in the centripetal force acting on the animal due to variation in hip heights), animals walk with dynamic similarity. However, humans walking in simulated reduced gravity do not walk with dynamically similar kinematics. The simulated gravity experiments did not completely account for the effects of gravity on all body segments, and the importance of gravity in the DSH requires further examination. This study uses a kinematic model to predict the effects of gravity on human locomotion, taking into account both the effects of gravitational forces on the upper body and on the limbs. Results show that dynamic similarity is maintained in altered gravitational environments. Thus, the DSH does account for differences in the inertial forces governing locomotion (e.g. differences in hip height) as well as differences in the gravitational forces governing locomotion.

Defining the optimal animal model for translational research using gene set enrichment analysis.

PubMed

Weidner, Christopher; Steinfath, Matthias; Opitz, Elisa; Oelgeschläger, Michael; Schönfelder, Gilbert

2016-08-01

The mouse is the main model organism used to study the functions of human genes because most biological processes in the mouse are highly conserved in humans. Recent reports that compared identical transcriptomic datasets of human inflammatory diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. To reduce susceptibility to biased interpretation, all genes of interest for the biological question under investigation should be considered. Thus, standardized approaches for systematic data analysis are needed. We analyzed the same datasets using gene set enrichment analysis focusing on pathways assigned to inflammatory processes in either humans or mice. The analyses revealed a moderate overlap between all human and mouse datasets, with average positive and negative predictive values of 48 and 57% significant correlations. Subgroups of the septic mouse models (i.e., Staphylococcus aureus injection) correlated very well with most human studies. These findings support the applicability of targeted strategies to identify the optimal animal model and protocol to improve the success of translational research. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship.

PubMed

Lee, S Hong; Clark, Sam; van der Werf, Julius H J

2017-01-01

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as 'unrelated' individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Ne). Both the effective number of chromosome segments (Me) and Ne are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Predictive control of intersegmental tarsal movements in an insect.

PubMed

Costalago-Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2017-08-01

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Reevaluating the Potential of Mouse Models for the Human Immune System.

PubMed

Jameson, Stephen C; Masopust, David

2018-04-02

Much of what we understand about immunology, including the response to vaccines, come from studies in mice because they provide many practical advantages compared with research in higher mammals and humans. Nevertheless, modalities for preventing or treating disease do not always translate from mouse to humans, which has led to increasing scrutiny of the continued merits of mouse research. Here, we summarize the pros and cons of current laboratory mouse models for immunology research and discuss whether overreliance on nonphysiological, ultra-hygienic animal husbandry approaches has limited the ultimate translation potential of mouse-derived data to humans. Alternative approaches are discussed that may extend the use of the mouse model for preclinical studies. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Does movement behaviour predict population densities? A test with 25 butterfly species.

PubMed

Schultz, Cheryl B; Pe'er, B Guy; Damiani, Christine; Brown, Leone; Crone, Elizabeth E

2017-03-01

Diffusion, which approximates a correlated random walk, has been used by ecologists to describe movement, and forms the basis for many theoretical models. However, it is often criticized as too simple a model to describe animal movement in real populations. We test a key prediction of diffusion models, namely, that animals should be more abundant in land cover classes through which they move more slowly. This relationship between density and diffusion has rarely been tested across multiple species within a given landscape. We estimated diffusion rates and corresponding densities of 25 Israeli butterfly species from flight path data and visual surveys. The data were collected across 19 sites in heterogeneous landscapes with four land cover classes: semi-natural habitat, olive groves, wheat fields and field margins. As expected from theory, species tended to have higher densities in land cover classes through which they moved more slowly and lower densities in land cover classes through which they moved more quickly. Two components of movement (move length and turning angle) were not associated with density, nor was expected net squared displacement. Move time, however, was associated with density, and animals spent more time per move step in areas with higher density. The broad association we document between movement behaviour and density suggests that diffusion is a good first approximation of movement in butterflies. Moreover, our analyses demonstrate that dispersal is not a species-invariant trait, but rather one that depends on landscape context. Thus, land cover classes with high diffusion rates are likely to have low densities and be effective conduits for movement. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Pre-exposure to environmental cues predictive of food availability elicits hypothalamic-pituitary-adrenal axis activation and increases operant responding for food in female rats.

PubMed

Cifani, Carlo; Zanoncelli, Alessandro; Tessari, Michela; Righetti, Claudio; Di Francesco, Carla; Ciccocioppo, Roberto; Massi, Maurizio; Melotto, Sergio

2009-09-01

The present study was undertaken to develop an animal model exploiting food cue-induced increased motivation to obtain food under operant self-administration conditions. To demonstrate the predictive validity of the model, rimonabant, fluoxetine, sibutramine and topiramate, administered 1 hour before the experiment, were tested. For 5 days, female Wistar rats were trained to self-administer standard 45 mg food pellets in one daily session (30 minutes) under FR1 (fixed ratio 1) schedule of reinforcement. Rats were then trained to an FR3 schedule and finally divided into two groups. The first group (control) was subjected to a standard 30 minutes FR3 food self-administration session. The second group was exposed to five presentations of levers and light for 10 seconds each (every 3 minutes in 15 minutes total). At the completion of this pre-session phase, a normal 30-minute session (as in the control group) started. Results showed that pre-exposure to environmental stimuli associated to food deliveries increased response for food when the session started. Corticosterone and adrenocorticotropic hormone plasma levels, measured after the 15-minute pre-exposure, were also significantly increased. No changes were observed for the other measured hormones (growth hormone, prolactin, thyroid-stimulating hormone, luteinizing hormone, insulin, amylin, gastric inhibitor polypeptide, ghrelin, leptin, peptide YY and pancreatic polypeptide). Rimonabant, sibutramine and fluoxetine significantly reduced food intake in both animals pre-exposed and in those not pre-exposed to food-associated cues. Topiramate selectively reduced feeding only in pre-exposed rats. The present study describes the development of a new animal model to investigate cue-induced increased motivation to obtain food. This model shows face and predictive validity, thus, supporting its usefulness in the investigation of new potential treatments of binge-related eating disorders. In addition, the present findings confirm that topiramate may represent an important pharmacotherapeutic approach to binge-related eating.

Genomic Insights into Cardiomyopathies: A Comparative Cross-Species Review

PubMed Central

Simpson, Siobhan; Rutland, Paul; Rutland, Catrin Sian

2017-01-01

In the global human population, the leading cause of non-communicable death is cardiovascular disease. It is predicted that by 2030, deaths attributable to cardiovascular disease will have risen to over 20 million per year. This review compares the cardiomyopathies in both human and non-human animals and identifies the genetic associations for each disorder in each species/taxonomic group. Despite differences between species, advances in human medicine can be gained by utilising animal models of cardiac disease; likewise, gains can be made in animal medicine from human genomic insights. Advances could include undertaking regular clinical checks in individuals susceptible to cardiomyopathy, genetic testing prior to breeding, and careful administration of breeding programmes (in non-human animals), further development of treatment regimes, and drugs and diagnostic techniques. PMID:29056678

75 FR 11834 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-12

... for animal disease spread models. Without this type of data, the ability to detect trends in..., veterinary, and industry reference; (2) Predict or detect national trends in disease emergence and movement.... Description of Respondents: Business or other for-profit. Number of Respondents: 22,243. Frequency of...

Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments

USDA-ARS?s Scientific Manuscript database

Determining the microbial quality of recreational, irrigation and shellfish-harvesting waters is important to ensure compliance with health-related standards and associated legislation. Animal faeces represent a significant human health risk, and concentrations of fecal indicator organisms (FIOs) pr...

Using Quantitative Structure-Activity Relationship Modeling to Quantitatively Predict the Developmental Toxicity of Halogenated Azole compounds

EPA Science Inventory

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity studies remain unavailable for many environmental contaminants due to the complexity and cost of these types of analy...

Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru

It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination ofmore » serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased inflammatory cells and IgE, but the others did not. • The allergic drugs commonly induced germinal center hyperplasia in lymphoid tissues. • Some of these allergic drugs transiently increased CD4{sup +}CD25{sup +} T cells in the spleen.« less

Generation of a Homozygous Transgenic Rat Strain Stably Expressing a Calcium Sensor Protein for Direct Examination of Calcium Signaling.

PubMed

Szebényi, Kornélia; Füredi, András; Kolacsek, Orsolya; Pergel, Enikő; Bősze, Zsuzsanna; Bender, Balázs; Vajdovich, Péter; Tóvári, József; Homolya, László; Szakács, Gergely; Héja, László; Enyedi, Ágnes; Sarkadi, Balázs; Apáti, Ágota; Orbán, Tamás I

2015-08-03

In drug discovery, prediction of selectivity and toxicity require the evaluation of cellular calcium homeostasis. The rat is a preferred laboratory animal for pharmacology and toxicology studies, while currently no calcium indicator protein expressing rat model is available. We established a transgenic rat strain stably expressing the GCaMP2 fluorescent calcium sensor by a transposon-based methodology. Zygotes were co-injected with mRNA of transposase and a CAG-GCaMP2 expressing construct, and animals with one transgene copy were pre-selected by measuring fluorescence in blood cells. A homozygous rat strain was generated with high sensor protein expression in the heart, kidney, liver, and blood cells. No pathological alterations were found in these animals, and fluorescence measurements in cardiac tissue slices and primary cultures demonstrated the applicability of this system for studying calcium signaling. We show here that the GCaMP2 expressing rat cardiomyocytes allow the prediction of cardiotoxic drug side-effects, and provide evidence for the role of Na(+)/Ca(2+) exchanger and its beneficial pharmacological modulation in cardiac reperfusion. Our data indicate that drug-induced alterations and pathological processes can be followed by using this rat model, suggesting that transgenic rats expressing a calcium-sensitive protein provide a valuable system for pharmacological and toxicological studies.

Automatic classification of animal vocalizations

NASA Astrophysics Data System (ADS)

Clemins, Patrick J.

2005-11-01

Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator.

PubMed

Grant, Claire; Ewart, Lorna; Muthas, Daniel; Deavall, Damian; Smith, Simon A; Clack, Glen; Newham, Pete

2016-04-01

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility of integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive "pica" behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. Copyright © 2016 Elsevier Inc. All rights reserved.

A diurnal animation of thermal images from a day-night pair

USGS Publications Warehouse

Watson, K.

2000-01-01

Interpretation of thermal images is often complicated because the physical property information is contained in both the spatial and temporal variations of the data and thermal models are necessary to extract and display this information. A linearized radiative transfer solution to the surface flux has been used to derive a function that is invariant with respect to thermal inertia. This relationship makes it possible to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements (e.g., noon and midnight). An animation can then be constructed from a pair of day-night images to view both the spatial and temporal temperature changes throughout the diurnal cycle. A more complete solution for the invariant function, using the method of Laplace transforms and based on the linearized solution, was introduced. These results indicate that the linear model does not provide a sufficiently accurate estimate. Using standard conditions (latitude 30??, solar declination 0??, acquisition times at noon and midnight), this new relationship was used to predict temperature throughout the diurnal cycle to an rms error of 0.2??C, which is close to the system noise of most thermal scanners. The method was further extended to include the primary effects of topographic slope with similar accuracy. The temperature was computed at 48 equally spaced times in the diurnal cycle with this algorithm using a co-registered day and night TIMS (Thermal Infrared Multispectral Scanner) data pair (330 pixels, 450 lilies) acquired of the Carlin, Nevada, area and a co-registered DEM (Digital Elevation Model). (Any reader can view the results by downloading the animation file from an identified tip site). The results illustrate the power of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences. This 'visual change' approach could significantly increase the use of thermal data for environmental, hazard, and resource studies. Published by Elsevier Science Inc., 2000.A linearized radiative transfer solution of determining the surface flux is proposed to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements. An animation is constructed from a pair of day-night images to view the spatial and temporal temperature changes throughout the diurnal cycle. The results illustrate the effectiveness of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences.

Refinement, Reduction, and Replacement of Animal Toxicity Tests by Computational Methods.

PubMed

Ford, Kevin A

2016-12-01

Widespread public and scientific interest in promoting the care and well-being of animals used for toxicity testing has given rise to improvements in animal welfare practices and views over time, as well as laws and regulations that support means to reduce, refine, and replace animal use (known as the 3Rs) in certain toxicity studies. One way these regulations continue to achieve their aim is by promoting the research, development, and application of alternative testing approaches to characterize potential toxicities either without animals or with minimal use. An important example of an alternative approach is the use of computational toxicology models. Along with the potential capacity to reduce or replace the use of animals for the assessment of particular toxicological endpoints, computational models offer several advantages compared to in vitro and in vivo approaches, including cost-effectiveness, rapid availability of results, and the ability to fully standardize procedures. Pharmaceutical research incorporating the use of computational models has increased steadily over the past 15 years, likely driven by the motivation of companies to screen out toxic compounds in the early stages of development. Models are currently available to aid in the prediction of several important toxicological endpoints, including mutagenicity, carcinogenicity, eye irritation, hepatotoxicity, and skin sensitization, albeit with varying degrees of success. This review serves to introduce the concepts of computational toxicology and evaluate their role in the safety assessment of compounds, while also highlighting the application of in silico methods in the support of the goal and vision of the 3Rs. © The Author 2016. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research.All rights reserved. For permissions, please email: journals.permissions@oup.com.

Animal Models of Bipolar Mania: The Past, Present and Future

PubMed Central

Logan, Ryan W.; McClung, Colleen A.

2015-01-01

Bipolar disorder (BD) is the sixth leading cause of disability in the world according to the World Health Organization and affects nearly 6 million (~2.5% of the population) adults in the United State alone each year. BD is primarily characterized by mood cycling of depressive (e.g., helplessness, reduced energy and activity, and anhedonia) and manic (e.g., increased energy and hyperactivity, reduced need for sleep, impulsivity, reduced anxiety and depression), episodes. The following review describes several animal models of bipolar mania with a focus on more recent findings using genetically modified mice, including several with the potential of investigating the mechanisms underlying ‘mood’ cycling (or behavioral switching in rodents). We discuss whether each of these models satisfy criteria of validity (i.e., face, predictive, and construct), while highlighting their strengths and limitations. Animal models are helping to address critical questions related to pathophysiology of bipolar mania, in an effort to more clearly define necessary targets of first-line medications, lithium and valproic acid, and to discover novel mechanisms with the hope of developing more effective therapeutics. Future studies will leverage new technologies and strategies for integrating animal and human data to reveal important insights into the etiology, pathophysiology, and treatment of BD. PMID:26314632

Reproducibility of preclinical animal research improves with heterogeneity of study samples

PubMed Central

Vogt, Lucile; Sena, Emily S.; Würbel, Hanno

2018-01-01

Single-laboratory studies conducted under highly standardized conditions are the gold standard in preclinical animal research. Using simulations based on 440 preclinical studies across 13 different interventions in animal models of stroke, myocardial infarction, and breast cancer, we compared the accuracy of effect size estimates between single-laboratory and multi-laboratory study designs. Single-laboratory studies generally failed to predict effect size accurately, and larger sample sizes rendered effect size estimates even less accurate. By contrast, multi-laboratory designs including as few as 2 to 4 laboratories increased coverage probability by up to 42 percentage points without a need for larger sample sizes. These findings demonstrate that within-study standardization is a major cause of poor reproducibility. More representative study samples are required to improve the external validity and reproducibility of preclinical animal research and to prevent wasting animals and resources for inconclusive research. PMID:29470495

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Can orchards help connect Mediterranean ecosystems? Animal movement data alter conservation priorities

USGS Publications Warehouse

Nogeire, Theresa M.; Davis, Frank W.; Crooks, Kevin R.; McRae, Brad H.; Lyren, Lisa M.; Boydston, Erin E.

2015-01-01

As natural habitats become fragmented by human activities, animals must increasingly move through human-dominated systems, particularly agricultural landscapes. Mapping areas important for animal movement has therefore become a key part of conservation planning. Models of landscape connectivity are often parameterized using expert opinion and seldom distinguish between the risks and barriers presented by different crop types. Recent research, however, suggests different crop types, such as row crops and orchards, differ in the degree to which they facilitate or impede species movements. Like many mammalian carnivores, bobcats (Lynx rufus) are sensitive to fragmentation and loss of connectivity between habitat patches. We investigated how distinguishing between different agricultural land covers might change conclusions about the relative conservation importance of different land uses in a Mediterranean ecosystem. Bobcats moved relatively quickly in row crops but relatively slowly in orchards, at rates similar to those in natural habitats of woodlands and scrub. We found that parameterizing a connectivity model using empirical data on bobcat movements in agricultural lands and other land covers, instead of parameterizing the model using habitat suitability indices based on expert opinion, altered locations of predicted animal movement routes. These results emphasize that differentiating between types of agriculture can alter conservation planning outcomes.

AnimatLab: a 3D graphics environment for neuromechanical simulations.

PubMed

Cofer, David; Cymbalyuk, Gennady; Reid, James; Zhu, Ying; Heitler, William J; Edwards, Donald H

2010-03-30

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control requires a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. AnimatLab is a software tool that provides an approach to this problem through computer simulation. AnimatLab enables a computational model of an animal's body to be constructed from simple building blocks, situated in a virtual 3D world subject to the laws of physics, and controlled by the activity of a multicellular, multicompartment neural circuit. Sensor receptors on the body surface and inside the body respond to external and internal signals and then excite central neurons, while motor neurons activate Hill muscle models that span the joints and generate movement. AnimatLab provides a common neuromechanical simulation environment in which to construct and test models of any skeletal animal, vertebrate or invertebrate. The use of AnimatLab is demonstrated in a neuromechanical simulation of human arm flexion and the myotactic and contact-withdrawal reflexes. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-10-01

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

The paradoxical effect of low reward probabilities in suboptimal choice.

PubMed

Fortes, Inês; Pinto, Carlos; Machado, Armando; Vasconcelos, Marco

2018-04-01

When offered a choice between 2 alternatives, animals sometimes prefer the option yielding less food. For instance, pigeons and starlings prefer an option that on 20% of the trials presents a stimulus always followed by food, and on the remaining 80% of the trials presents a stimulus never followed by food (the Informative Option), over an option that provides food on 50% of the trials regardless of the stimulus presented (the Noninformative Option). To explain this suboptimal behavior, it has been hypothesized that animals ignore (or do not engage with) the stimulus that is never followed by food in the Informative Option. To assess when pigeons attend to the stimulus usually not followed by food, we increased the probability of reinforcement, p, in the presence of that stimulus. Across 2 experiments, we found that the value of the Informative Option decreased with p. To account for the results, we added to the Reinforcement Rate Model (and also to the Hyperbolic Discounting Model) an engagement function, f(p), that specified the likelihood the animal attends to a stimulus followed by reward with probability p, and then derived the model predictions for 2 forms of f(p), a linear function, and an all-or-none threshold function. Both models predicted the observed findings with a linear engagement function: The higher the probability of reinforcement after a stimulus, the higher the probability of engaging the stimulus, and, surprisingly, the less the value of the option comprising the stimulus. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

ADAPT: The Agent Development and Prototyping Testbed.

PubMed

Shoulson, Alexander; Marshak, Nathan; Kapadia, Mubbasir; Badler, Norman I

2014-07-01

We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. The animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. The navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a character's animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model.

New Breed of Mice May Improve Accuracy for Preclinical Testing of Cancer Drugs | Poster

Cancer.gov

A new breed of lab animals, dubbed “glowing head mice,” may do a better job than conventional mice in predicting the success of experimental cancer drugs—while also helping to meet an urgent need for more realistic preclinical animal models. The mice were developed to tolerate often-used light-emitting molecules, such as luciferase from fireflies and green fluorescent protein (GFP) from jellyfish. These “optical reporters” are useful for monitoring the effect of experimental therapies in live animals over time because they emit an immediate and easily detected light signal showing whether a tumor inside the animal’s body is shrinking as desired.

An assessment of Gallistel's (2012) rationalistic account of extinction phenomena.

PubMed

Miller, Ralph R

2012-05-01

Gallistel (2012) asserts that animals use rationalistic reasoning (i.e., information theory and Bayesian inference) to make decisions that underlie select extinction phenomena. Rational processes are presumed to lead to evolutionarily optimal behavior. Thus, Gallistel's model is a type of optimality theory. But optimality theory is only a theory, a theory about an ideal organism, and its predictions frequently deviate appreciably from observed behavior of animals in the laboratory and the real world. That is, behavior of animals is often far from optimal, as is evident in many behavioral phenomena. Hence, appeals to optimality theory to explain, rather than illuminate, actual behavior are misguided. Copyright © 2012 Elsevier B.V. All rights reserved.

The aerodynamic cost of flight in bats--comparing theory with measurement

NASA Astrophysics Data System (ADS)

von Busse, Rhea; Waldman, Rye M.; Swartz, Sharon M.; Breuer, Kenneth S.

2012-11-01

Aerodynamic theory has long been used to predict the aerodynamic power required for animal flight. However, even though the actuator disk model does not account for the flapping motion of a wing, it is used for lack of any better model. The question remains: how close are these predictions to reality? We designed a study to compare predicted aerodynamic power to measured power from the kinetic energy contained in the wake shed behind a bat flying in a wind tunnel. A high-accuracy displaced light-sheet stereo PIV system was used in the Trefftz plane to capture the wake behind four bats flown over a range of flight speeds (1-6m/s). The total power in the wake was computed from the wake vorticity and these estimates were compared with the power predicted using Pennycuick's model for bird flight as well as estimates derived from measurements of the metabolic cost of flight, previously acquired from the same individuals.

Experimental Diabetes Mellitus in Different Animal Models

PubMed Central

Al-awar, Amin; Veszelka, Médea; Szűcs, Gergő; Attieh, Zouhair; Murlasits, Zsolt; Török, Szilvia; Pósa, Anikó; Varga, Csaba

2016-01-01

Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology and target identification and in the evaluation of novel therapeutic agents and treatments in vivo. Diabetes mellitus disease, commonly known as diabetes, is a group of metabolic disorders characterized by high blood glucose levels for a prolonged time. To avoid late complications of diabetes and related costs, primary prevention and early treatment are therefore necessary. Due to its chronic symptoms, new treatment strategies need to be developed, because of the limited effectiveness of the current therapies. We overviewed the pathophysiological features of diabetes in relation to its complications in type 1 and type 2 mice along with rat models, including Zucker Diabetic Fatty (ZDF) rats, BB rats, LEW 1AR1/-iddm rats, Goto-Kakizaki rats, chemically induced diabetic models, and Nonobese Diabetic mouse, and Akita mice model. The advantages and disadvantages that these models comprise were also addressed in this review. This paper briefly reviews the wide pathophysiological and molecular mechanisms associated with type 1 and type 2 diabetes, particularly focusing on the challenges associated with the evaluation and predictive validation of these models as ideal animal models for preclinical assessments and discovering new drugs and therapeutic agents for translational application in humans. PMID:27595114

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values.

PubMed

Přibyl, J; Bauer, J; Čermák, V; Pešek, P; Přibylová, J; Šplíchal, J; Vostrá-Vydrová, H; Vostrý, L; Zavadilová, L

2015-10-01

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP - Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

Observed and predicted reproduction of Ceriodaphnia dubia exposed to chloride, sulfate, and bicarbonate

USGS Publications Warehouse

Lasier, Peter J.; Hardin, Ian R.

2010-01-01

Chronic toxicities of Cl-, SO42-, and HCO3- to Ceriodaphnia dubia were evaluated in low- and moderate-hardness waters using a three-brood reproduction test method. Toxicity tests of anion mixtures were used to determine interaction effects and to produce models predicting C. dubia reproduction. Effluents diluted with low- and moderate-hardness waters were tested with animals acclimated to low- and moderate-hardness conditions to evaluate the models and to assess the effects of hardness and acclimation. Sulfate was significantly less toxic than Cl- and HCO3- in both types of water. Chloride and HCO3- toxicities were similar in low-hardness water, but HCO3- was the most toxic in moderate-hardness water. Low acute-to-chronic ratios indicate that toxicities of these anions will decrease quickly with dilution. Hardness significantly reduced Cl- and SO42- toxicity but had little effect on HCO3-. Chloride toxicity decreased with an increase in Na+ concentration, and CO3- toxicity may have been reduced by the dissolved organic carbon in effluent. Multivariate models using measured anion concentrations in effluents with low to moderate hardness levels provided fairly accurate predictions of reproduction. Determinations of toxicity for several effluents differed significantly depending on the hardness of the dilution water and the hardness of the water used to culture test animals. These results can be used to predict the contribution of elevated anion concentrations to the chronic toxicity of effluents; to identify effluents that are toxic due to contaminants other than Cl-, SO42-, and HCO3-; and to provide a basis for chemical substitutions in manufacturing processes.

Training set optimization under population structure in genomic selection

USDA-ARS?s Scientific Manuscript database

The optimization of the training set (TRS) in genomic selection (GS) has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the Coefficient of D...

Gene Expression in Developing Brain is Altered by Modest Reductions in Circulating Levels of Thyroid Hormone.

EPA Science Inventory

Disruption of thyroid hormone (TH) homeostasis is a known effect of environmental contaminants. Although animal models of developmental TH deficiency can predict the impact of severe insults to the thyroid system, the effects of moderate TH insufficiencies have not been adequatel...