Integrating a human thermoregulatory model with a clothing model to predict core and skin temperatures.

PubMed

Yang, Jie; Weng, Wenguo; Wang, Faming; Song, Guowen

2017-05-01

This paper aims to integrate a human thermoregulatory model with a clothing model to predict core and skin temperatures. The human thermoregulatory model, consisting of an active system and a passive system, was used to determine the thermoregulation and heat exchanges within the body. The clothing model simulated heat and moisture transfer from the human skin to the environment through the microenvironment and fabric. In this clothing model, the air gap between skin and clothing, as well as clothing properties such as thickness, thermal conductivity, density, porosity, and tortuosity were taken into consideration. The simulated core and mean skin temperatures were compared to the published experimental results of subject tests at three levels of ambient temperatures of 20 °C, 30 °C, and 40 °C. Although lower signal-to-noise-ratio was observed, the developed model demonstrated positive performance at predicting core temperatures with a maximum difference between the simulations and measurements of no more than 0.43 °C. Generally, the current model predicted the mean skin temperatures with reasonable accuracy. It could be applied to predict human physiological responses and assess thermal comfort and heat stress. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

NASA Technical Reports Server (NTRS)

Sebok, Angelia; Wickens, Christopher; Sargent, Robert

2015-01-01

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

PubMed

De Buck, Stefan S; Sinha, Vikash K; Fenu, Luca A; Nijsen, Marjoleen J; Mackie, Claire E; Gilissen, Ron A H J

2007-10-01

The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P(tp)) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V(ss)). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P(tp) data (method Vd2). Best V(ss) predictions were obtained using method Vd2, providing that rat P(tp) input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V(ss) predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

PubMed

Thomas, Reuben; Thomas, Russell S; Auerbach, Scott S; Portier, Christopher J

2013-01-01

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Animal Models and Bone Histomorphometry: Translational Research for the Human Research Program

NASA Technical Reports Server (NTRS)

Sibonga, Jean D.

2010-01-01

This slide presentation reviews the use of animal models to research and inform bone morphology, in particular relating to human research in bone loss as a result of low gravity environments. Reasons for use of animal models as tools for human research programs include: time-efficient, cost-effective, invasive measures, and predictability as some model are predictive for drug effects.

Experimental and numerical study of physiological responses in hot environments.

PubMed

Yang, Jie; Weng, Wenguo; Zhang, Baoting

2014-10-01

This paper proposed a multi-node human thermal model to predict human thermal responses in hot environments. The model was extended based on the Tanabe's work by considering the effects of high temperature on heat production, blood flow rate, and heat exchange coefficients. Five healthy men dressed in shorts were exposed in thermal neutral (29 °C) and high temperature (45 °C) environments. The rectal temperatures and skin temperatures of seven human body segments were continuously measured during the experiment. Validation of this model was conducted with experimental data. The results showed that the current model could accurately predict the skin and core temperatures in terms of the tendency and absolute values. In the human body segments expect calf and trunk, the temperature differences between the experimental data and the predicted results in high temperature environment were smaller than those in the thermally neutral environment conditions. The extended model was proved to be capable of predicting accurately human physiological responses in hot environments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Challenges facing developers of CAD/CAM models that seek to predict human working postures

NASA Astrophysics Data System (ADS)

Wiker, Steven F.

2005-11-01

This paper outlines the need for development of human posture prediction models for Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) design applications in product, facility and work design. Challenges facing developers of posture prediction algorithms are presented and discussed.

Finite element modeling for predicting the contact pressure between a foam mattress and the human body in a supine position.

PubMed

Lee, Wookjin; Won, Byeong Hee; Cho, Seong Wook

2017-01-01

In this paper, we generated finite element (FE) models to predict the contact pressure between a foam mattress and the human body in a supine position. Twenty-year-old males were used for three-dimensional scanning to produce the FE human models, which was composed of skin and muscle tissue. A linear elastic isotropic material model was used for the skin, and the Mooney-Rivlin model was used for the muscle tissue because it can effectively represent the nonlinear behavior of muscle. The contact pressure between the human model and the mattress was predicted by numerical simulation. The human models were validated by comparing the body pressure distribution obtained from the same human subject when he was lying on two different mattress types. The experimental results showed that the slope of the lower part of the mattress caused a decrease in the contact pressure at the heels, and the effect of bone structure was most pronounced in the scapula. After inserting a simple structure to function as the scapula, the contact pressure predicted by the FE human models was consistent with the experimental body pressure distribution for all body parts. These results suggest that the models proposed in this paper will be useful to researchers and designers of products related to the prevention of pressure ulcers.

Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

PubMed

Deng, Li; Wang, Guohua; Chen, Bo

2015-01-01

In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP

PubMed Central

Wang, Guohua; Chen, Bo

2015-01-01

In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency. PMID:26448740

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...

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features

USGS Publications Warehouse

Gieder, Katherina D.; Karpanty, Sarah M.; Fraser, James D.; Catlin, Daniel H.; Gutierrez, Benjamin T.; Plant, Nathaniel G.; Turecek, Aaron M.; Thieler, E. Robert

2014-01-01

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Human-centric predictive model of task difficulty for human-in-the-loop control tasks

PubMed Central

Majewicz Fey, Ann

2018-01-01

Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control. PMID:29621301

Evaluation of Interindividual Human Variation in Bioactivation and DNA Adduct Formation of Estragole in Liver Predicted by Physiologically Based Kinetic/Dynamic and Monte Carlo Modeling.

PubMed

Punt, Ans; Paini, Alicia; Spenkelink, Albertus; Scholz, Gabriele; Schilter, Benoit; van Bladeren, Peter J; Rietjens, Ivonne M C M

2016-04-18

Estragole is a known hepatocarcinogen in rodents at high doses following metabolic conversion to the DNA-reactive metabolite 1'-sulfooxyestragole. The aim of the present study was to model possible levels of DNA adduct formation in (individual) humans upon exposure to estragole. This was done by extending a previously defined PBK model for estragole in humans to include (i) new data on interindividual variation in the kinetics for the major PBK model parameters influencing the formation of 1'-sulfooxyestragole, (ii) an equation describing the relationship between 1'-sulfooxyestragole and DNA adduct formation, (iii) Monte Carlo modeling to simulate interindividual human variation in DNA adduct formation in the population, and (iv) a comparison of the predictions made to human data on DNA adduct formation for the related alkenylbenzene methyleugenol. Adequate model predictions could be made, with the predicted DNA adduct levels at the estimated daily intake of estragole of 0.01 mg/kg bw ranging between 1.6 and 8.8 adducts in 10(8) nucleotides (nts) (50th and 99th percentiles, respectively). This is somewhat lower than values reported in the literature for the related alkenylbenzene methyleugenol in surgical human liver samples. The predicted levels seem to be below DNA adduct levels that are linked with tumor formation by alkenylbenzenes in rodents, which were estimated to amount to 188-500 adducts per 10(8) nts at the BMD10 values of estragole and methyleugenol. Although this does not seem to point to a significant health concern for human dietary exposure, drawing firm conclusions may have to await further validation of the model's predictions.

Development of a physiologically based pharmacokinetic model for assessment of human exposure to bisphenol A

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

Yang, Xiaoxia, E-mail: xiaoxia.yang@fda.hhs.gov; Doerge, Daniel R.; Teeguarden, Justin G.

A previously developed physiologically based pharmacokinetic (PBPK) model for bisphenol A (BPA) in adult rhesus monkeys was modified to characterize the pharmacokinetics of BPA and its phase II conjugates in adult humans following oral ingestion. Coupled with in vitro studies on BPA metabolism in the liver and the small intestine, the PBPK model was parameterized using oral pharmacokinetic data with deuterated-BPA (d{sub 6}-BPA) delivered in cookies to adult humans after overnight fasting. The availability of the serum concentration time course of unconjugated d{sub 6}-BPA offered direct empirical evidence for the calibration of BPA model parameters. The recalibrated PBPK adult humanmore » model for BPA was then evaluated against published human pharmacokinetic studies with BPA. A hypothesis of decreased oral uptake was needed to account for the reduced peak levels observed in adult humans, where d{sub 6}-BPA was delivered in soup and food was provided prior to BPA ingestion, suggesting the potential impact of dosing vehicles and/or fasting on BPA disposition. With the incorporation of Monte Carlo analysis, the recalibrated adult human model was used to address the inter-individual variability in the internal dose metrics of BPA for the U.S. general population. Model-predicted peak BPA serum levels were in the range of pM, with 95% of human variability falling within an order of magnitude. This recalibrated PBPK model for BPA in adult humans provides a scientific basis for assessing human exposure to BPA that can serve to minimize uncertainties incurred during extrapolations across doses and species. - Highlights: • A PBPK model predicts the kinetics of bisphenol A (BPA) in adult humans. • Serum concentrations of aglycone BPA are available for model calibration. • Model predicted peak BPA serum levels for adult humans were in the range of pM. • Model predicted 95% of human variability fell within an order of magnitude.« less

Validating models of target acquisition performance in the dismounted soldier context

NASA Astrophysics Data System (ADS)

Glaholt, Mackenzie G.; Wong, Rachel K.; Hollands, Justin G.

2018-04-01

The problem of predicting real-world operator performance with digital imaging devices is of great interest within the military and commercial domains. There are several approaches to this problem, including: field trials with imaging devices, laboratory experiments using imagery captured from these devices, and models that predict human performance based on imaging device parameters. The modeling approach is desirable, as both field trials and laboratory experiments are costly and time-consuming. However, the data from these experiments is required for model validation. Here we considered this problem in the context of dismounted soldiering, for which detection and identification of human targets are essential tasks. Human performance data were obtained for two-alternative detection and identification decisions in a laboratory experiment in which photographs of human targets were presented on a computer monitor and the images were digitally magnified to simulate range-to-target. We then compared the predictions of different performance models within the NV-IPM software package: Targeting Task Performance (TTP) metric model and the Johnson model. We also introduced a modification to the TTP metric computation that incorporates an additional correction for target angular size. We examined model predictions using NV-IPM default values for a critical model constant, V50, and we also considered predictions when this value was optimized to fit the behavioral data. When using default values, certain model versions produced a reasonably close fit to the human performance data in the detection task, while for the identification task all models substantially overestimated performance. When using fitted V50 values the models produced improved predictions, though the slopes of the performance functions were still shallow compared to the behavioral data. These findings are discussed in relation to the models' designs and parameters, and the characteristics of the behavioral paradigm.

A predictive model for biomimetic plate type broadband frequency sensor

NASA Astrophysics Data System (ADS)

Ahmed, Riaz U.; Banerjee, Sourav

2016-04-01

In this work, predictive model for a bio-inspired broadband frequency sensor is developed. Broadband frequency sensing is essential in many domains of science and technology. One great example of such sensor is human cochlea, where it senses a frequency band of 20 Hz to 20 KHz. Developing broadband sensor adopting the physics of human cochlea has found tremendous interest in recent years. Although few experimental studies have been reported, a true predictive model to design such sensors is missing. A predictive model is utmost necessary for accurate design of selective broadband sensors that are capable of sensing very selective band of frequencies. Hence, in this study, we proposed a novel predictive model for the cochlea-inspired broadband sensor, aiming to select the frequency band and model parameters predictively. Tapered plate geometry is considered mimicking the real shape of the basilar membrane in the human cochlea. The predictive model is intended to develop flexible enough that can be employed in a wide variety of scientific domains. To do that, the predictive model is developed in such a way that, it can not only handle homogeneous but also any functionally graded model parameters. Additionally, the predictive model is capable of managing various types of boundary conditions. It has been found that, using the homogeneous model parameters, it is possible to sense a specific frequency band from a specific portion (B) of the model length (L). It is also possible to alter the attributes of `B' using functionally graded model parameters, which confirms the predictive frequency selection ability of the developed model.

Prediction of skin sensitization potency using machine learning approaches.

PubMed

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

2017-07-01

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

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

Atienzar, Franck A., E-mail: franck.atienzar@ucb.com; Novik, Eric I.; Gerets, Helga H.

Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine modelmore » along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes.« less

Model-based influences on humans' choices and striatal prediction errors.

PubMed

Daw, Nathaniel D; Gershman, Samuel J; Seymour, Ben; Dayan, Peter; Dolan, Raymond J

2011-03-24

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. Copyright © 2011 Elsevier Inc. All rights reserved.

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

NASA Astrophysics Data System (ADS)

Kalayeh, Mahdi M.; Marin, Thibault; Pretorius, P. Hendrik; Wernick, Miles N.; Yang, Yongyi; Brankov, Jovan G.

2011-03-01

In this paper, we present a numerical observer for image quality assessment, aiming to predict human observer accuracy in a cardiac perfusion defect detection task for single-photon emission computed tomography (SPECT). In medical imaging, image quality should be assessed by evaluating the human observer accuracy for a specific diagnostic task. This approach is known as task-based assessment. Such evaluations are important for optimizing and testing imaging devices and algorithms. Unfortunately, human observer studies with expert readers are costly and time-demanding. To address this problem, numerical observers have been developed as a surrogate for human readers to predict human diagnostic performance. The channelized Hotelling observer (CHO) with internal noise model has been found to predict human performance well in some situations, but does not always generalize well to unseen data. We have argued in the past that finding a model to predict human observers could be viewed as a machine learning problem. Following this approach, in this paper we propose a channelized relevance vector machine (CRVM) to predict human diagnostic scores in a detection task. We have previously used channelized support vector machines (CSVM) to predict human scores and have shown that this approach offers better and more robust predictions than the classical CHO method. The comparison of the proposed CRVM with our previously introduced CSVM method suggests that CRVM can achieve similar generalization accuracy, while dramatically reducing model complexity and computation time.

Modelling dimercaptosuccinic acid (DMSA) plasma kinetics in humans.

PubMed

van Eijkeren, Jan C H; Olie, J Daniël N; Bradberry, Sally M; Vale, J Allister; de Vries, Irma; Meulenbelt, Jan; Hunault, Claudine C

2016-11-01

No kinetic models presently exist which simulate the effect of chelation therapy on lead blood concentrations in lead poisoning. Our aim was to develop a kinetic model that describes the kinetics of dimercaptosuccinic acid (DMSA; succimer), a commonly used chelating agent, that could be used in developing a lead chelating model. This was a kinetic modelling study. We used a two-compartment model, with a non-systemic gastrointestinal compartment (gut lumen) and the whole body as one systemic compartment. The only data available from the literature were used to calibrate the unknown model parameters. The calibrated model was then validated by comparing its predictions with measured data from three different experimental human studies. The model predicted total DMSA plasma and urine concentrations measured in three healthy volunteers after ingestion of DMSA 10 mg/kg. The model was then validated by using data from three other published studies; it predicted concentrations within a factor of two, representing inter-human variability. A simple kinetic model simulating the kinetics of DMSA in humans has been developed and validated. The interest of this model lies in the future potential to use it to predict blood lead concentrations in lead-poisoned patients treated with DMSA.

Inductive reasoning about causally transmitted properties.

PubMed

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.

Physiologically Based Pharmacokinetic Model for Terbinafine in Rats and Humans

PubMed Central

Hosseini-Yeganeh, Mahboubeh; McLachlan, Andrew J.

2002-01-01

The aim of this study was to develop a physiologically based pharmacokinetic (PB-PK) model capable of describing and predicting terbinafine concentrations in plasma and tissues in rats and humans. A PB-PK model consisting of 12 tissue and 2 blood compartments was developed using concentration-time data for tissues from rats (n = 33) after intravenous bolus administration of terbinafine (6 mg/kg of body weight). It was assumed that all tissues except skin and testis tissues were well-stirred compartments with perfusion rate limitations. The uptake of terbinafine into skin and testis tissues was described by a PB-PK model which incorporates a membrane permeability rate limitation. The concentration-time data for terbinafine in human plasma and tissues were predicted by use of a scaled-up PB-PK model, which took oral absorption into consideration. The predictions obtained from the global PB-PK model for the concentration-time profile of terbinafine in human plasma and tissues were in close agreement with the observed concentration data for rats. The scaled-up PB-PK model provided an excellent prediction of published terbinafine concentration-time data obtained after the administration of single and multiple oral doses in humans. The estimated volume of distribution at steady state (Vss) obtained from the PB-PK model agreed with the reported value of 11 liters/kg. The apparent volume of distribution of terbinafine in skin and adipose tissues accounted for 41 and 52%, respectively, of the Vss for humans, indicating that uptake into and redistribution from these tissues dominate the pharmacokinetic profile of terbinafine. The PB-PK model developed in this study was capable of accurately predicting the plasma and tissue terbinafine concentrations in both rats and humans and provides insight into the physiological factors that determine terbinafine disposition. PMID:12069977

Numerical modeling of particle generation from ozone reactions with human-worn clothing in indoor environments

NASA Astrophysics Data System (ADS)

Rai, Aakash C.; Lin, Chao-Hsin; Chen, Qingyan

2015-02-01

Ozone-terpene reactions are important sources of indoor ultrafine particles (UFPs), a potential health hazard for human beings. Humans themselves act as possible sites for ozone-initiated particle generation through reactions with squalene (a terpene) that is present in their skin, hair, and clothing. This investigation developed a numerical model to probe particle generation from ozone reactions with clothing worn by humans. The model was based on particle generation measured in an environmental chamber as well as physical formulations of particle nucleation, condensational growth, and deposition. In five out of the six test cases, the model was able to predict particle size distributions reasonably well. The failure in the remaining case demonstrated the fundamental limitations of nucleation models. The model that was developed was used to predict particle generation under various building and airliner cabin conditions. These predictions indicate that ozone reactions with human-worn clothing could be an important source of UFPs in densely occupied classrooms and airliner cabins. Those reactions could account for about 40% of the total UFPs measured on a Boeing 737-700 flight. The model predictions at this stage are indicative and should be improved further.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Human demography and reserve size predict wildlife extinction in West Africa.

PubMed Central

Brashares, J S; Arcese, P; Sam, M K

2001-01-01

Species-area models have become the primary tool used to predict baseline extinction rates for species in isolated habitats, and have influenced conservation and land-use planning worldwide. In particular, these models have been used to predict extinction rates following the loss or fragmentation of natural habitats in the absence of direct human influence on species persistence. Thus, where direct human influences, such as hunting, put added pressure on species in remnant habitat patches, we should expect to observe extinction rates higher than those predicted by simple species-area models. Here, we show that extinction rates for 41 species of large mammals in six nature reserves in West Africa are 14-307 times higher than those predicted by models based on reserve size alone. Human population and reserve size accounted for 98% of the observed variation in extinction rates between reserves. Extinction occurred at higher rates than predicted by species-area models for carnivores, primates and ungulates, and at the highest rates overall near reserve borders. Our results indicate that, where the harvest of wildlife is common, conservation plans should focus on increasing the size of reserves and reducing the rate of hunting. PMID:11747566

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.

Correlation of Wissler Human Thermal Model Blood Flow and Shiver Algorithms

NASA Technical Reports Server (NTRS)

Bue, Grant; Makinen, Janice; Cognata, Thomas

2010-01-01

The Wissler Human Thermal Model (WHTM) is a thermal math model of the human body that has been widely used to predict the human thermoregulatory response to a variety of cold and hot environments. The model has been shown to predict core temperature and skin temperatures higher and lower, respectively, than in tests of subjects in crew escape suit working in a controlled hot environments. Conversely the model predicts core temperature and skin temperatures lower and higher, respectively, than in tests of lightly clad subjects immersed in cold water conditions. The blood flow algorithms of the model has been investigated to allow for more and less flow, respectively, for the cold and hot case. These changes in the model have yielded better correlation of skin and core temperatures in the cold and hot cases. The algorithm for onset of shiver did not need to be modified to achieve good agreement in cold immersion simulations

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.

Mathematical model for predicting human vertebral fracture

NASA Technical Reports Server (NTRS)

Benedict, J. V.

1973-01-01

Mathematical model has been constructed to predict dynamic response of tapered, curved beam columns in as much as human spine closely resembles this form. Model takes into consideration effects of impact force, mass distribution, and material properties. Solutions were verified by dynamic tests on curved, tapered, elastic polyethylene beam.

Rational Design of Mouse Models for Cancer Research.

PubMed

Landgraf, Marietta; McGovern, Jacqui A; Friedl, Peter; Hutmacher, Dietmar W

2018-03-01

The laboratory mouse is widely considered as a valid and affordable model organism to study human disease. Attempts to improve the relevance of murine models for the investigation of human pathologies led to the development of various genetically engineered, xenograft and humanized mouse models. Nevertheless, most preclinical studies in mice suffer from insufficient predictive value when compared with cancer biology and therapy response of human patients. We propose an innovative strategy to improve the predictive power of preclinical cancer models. Combining (i) genomic, tissue engineering and regenerative medicine approaches for rational design of mouse models with (ii) rapid prototyping and computational benchmarking against human clinical data will enable fast and nonbiased validation of newly generated models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combining Modeling and Gaming for Predictive Analytics

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

Riensche, Roderick M.; Whitney, Paul D.

2012-08-22

Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describemore » our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.« less

Foveated model observers to predict human performance in 3D images

NASA Astrophysics Data System (ADS)

Lago, Miguel A.; Abbey, Craig K.; Eckstein, Miguel P.

2017-03-01

We evaluate 3D search requires model observers that take into account the peripheral human visual processing (foveated models) to predict human observer performance. We show that two different 3D tasks, free search and location-known detection, influence the relative human visual detectability of two signals of different sizes in synthetic backgrounds mimicking the noise found in 3D digital breast tomosynthesis. One of the signals resembled a microcalcification (a small and bright sphere), while the other one was designed to look like a mass (a larger Gaussian blob). We evaluated current standard models observers (Hotelling; Channelized Hotelling; non-prewhitening matched filter with eye filter, NPWE; and non-prewhitening matched filter model, NPW) and showed that they incorrectly predict the relative detectability of the two signals in 3D search. We propose a new model observer (3D Foveated Channelized Hotelling Observer) that incorporates the properties of the visual system over a large visual field (fovea and periphery). We show that the foveated model observer can accurately predict the rank order of detectability of the signals in 3D images for each task. Together, these results motivate the use of a new generation of foveated model observers for predicting image quality for search tasks in 3D imaging modalities such as digital breast tomosynthesis or computed tomography.

Coupling of the Models of Human Physiology and Thermal Comfort

NASA Astrophysics Data System (ADS)

Pokorny, J.; Jicha, M.

2013-04-01

A coupled model of human physiology and thermal comfort was developed in Dymola/Modelica. A coupling combines a modified Tanabe model of human physiology and thermal comfort model developed by Zhang. The Coupled model allows predicting the thermal sensation and comfort of both local and overall from local boundary conditions representing ambient and personal factors. The aim of this study was to compare prediction of the Coupled model with the Fiala model prediction and experimental data. Validation data were taken from the literature, mainly from the validation manual of software Theseus-FE [1]. In the paper validation of the model for very light physical activities (1 met) indoor environment with temperatures from 12 °C up to 48 °C is presented. The Coupled model predicts mean skin temperature for cold, neutral and warm environment well. However prediction of core temperature in cold environment is inaccurate and very affected by ambient temperature. Evaluation of thermal comfort in warm environment is supplemented by skin wettedness prediction. The Coupled model is designed for non-uniform and transient environmental conditions; it is also suitable simulation of thermal comfort in vehicles cabins. The usage of the model is limited for very light physical activities up to 1.2 met only.

Outbreaks of Tularemia in a Boreal Forest Region Depends on Mosquito Prevalence

PubMed Central

Rydén, Patrik; Björk, Rafael; Schäfer, Martina L.; Lundström, Jan O.; Petersén, Bodil; Lindblom, Anders; Forsman, Mats; Sjöstedt, Anders

2012-01-01

Background. We aimed to evaluate the potential association of mosquito prevalence in a boreal forest area with transmission of the bacterial disease tularemia to humans, and model the annual variation of disease using local weather data. Methods. A prediction model for mosquito abundance was built using weather and mosquito catch data. Then a negative binomial regression model based on the predicted mosquito abundance and local weather data was built to predict annual numbers of humans contracting tularemia in Dalarna County, Sweden. Results. Three hundred seventy humans were diagnosed with tularemia between 1981 and 2007, 94% of them during 7 summer outbreaks. Disease transmission was concentrated along rivers in the area. The predicted mosquito abundance was correlated (0.41, P < .05) with the annual number of human cases. The predicted mosquito peaks consistently preceded the median onset time of human tularemia (temporal correlation, 0.76; P < .05). Our final predictive model included 5 environmental variables and identified 6 of the 7 outbreaks. Conclusions. This work suggests that a high prevalence of mosquitoes in late summer is a prerequisite for outbreaks of tularemia in a tularemia-endemic boreal forest area of Sweden and that environmental variables can be used as risk indicators. PMID:22124130

Population Physiologically-Based Pharmacokinetic Modeling for the Human Lactational Transfer of PCB 153 with Consideration of Worldwide Human Biomonitoring Results

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

Redding, Laurel E.; Sohn, Michael D.; McKone, Thomas E.

2008-03-01

We developed a physiologically based pharmacokinetic model of PCB 153 in women, and predict its transfer via lactation to infants. The model is the first human, population-scale lactational model for PCB 153. Data in the literature provided estimates for model development and for performance assessment. Physiological parameters were taken from a cohort in Taiwan and from reference values in the literature. We estimated partition coefficients based on chemical structure and the lipid content in various body tissues. Using exposure data in Japan, we predicted acquired body burden of PCB 153 at an average childbearing age of 25 years and comparemore » predictions to measurements from studies in multiple countries. Forward-model predictions agree well with human biomonitoring measurements, as represented by summary statistics and uncertainty estimates. The model successfully describes the range of possible PCB 153 dispositions in maternal milk, suggesting a promising option for back estimating doses for various populations. One example of reverse dosimetry modeling was attempted using our PBPK model for possible exposure scenarios in Canadian Inuits who had the highest level of PCB 153 in their milk in the world.« less

I-TASSER: fully automated protein structure prediction in CASP8.

PubMed

Zhang, Yang

2009-01-01

The I-TASSER algorithm for 3D protein structure prediction was tested in CASP8, with the procedure fully automated in both the Server and Human sections. The quality of the server models is close to that of human ones but the human predictions incorporate more diverse templates from other servers which improve the human predictions in some of the distant homology targets. For the first time, the sequence-based contact predictions from machine learning techniques are found helpful for both template-based modeling (TBM) and template-free modeling (FM). In TBM, although the accuracy of the sequence based contact predictions is on average lower than that from template-based ones, the novel contacts in the sequence-based predictions, which are complementary to the threading templates in the weakly or unaligned regions, are important to improve the global and local packing in these regions. Moreover, the newly developed atomic structural refinement algorithm was tested in CASP8 and found to improve the hydrogen-bonding networks and the overall TM-score, which is mainly due to its ability of removing steric clashes so that the models can be generated from cluster centroids. Nevertheless, one of the major issues of the I-TASSER pipeline is the model selection where the best models could not be appropriately recognized when the correct templates are detected only by the minority of the threading algorithms. There are also problems related with domain-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM-based structure predictions. Copyright 2009 Wiley-Liss, Inc.

A Conceptual Framework for Predicting Error in Complex Human-Machine Environments

NASA Technical Reports Server (NTRS)

Freed, Michael; Remington, Roger; Null, Cynthia H. (Technical Monitor)

1998-01-01

We present a Goals, Operators, Methods, and Selection Rules-Model Human Processor (GOMS-MHP) style model-based approach to the problem of predicting human habit capture errors. Habit captures occur when the model fails to allocate limited cognitive resources to retrieve task-relevant information from memory. Lacking the unretrieved information, decision mechanisms act in accordance with implicit default assumptions, resulting in error when relied upon assumptions prove incorrect. The model helps interface designers identify situations in which such failures are especially likely.

Predictive discomfort in single- and combined-axis whole-body vibration considering different seated postures.

PubMed

DeShaw, Jonathan; Rahmatalla, Salam

2014-08-01

The aim of this study was to develop a predictive discomfort model in single-axis, 3-D, and 6-D combined-axis whole-body vibrations of seated occupants considering different postures. Non-neutral postures in seated whole-body vibration play a significant role in the resulting level of perceived discomfort and potential long-term injury. The current international standards address contact points but not postures. The proposed model computes discomfort on the basis of static deviation of human joints from their neutral positions and how fast humans rotate their joints under vibration. Four seated postures were investigated. For practical implications, the coefficients of the predictive discomfort model were changed into the Borg scale with psychophysical data from 12 volunteers in different vibration conditions (single-axis random fore-aft, lateral, and vertical and two magnitudes of 3-D). The model was tested under two magnitudes of 6-D vibration. Significant correlations (R = .93) were found between the predictive discomfort model and the reported discomfort with different postures and vibrations. The ISO 2631-1 correlated very well with discomfort (R2 = .89) but was not able to predict the effect of posture. Human discomfort in seated whole-body vibration with different non-neutral postures can be closely predicted by a combination of static posture and the angular velocities of the joint. The predictive discomfort model can assist ergonomists and human factors researchers design safer environments for seated operators under vibration. The model can be integrated with advanced computer biomechanical models to investigate the complex interaction between posture and vibration.

Universal predictability of mobility patterns in cities

PubMed Central

Yan, Xiao-Yong; Zhao, Chen; Fan, Ying; Di, Zengru; Wang, Wen-Xu

2014-01-01

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements. PMID:25232053

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

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

PubMed

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

2016-01-01

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

The prediction of human skin responses by using the combined in vitro fluorescein leakage/Alamar Blue (resazurin) assay.

PubMed

Clothier, Richard; Starzec, Gemma; Pradel, Lionel; Baxter, Victoria; Jones, Melanie; Cox, Helen; Noble, Linda

2002-01-01

A range of cosmetics formulations with human patch-test data were supplied in a coded form, for the examination of the use of a combined in vitro permeability barrier assay and cell viability assay to generate, and then test, a prediction model for assessing potential human skin patch-test results. The target cells employed were of the Madin Darby canine kidney cell line, which establish tight junctions and adherens junctions able to restrict the permeability of sodium fluorescein across the barrier of the confluent cell layer. The prediction model for interpretation of the in vitro assay results included initial effects and the recovery profile over 72 hours. A set of the hand-wash, surfactant-based formulations were tested to generate the prediction model, and then six others were evaluated. The model system was then also evaluated with powder laundry detergents and hand moisturisers: their effects were predicted by the in vitro test system. The model was under-predictive for two of the ten hand-wash products. It was over-predictive for the moisturisers, (two out of six) and eight out of ten laundry powders. However, the in vivo human patch test data were variable, and 19 of the 26 predictions were correct or within 0.5 on the 0-4.0 scale used for the in vivo scores, i.e. within the same variable range reported for the repeat-test hand-wash in vivo data.

Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

PubMed

Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

2016-10-01

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

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

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait.

PubMed

Torricelli, Diego; Cortés, Camilo; Lete, Nerea; Bertelsen, Álvaro; Gonzalez-Vargas, Jose E; Del-Ama, Antonio J; Dimbwadyo, Iris; Moreno, Juan C; Florez, Julian; Pons, Jose L

2018-01-01

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

PubMed Central

Torricelli, Diego; Cortés, Camilo; Lete, Nerea; Bertelsen, Álvaro; Gonzalez-Vargas, Jose E.; del-Ama, Antonio J.; Dimbwadyo, Iris; Moreno, Juan C.; Florez, Julian; Pons, Jose L.

2018-01-01

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton. PMID:29755336

Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model

PubMed Central

2018-01-01

Abstract Human observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modeled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the question of whether and how the human brain tracks continuous temporal hazard on a moment-to-moment basis, and how flexibly it adjusts to strictly implicit variations in the hazard function. We applied an encoding-model approach to human electroencephalographic data recorded during a pitch-discrimination task, in which we implicitly manipulated temporal predictability of the target tones by varying the interval between cue and target tone (i.e. the foreperiod). Critically, temporal predictability either was driven solely by the passage of time (resulting in a monotonic hazard function) or was modulated to increase at intermediate foreperiods (resulting in a modulated hazard function with a peak at the intermediate foreperiod). Forward-encoding models trained to predict the recorded EEG signal from different temporal hazard functions were able to distinguish between experimental conditions, showing that implicit variations of temporal hazard bear tractable signatures in the human electroencephalogram. Notably, this tracking signal was reconstructed best from the supplementary motor area, underlining this area’s link to cognitive processing of time. Our results underline the relevance of temporal hazard to cognitive processing and show that the predictive accuracy of the encoding-model approach can be utilized to track abstract time-resolved stimuli. PMID:29740594

Genetic determinants of freckle occurrence in the Spanish population: Towards ephelides prediction from human DNA samples.

PubMed

Hernando, Barbara; Ibañez, Maria Victoria; Deserio-Cuesta, Julio Alberto; Soria-Navarro, Raquel; Vilar-Sastre, Inca; Martinez-Cadenas, Conrado

2018-03-01

Prediction of human pigmentation traits, one of the most differentiable externally visible characteristics among individuals, from biological samples represents a useful tool in the field of forensic DNA phenotyping. In spite of freckling being a relatively common pigmentation characteristic in Europeans, little is known about the genetic basis of this largely genetically determined phenotype in southern European populations. In this work, we explored the predictive capacity of eight freckle and sunlight sensitivity-related genes in 458 individuals (266 non-freckled controls and 192 freckled cases) from Spain. Four loci were associated with freckling (MC1R, IRF4, ASIP and BNC2), and female sex was also found to be a predictive factor for having a freckling phenotype in our population. After identifying the most informative genetic variants responsible for human ephelides occurrence in our sample set, we developed a DNA-based freckle prediction model using a multivariate regression approach. Once developed, the capabilities of the prediction model were tested by a repeated 10-fold cross-validation approach. The proportion of correctly predicted individuals using the DNA-based freckle prediction model was 74.13%. The implementation of sex into the DNA-based freckle prediction model slightly improved the overall prediction accuracy by 2.19% (76.32%). Further evaluation of the newly-generated prediction model was performed by assessing the model's performance in a new cohort of 212 Spanish individuals, reaching a classification success rate of 74.61%. Validation of this prediction model may be carried out in larger populations, including samples from different European populations. Further research to validate and improve this newly-generated freckle prediction model will be needed before its forensic application. Together with DNA tests already validated for eye and hair colour prediction, this freckle prediction model may lead to a substantially more detailed physical description of unknown individuals from DNA found at the crime scene. Copyright © 2017 Elsevier B.V. All rights reserved.

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

PubMed

Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

2005-09-01

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Physiologically based pharmacokinetic modeling of tea catechin mixture in rats and humans.

PubMed

Law, Francis C P; Yao, Meicun; Bi, Hui-Chang; Lam, Stephen

2017-06-01

Although green tea ( Camellia sinensis) (GT) contains a large number of polyphenolic compounds with anti-oxidative and anti-proliferative activities, little is known of the pharmacokinetics and tissue dose of tea catechins (TCs) as a chemical mixture in humans. The objectives of this study were to develop and validate a physiologically based pharmacokinetic (PBPK) model of tea catechin mixture (TCM) in rats and humans, and to predict an integrated or total concentration of TCM in the plasma of humans after consuming GT or Polyphenon E (PE). To this end, a PBPK model of epigallocatechin gallate (EGCg) consisting of 13 first-order, blood flow-limited tissue compartments was first developed in rats. The rat model was scaled up to humans by replacing its physiological parameters, pharmacokinetic parameters and tissue/blood partition coefficients (PCs) with human-specific values. Both rat and human EGCg models were then extrapolated to other TCs by substituting its physicochemical parameters, pharmacokinetic parameters, and PCs with catechin-specific values. Finally, a PBPK model of TCM was constructed by linking three rat (or human) tea catechin models together without including a description for pharmacokinetic interaction between the TCs. The mixture PBPK model accurately predicted the pharmacokinetic behaviors of three individual TCs in the plasma of rats and humans after GT or PE consumption. Model-predicted total TCM concentration in the plasma was linearly related to the dose consumed by humans. The mixture PBPK model is able to translate an external dose of TCM into internal target tissue doses for future safety assessment and dose-response analysis studies in humans. The modeling framework as described in this paper is also applicable to the bioactive chemical in other plant-based health products.

Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents

PubMed Central

Collins, Michael G.; Juvina, Ion; Gluck, Kevin A.

2016-01-01

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair. PMID:26903892

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

A model of the human in a cognitive prediction task.

NASA Technical Reports Server (NTRS)

Rouse, W. B.

1973-01-01

The human decision maker's behavior when predicting future states of discrete linear dynamic systems driven by zero-mean Gaussian processes is modeled. The task is on a slow enough time scale that physiological constraints are insignificant compared with cognitive limitations. The model is basically a linear regression system identifier with a limited memory and noisy observations. Experimental data are presented and compared to the model.

PREDICTING POPULATION EXPOSURES TO PM: THE IMPORTANCE OF MICROENVIRONMENTAL CONCENTRATIONS AND HUMAN ACTIVITIES

EPA Science Inventory

The Stochastic Human Exposure and Dose Simulation (SHEDS) models being developed by the US EPA/NERL use a probabilistic approach to predict population exposures to pollutants. The SHEDS model for particulate matter (SHEDS-PM) estimates the population distribution of PM exposure...

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

PubMed Central

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research. PMID:29599739

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

PubMed

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

Model for Predicting the Performance of Planetary Suit Hip Bearing Designs

NASA Technical Reports Server (NTRS)

Cowley, Matthew S.; Margerum, Sarah; Hharvill, Lauren; Rajulu, Sudhakar

2012-01-01

Designing a space suit is very complex and often requires difficult trade-offs between performance, cost, mass, and system complexity. During the development period of the suit numerous design iterations need to occur before the hardware meets human performance requirements. Using computer models early in the design phase of hardware development is advantageous, by allowing virtual prototyping to take place. A virtual design environment allows designers to think creatively, exhaust design possibilities, and study design impacts on suit and human performance. A model of the rigid components of the Mark III Technology Demonstrator Suit (planetary-type space suit) and a human manikin were created and tested in a virtual environment. The performance of the Mark III hip bearing model was first developed and evaluated virtually by comparing the differences in mobility performance between the nominal bearing configurations and modified bearing configurations. Suited human performance was then simulated with the model and compared to actual suited human performance data using the same bearing configurations. The Mark III hip bearing model was able to visually represent complex bearing rotations and the theoretical volumetric ranges of motion in three dimensions. The model was also able to predict suited human hip flexion and abduction maximums to within 10% of the actual suited human subject data, except for one modified bearing condition in hip flexion which was off by 24%. Differences between the model predictions and the human subject performance data were attributed to the lack of joint moment limits in the model, human subject fitting issues, and the limited suit experience of some of the subjects. The results demonstrate that modeling space suit rigid segments is a feasible design tool for evaluating and optimizing suited human performance. Keywords: space suit, design, modeling, performance

Model-based influences on humans’ choices and striatal prediction errors

PubMed Central

Daw, Nathaniel D.; Gershman, Samuel J.; Seymour, Ben; Dayan, Peter; Dolan, Raymond J.

2011-01-01

Summary The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making. PMID:21435563

An age-specific biokinetic model for iodine

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

Leggett, Richard Wayne

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

An age-specific biokinetic model for iodine

DOE PAGES

Leggett, Richard Wayne

2017-10-26

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

Action perception as hypothesis testing.

PubMed

Donnarumma, Francesco; Costantini, Marcello; Ambrosini, Ettore; Friston, Karl; Pezzulo, Giovanni

2017-04-01

We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions - and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of biodiversity hotspots in the Anthropocene: The case of veteran oaks.

PubMed

Skarpaas, Olav; Blumentrath, Stefan; Evju, Marianne; Sverdrup-Thygeson, Anne

2017-10-01

Over the past centuries, humans have transformed large parts of the biosphere, and there is a growing need to understand and predict the distribution of biodiversity hotspots influenced by the presence of humans. Our basic hypothesis is that human influence in the Anthropocene is ubiquitous, and we predict that biodiversity hot spot modeling can be improved by addressing three challenges raised by the increasing ecological influence of humans: (i) anthropogenically modified responses to individual ecological factors, (ii) fundamentally different processes and predictors in landscape types shaped by different land use histories and (iii) a multitude and complexity of natural and anthropogenic processes that may require many predictors and even multiple models in different landscape types. We modeled the occurrence of veteran oaks in Norway, and found, in accordance with our basic hypothesis and predictions, that humans influence the distribution of veteran oaks throughout its range, but in different ways in forests and open landscapes. In forests, geographical and topographic variables related to the oak niche are still important, but the occurrence of veteran oaks is shifted toward steeper slopes, where logging is difficult. In open landscapes, land cover variables are more important, and veteran oaks are more common toward the north than expected from the fundamental oak niche. In both landscape types, multiple predictor variables representing ecological and human-influenced processes were needed to build a good model, and several models performed almost equally well. Models accounting for the different anthropogenic influences on landscape structure and processes consistently performed better than models based exclusively on natural biogeographical and ecological predictors. Thus, our results for veteran oaks clearly illustrate the challenges to distribution modeling raised by the ubiquitous influence of humans, even in a moderately populated region, but also show that predictions can be improved by explicitly addressing these anthropogenic complexities.

Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans

EPA Pesticide Factsheets

Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans (Presented by James McKim, Ph.D., DABT, Founder and Chief Science Officer, CeeTox) (5/25/2012)

REVIEW OF NUMERICAL MODELS FOR PREDICTING THE ENERGY DEPOSITION AND RESULTANT THERMAL RESPONSE OF HUMANS EXPOSED TO ELECTROMAGNETIC FIELDS

EPA Science Inventory

For humans exposed to electromagnetic (EM) radiation, the resulting thermophysiologic response is not well understood. Because it is unlikely that this information will be determined from quantitative experimentation, it is necessary to develop theoretical models which predict th...

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.

Review of Nearshore Morphologic Prediction

NASA Astrophysics Data System (ADS)

Plant, N. G.; Dalyander, S.; Long, J.

2014-12-01

The evolution of the world's erodible coastlines will determine the balance between the benefits and costs associated with human and ecological utilization of shores, beaches, dunes, barrier islands, wetlands, and estuaries. So, we would like to predict coastal evolution to guide management and planning of human and ecological response to coastal changes. After decades of research investment in data collection, theoretical and statistical analysis, and model development we have a number of empirical, statistical, and deterministic models that can predict the evolution of the shoreline, beaches, dunes, and wetlands over time scales of hours to decades, and even predict the evolution of geologic strata over the course of millennia. Comparisons of predictions to data have demonstrated that these models can have meaningful predictive skill. But these comparisons also highlight the deficiencies in fundamental understanding, formulations, or data that are responsible for prediction errors and uncertainty. Here, we review a subset of predictive models of the nearshore to illustrate tradeoffs in complexity, predictive skill, and sensitivity to input data and parameterization errors. We identify where future improvement in prediction skill will result from improved theoretical understanding, and data collection, and model-data assimilation.

Metabolome of human gut microbiome is predictive of host dysbiosis.

PubMed

Larsen, Peter E; Dai, Yang

2015-01-01

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependentmore » on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE PAGES

Larsen, Peter E.; Dai, Yang

2015-09-14

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

PubMed Central

Blais, Edik M.; Rawls, Kristopher D.; Dougherty, Bonnie V.; Li, Zhuo I.; Kolling, Glynis L.; Ye, Ping; Wallqvist, Anders; Papin, Jason A.

2017-01-01

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications. PMID:28176778

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

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

Saylor, Kyle, E-mail: saylor@vt.edu; Zhang, Chenmi

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down intomore » different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. - Highlights: • Modelling of nicotine disposition in the presence of anti-nicotine antibodies • Key vaccine efficacy factors are evaluated in silico in rats and in humans. • Model predicts insufficient antibody binding in past human nicotine vaccines. • Improving immunogenicity and antibody specificity may lead to vaccine success.« less

Corneal cell culture models: a tool to study corneal drug absorption.

PubMed

Dey, Surajit

2011-05-01

In recent times, there has been an ever increasing demand for ocular drugs to treat sight threatening diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. As more drugs are developed, there is a great need to test in vitro permeability of these drugs to predict their efficacy and bioavailability in vivo. Corneal cell culture models are the only tool that can predict drug absorption across ocular layers accurately and rapidly. Cell culture studies are also valuable in reducing the number of animals needed for in vivo studies which can increase the cost of the drug developmental process. Currently, rabbit corneal cell culture models are used to predict human corneal absorption due to the difficulty in human corneal studies. More recently, a three dimensional human corneal equivalent has been developed using three different cell types to mimic the human cornea. In the future, human corneal cell culture systems need to be developed to be used as a standardized model for drug permeation.

Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC.

PubMed

Figueroa, Isabel; Leipold, Doug; Leong, Steve; Zheng, Bing; Triguero-Carrasco, Montserrat; Fourie-O'Donohue, Aimee; Kozak, Katherine R; Xu, Keyang; Schutten, Melissa; Wang, Hong; Polson, Andrew G; Kamath, Amrita V

2018-05-14

For antibody-drug conjugates (ADCs) that carry a cytotoxic drug, doses that can be administered in preclinical studies are typically limited by tolerability, leading to a narrow dose range that can be tested. For molecules with non-linear pharmacokinetics (PK), this limited dose range may be insufficient to fully characterize the PK of the ADC and limits translation to humans. Mathematical PK models are frequently used for molecule selection during preclinical drug development and for translational predictions to guide clinical study design. Here, we present a practical approach that uses limited PK and receptor occupancy (RO) data of the corresponding unconjugated antibody to predict ADC PK when conjugation does not alter the non-specific clearance or the antibody-target interaction. We used a 2-compartment model incorporating non-specific and specific (target mediated) clearances, where the latter is a function of RO, to describe the PK of anti-CD33 ADC with dose-limiting neutropenia in cynomolgus monkeys. We tested our model by comparing PK predictions based on the unconjugated antibody to observed ADC PK data that was not utilized for model development. Prospective prediction of human PK was performed by incorporating in vitro binding affinity differences between species for varying levels of CD33 target expression. Additionally, this approach was used to predict human PK of other previously tested anti-CD33 molecules with published clinical data. The findings showed that, for a cytotoxic ADC with non-linear PK and limited preclinical PK data, incorporating RO in the PK model and using data from the corresponding unconjugated antibody at higher doses allowed the identification of parameters to characterize monkey PK and enabled human PK predictions.

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.

Evaluation of internal noise methods for Hotelling observers

NASA Astrophysics Data System (ADS)

Zhang, Yani; Pham, Binh T.; Eckstein, Miguel P.

2005-04-01

Including internal noise in computer model observers to degrade model observer performance to human levels is a common method to allow for quantitatively comparisons of human and model performance. In this paper, we studied two different types of methods for injecting internal noise to Hotelling model observers. The first method adds internal noise to the output of the individual channels: a) Independent non-uniform channel noise, b) Independent uniform channel noise. The second method adds internal noise to the decision variable arising from the combination of channel responses: a) internal noise standard deviation proportional to decision variable's standard deviation due to the external noise, b) internal noise standard deviation proportional to decision variable's variance caused by the external noise. We tested the square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO). The studied task was detection of a filling defect of varying size/shape in one of four simulated arterial segment locations with real x-ray angiography backgrounds. Results show that the internal noise method that leads to the best prediction of human performance differs across the studied models observers. The CHO model best predicts human observer performance with the channel internal noise. The HO and LGHO best predict human observer performance with the decision variable internal noise. These results might help explain why previous studies have found different results on the ability of each Hotelling model to predict human performance. Finally, the present results might guide researchers with the choice of method to include internal noise into their Hotelling models.

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

PubMed Central

Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

2015-01-01

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

A contrast-sensitive channelized-Hotelling observer to predict human performance in a detection task using lumpy backgrounds and Gaussian signals

NASA Astrophysics Data System (ADS)

Park, Subok; Badano, Aldo; Gallas, Brandon D.; Myers, Kyle J.

2007-03-01

Previously, a non-prewhitening matched filter (NPWMF) incorporating a model for the contrast sensitivity of the human visual system was introduced for modeling human performance in detection tasks with different viewing angles and white-noise backgrounds by Badano et al. But NPWMF observers do not perform well detection tasks involving complex backgrounds since they do not account for random backgrounds. A channelized-Hotelling observer (CHO) using difference-of-Gaussians (DOG) channels has been shown to track human performance well in detection tasks using lumpy backgrounds. In this work, a CHO with DOG channels, incorporating the model of the human contrast sensitivity, was developed similarly. We call this new observer a contrast-sensitive CHO (CS-CHO). The Barten model was the basis of our human contrast sensitivity model. A scalar was multiplied to the Barten model and varied to control the thresholding effect of the contrast sensitivity on luminance-valued images and hence the performance-prediction ability of the CS-CHO. The performance of the CS-CHO was compared to the average human performance from the psychophysical study by Park et al., where the task was to detect a known Gaussian signal in non-Gaussian distributed lumpy backgrounds. Six different signal-intensity values were used in this study. We chose the free parameter of our model to match the mean human performance in the detection experiment at the strongest signal intensity. Then we compared the model to the human at five different signal-intensity values in order to see if the performance of the CS-CHO matched human performance. Our results indicate that the CS-CHO with the chosen scalar for the contrast sensitivity predicts human performance closely as a function of signal intensity.

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.

The Use of Behavior Models for Predicting Complex Operations

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2010-01-01

Modeling and simulation (M&S) plays an important role when complex human-system notions are being proposed, developed and tested within the system design process. National Aeronautics and Space Administration (NASA) as an agency uses many different types of M&S approaches for predicting human-system interactions, especially when it is early in the development phase of a conceptual design. NASA Ames Research Center possesses a number of M&S capabilities ranging from airflow, flight path models, aircraft models, scheduling models, human performance models (HPMs), and bioinformatics models among a host of other kinds of M&S capabilities that are used for predicting whether the proposed designs will benefit the specific mission criteria. The Man-Machine Integration Design and Analysis System (MIDAS) is a NASA ARC HPM software tool that integrates many models of human behavior with environment models, equipment models, and procedural / task models. The challenge to model comprehensibility is heightened as the number of models that are integrated and the requisite fidelity of the procedural sets are increased. Model transparency is needed for some of the more complex HPMs to maintain comprehensibility of the integrated model performance. This will be exemplified in a recent MIDAS v5 application model and plans for future model refinements will be presented.

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.

A Physiologically-Based Description of the Inhalation Pharmacokinetics of Styrene in Rats and Humans

DTIC Science & Technology

1983-01-01

model for rat were scaled to give a description of human kinetics and the predictions agreed closely with available data from the literature (Fig. 4...for predicting human kinetics from a data base in other mammalian species. The ability to anticipate kinetic behavior in humans could very much improve

Application of simple mathematical expressions to relate the half-lives of xenobiotics in rats to values in humans.

PubMed

Ward, Keith W; Erhardt, Paul; Bachmann, Kenneth

2005-01-01

Previous publications from GlaxoSmithKline and University of Toledo laboratories convey our independent attempts to predict the half-lives of xenobiotics in humans using data obtained from rats. The present investigation was conducted to compare the performance of our published models against a common dataset obtained by merging the two sets of rat versus human half-life (hHL) data previously used by each laboratory. After combining data, mathematical analyses were undertaken by deploying both of our previous models, namely the use of an empirical algorithm based on a best-fit model and the use of rat-to-human liver blood flow ratios as a half-life correction factor. Both qualitative and quantitative analyses were performed, as well as evaluation of the impact of molecular properties on predictability. The merged dataset was remarkably diverse with respect to physiochemical and pharmacokinetic (PK) properties. Application of both models revealed similar predictability, depending upon the measure of stipulated accuracy. Certain molecular features, particularly rotatable bond count and pK(a), appeared to influence the accuracy of prediction. This collaborative effort has resulted in an improved understanding and appreciation of the value of rats to serve as a surrogate for the prediction of xenobiotic half-lives in humans when clinical pharmacokinetic studies are not possible or practicable.

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

Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

PubMed

Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

2017-09-01

Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Spatiotemporal property and predictability of large-scale human mobility

NASA Astrophysics Data System (ADS)

Zhang, Hai-Tao; Zhu, Tao; Fu, Dongfei; Xu, Bowen; Han, Xiao-Pu; Chen, Duxin

2018-04-01

Spatiotemporal characteristics of human mobility emerging from complexity on individual scale have been extensively studied due to the application potential on human behavior prediction and recommendation, and control of epidemic spreading. We collect and investigate a comprehensive data set of human activities on large geographical scales, including both websites browse and mobile towers visit. Numerical results show that the degree of activity decays as a power law, indicating that human behaviors are reminiscent of scale-free random walks known as Lévy flight. More significantly, this study suggests that human activities on large geographical scales have specific non-Markovian characteristics, such as a two-segment power-law distribution of dwelling time and a high possibility for prediction. Furthermore, a scale-free featured mobility model with two essential ingredients, i.e., preferential return and exploration, and a Gaussian distribution assumption on the exploration tendency parameter is proposed, which outperforms existing human mobility models under scenarios of large geographical scales.

Local air gap thickness and contact area models for realistic simulation of human thermo-physiological response

NASA Astrophysics Data System (ADS)

Psikuta, Agnes; Mert, Emel; Annaheim, Simon; Rossi, René M.

2018-02-01

To evaluate the quality of new energy-saving and performance-supporting building and urban settings, the thermal sensation and comfort models are often used. The accuracy of these models is related to accurate prediction of the human thermo-physiological response that, in turn, is highly sensitive to the local effect of clothing. This study aimed at the development of an empirical regression model of the air gap thickness and the contact area in clothing to accurately simulate human thermal and perceptual response. The statistical model predicted reliably both parameters for 14 body regions based on the clothing ease allowances. The effect of the standard error in air gap prediction on the thermo-physiological response was lower than the differences between healthy humans. It was demonstrated that currently used assumptions and methods for determination of the air gap thickness can produce a substantial error for all global, mean, and local physiological parameters, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation, and comfort. Thus, this model may help researchers to strive for improvement of human thermal comfort, health, productivity, safety, and overall sense of well-being with simultaneous reduction of energy consumption and costs in built environment.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study.

PubMed

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-03-27

Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1-7.2. Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1-7.2 is most promising.

Development and application of a multiroute physiologically based pharmacokinetic model for oxytetracycline in dogs and humans.

PubMed

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

2015-01-01

Oxytetracycline (OTC) is a commonly used tetracycline antibiotic in veterinary and human medicine. To establish a quantitative model for predicting OTC plasma and tissue exposure, a permeability-limited multiroute physiologically based pharmacokinetic model was developed in dogs. The model was calibrated with plasma pharmacokinetic data in beagle dogs following single intravenous (5 mg/kg), oral (100 mg/kg), and intramuscular (20 mg/kg) administrations. The model predicted other available dog data well, including drug concentrations in the liver, kidney, and muscle after repeated exposure, and data in the mixed-breed dog. The model was extrapolated to humans and the human model adequately simulated measured plasma OTC concentrations after intravenous (7.14 mg/kg) and oral exposures (6.67 mg/kg). The dog model was applied to predict 24-h OTC area-under-the-curve after three therapeutic treatments. Results were 27.75, 51.76, and 64.17 μg/mL*h in the plasma, and 120.93, 225.64, and 279.67 μg/mL*h in the kidney for oral (100 mg/kg), intravenous (10 mg/kg), and intramuscular (20 mg/kg) administrations, respectively. This model can be used to predict plasma and tissue concentrations to aid in designing optimal therapeutic regimens with OTC in veterinary, and potentially, human medicine; and as a foundation for scaling to other tetracycline antibiotics and to other animal species. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:233-243, 2015. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2017-01-01

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

Modeling the human invader in the United States

USGS Publications Warehouse

Stohlgren, Thomas J.; Jarnevich, Catherine S.; Giri, Chandra P.

2010-01-01

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km2) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km2> cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km2 > cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km2> cell is urban), while we predict that 24.5% of 1-km2> cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

A two-layer composite model of the vocal fold lamina propria for fundamental frequency regulation.

PubMed

Zhang, Kai; Siegmund, Thomas; Chan, Roger W

2007-08-01

The mechanical properties of the vocal fold lamina propria, including the vocal fold cover and the vocal ligament, play an important role in regulating the fundamental frequency of human phonation. This study examines the equilibrium hyperelastic tensile deformation behavior of cover and ligament specimens isolated from excised human larynges. Ogden's hyperelastic model is used to characterize the tensile stress-stretch behaviors at equilibrium. Several statistically significant differences in the mechanical response differentiating cover and ligament, as well as gender are found. Fundamental frequencies are predicted from a string model and a beam model, both accounting for the cover and the ligament. The beam model predicts nonzero F(0) for the unstretched state of the vocal fold. It is demonstrated that bending stiffness significantly contributes to the predicted F(0), with the ligament contributing to a higher F(0), especially in females. Despite the availability of only a small data set, the model predicts an age dependence of F(0) in males in agreement with experimental findings. Accounting for two mechanisms of fundamental frequency regulation--vocal fold posturing (stretching) and extended clamping--brings predicted F(0) close to the lower bound of the human phonatory range. Advantages and limitations of the current model are discussed.

INTEGRATING EARTH OBSERVATION AND FIELD DATA INTO A LYME DISEASE MODEL TO MAP AND PREDICT RISKS TO BIODIVERSITY AND HUMAN HEALTH

EPA Science Inventory

DW-75-92243901
Title: Integrating Earth Observation and Field Data into a Lyme Disease Model to Map and Predict Risks to Biodiversity and Human HealthDurland Fish, Maria Diuk-Wasser, Joe Roman, Yongtao Guan, Brad Lobitz, Rama Nemani, Joe Piesman, Montira J. Pongsiri, F...

Cultural, Human, and Social Capital as Determinants of Corporal Punishment: Toward an Integrated Theoretical Model.

ERIC Educational Resources Information Center

Xu, Xiaohe; Tung, Yuk-Ying; Dunaway, R. Gregory

2000-01-01

This article constructs a model to predict the likelihood of parental use of corporal punishment on children in two-parent families. Reports that corporal punishment is primarily determined by cultural, human, and social capital that are available to, or already acquired by parents. Discusses an integrated, resource-based theory for predicting use…

20180312 - Profiling the ToxCast library with a pluripotent human (H9) embryonic stem cell assay (SOT)

EPA Science Inventory

The Stemina devTOX quickPredict platform (STM) is a human pluripotent H9 stem cell-based assay that predicts developmental toxicants. Using the STM model, we screened 1065 ToxCast chemicals and entered the data into the ToxCast data analysis pipeline. Model performance was 83.3% ...

Clinical pharmacology of analgesics assessed with human experimental pain models: bridging basic and clinical research

PubMed Central

Oertel, Bruno Georg; Lötsch, Jörn

2013-01-01

The medical impact of pain is such that much effort is being applied to develop novel analgesic drugs directed towards new targets and to investigate the analgesic efficacy of known drugs. Ongoing research requires cost-saving tools to translate basic science knowledge into clinically effective analgesic compounds. In this review we have re-examined the prediction of clinical analgesia by human experimental pain models as a basis for model selection in phase I studies. The overall prediction of analgesic efficacy or failure of a drug correlated well between experimental and clinical settings. However, correct model selection requires more detailed information about which model predicts a particular clinical pain condition. We hypothesized that if an analgesic drug was effective in an experimental pain model and also a specific clinical pain condition, then that model might be predictive for that particular condition and should be selected for development as an analgesic for that condition. The validity of the prediction increases with an increase in the numbers of analgesic drug classes for which this agreement was shown. From available evidence, only five clinical pain conditions were correctly predicted by seven different pain models for at least three different drugs. Most of these models combine a sensitization method. The analysis also identified several models with low impact with respect to their clinical translation. Thus, the presently identified agreements and non-agreements between analgesic effects on experimental and on clinical pain may serve as a solid basis to identify complex sets of human pain models that bridge basic science with clinical pain research. PMID:23082949

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

PubMed

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

2016-02-19

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

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

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI.

PubMed

Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P

2017-10-01

Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.

Biomechanical Modeling of the Human Head

DTIC Science & Technology

2017-10-03

between model predictions and experimental data. This report details model calibration for all materials identified in models of a human head and...14 3 Stress-strain data for the pia mater and dura mater (human subject); experimental data orig- inally presented in [28...treated as one material) based on a hyperelastic model and experimental data from [59] ............................................... 20 5 Comparison of

Predicting neuroblastoma using developmental signals and a logic-based model.

PubMed

Kasemeier-Kulesa, Jennifer C; Schnell, Santiago; Woolley, Thomas; Spengler, Jennifer A; Morrison, Jason A; McKinney, Mary C; Pushel, Irina; Wolfe, Lauren A; Kulesa, Paul M

2018-07-01

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression. Copyright © 2018. Published by Elsevier B.V.

Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information.

PubMed

Upadhyay, Atul Kumar; Sowdhamini, Ramanathan

2016-01-01

3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

[Prediction model of human-caused fire occurrence in the boreal forest of northern China].

PubMed

Guo, Fu-tao; Su, Zhang-wen; Wang, Guang-yu; Wang, Qiang; Sun, Long; Yang, Ting-ting

2015-07-01

The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g., lightning) and human activities. The literature on human-caused fires indicates that climate, topography, vegetation, and human infrastructure are significant factors that impact the occurrence and spread of human-caused fires. But the studies on human-caused fires in the boreal forest of northern China are limited and less comprehensive. This paper applied the spatial analysis tools in ArcGIS 10.0 and Logistic regression model to investigate the driving factors of human-caused fires. Our data included the geographic coordinates of human-caused fires, climate factors during year 1974-2009, topographic information, and forest map. The results indicated that distance to railway (x1) and average relative humidity (x2) significantly impacted the occurrence of human-caused fire in the study area. The logistic model for predicting the fire occurrence probability was formulated as P= 1/[11+e-(3.026-0.00011x1-0.047x2)] with an accuracy rate of 80%. The above model was used to predict the monthly fire occurrence during the fire season of 2015 based on the HADCM2 future weather data. The prediction results showed that the high risk of human-caused fire occurrence concentrated in the months of April, May, June and August, while April and May had higher risk of fire occurrence than other months. According to the spatial distribution of possibility of fire occurrence, the high fire risk zones were mainly in the west and southwest of Tahe, where the major railways were located.

Differences in the subjective and motivational properties of alcohol across alcohol use severity: application of a novel translational human laboratory paradigm.

PubMed

Bujarski, Spencer; Jentsch, J David; Roche, Daniel J O; Ramchandani, Vijay A; Miotto, Karen; Ray, Lara A

2018-05-08

The Allostatic Model proposes that Alcohol Use Disorder (AUD) is associated with a transition in the motivational structure of alcohol drinking: from positive reinforcement in early-stage drinking to negative reinforcement in late-stage dependence. However, direct empirical support for this preclinical model from human experiments is limited. This study tests predictions derived from the Allostatic Model in humans. Specifically, this study tested whether alcohol use severity (1) independently predicts subjective responses to alcohol (SR; comprised of stimulation/hedonia, negative affect, sedation and craving domains), and alcohol self-administration and 2) moderates associations between domains of SR and alcohol self-administration. Heavy drinking participants ranging in severity of alcohol use and problems (N = 67) completed an intravenous alcohol administration paradigm combining an alcohol challenge (target BrAC = 60 mg%), with progressive ratio self-administration. Alcohol use severity was associated with greater baseline negative affect, sedation, and craving but did not predict changes in any SR domain during the alcohol challenge. Alcohol use severity also predicted greater self-administration. Craving during the alcohol challenge strongly predicted self-administration and sedation predicted lower self-administration. Neither stimulation, nor negative affect predicted self-administration. This study represents a novel approach to translating preclinical neuroscientific theories to the human laboratory. As expected, craving predicted self-administration and sedation was protective. Contrary to the predictions of the Allostatic Model, however, these results were inconsistent with a transition from positively to negatively reinforced alcohol consumption in severe AUD. Future studies that assess negative reinforcement in the context of an acute stressor are warranted.

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

An Exercise Health Simulation Method Based on Integrated Human Thermophysiological Model

PubMed Central

Chen, Xiaohui; Yu, Liang; Yang, Kaixing

2017-01-01

Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare. PMID:28702074

Assessment of the human epidermal model LabCyte EPI-MODEL for In vitro skin corrosion testing according to the OECD test guideline 431.

PubMed

Katoh, Masakazu; Hamajima, Fumiyasu; Ogasawara, Takahiro; Hata, Ken-Ichiro

2010-06-01

A new OECD test guideline 431 (TG431) for in vitro skin corrosion tests using human reconstructed skin models was adopted by OECD in 2004. TG431 defines the criteria for the general function and performance of applicable skin models. In order to confirm that the new reconstructed human epidermal model, LabCyte EPI-MODEL is applicable for the skin corrosion test according to TG431, the predictability and repeatability of the model for the skin corrosion test was evaluated. The test was performed according to the test protocol described in TG431. Based on the knowledge that LabCyte EPI-MODEL is an epidermal model as well as EpiDerm, we decided to adopt the the Epiderm prediction model of skin corrosion for the LabCyte EPI-MODEL, using twenty test chemicals (10 corrosive chemicals and 10 non-corrosive chemicals) in the 1(st) stage. The prediction model results showed that the distinction of non-corrosion to corrosion corresponded perfectly. Therefore, it was judged that the prediction model of EpiDerm could be applied to the LabCyte EPI-MODEL. In the 2(nd) stage, the repeatability of this test protocol with the LabCyte EPI-MODEL was examined using twelve chemicals (6 corrosive chemicals and 6 non-corrosive chemicals) that are described in TG431, and these results recognized a high repeatability and accurate predictability. It was concluded that LabCyte EPI-MODEL is applicable for the skin corrosive test protocol according to TG431.

Experimental investigation of biodynamic human body models subjected to whole-body vibration during a vehicle ride.

PubMed

Taskin, Yener; Hacioglu, Yuksel; Ortes, Faruk; Karabulut, Derya; Arslan, Yunus Ziya

2018-02-06

In this study, responses of biodynamic human body models to whole-body vibration during a vehicle ride were investigated. Accelerations were acquired from three different body parts, such as the head, upper torso and lower torso, of 10 seated passengers during a car ride while two different road conditions were considered. The same multipurpose vehicle was used during all experiments. Additionally, by two widely used biodynamic models in the literature, a set of simulations were run to obtain theoretical accelerations of the models and were compared with those obtained experimentally. To sustain a quantified comparison between experimental and theoretical approaches, the root mean square acceleration and acceleration spectral density were calculated. Time and frequency responses of the models demonstrated that neither of the models showed the best prediction performance of the human body behaviour in all cases, indicating that further models are required for better prediction of the human body responses.

A computational method for predicting regulation of human microRNAs on the influenza virus genome

PubMed Central

2013-01-01

Background While it has been suggested that host microRNAs (miRNAs) may downregulate viral gene expression as an antiviral defense mechanism, such a mechanism has not been explored in the influenza virus for human flu studies. As it is difficult to conduct related experiments on humans, computational studies can provide some insight. Although many computational tools have been designed for miRNA target prediction, there is a need for cross-species prediction, especially for predicting viral targets of human miRNAs. However, finding putative human miRNAs targeting influenza virus genome is still challenging. Results We developed machine-learning features and conducted comprehensive data training for predicting interactions between H1N1 genome segments and host miRNA. We defined our seed region as the first ten nucleotides from the 5' end of the miRNA to the 3' end of the miRNA and integrated various features including the number of consecutive matching bases in the seed region of 10 bases, a triplet feature in seed regions, thermodynamic energy, penalty of bulges and wobbles at binding sites, and the secondary structure of viral RNA for the prediction. Conclusions Compared to general predictive models, our model fully takes into account the conservation patterns and features of viral RNA secondary structures, and greatly improves the prediction accuracy. Our model identified some key miRNAs including hsa-miR-489, hsa-miR-325, hsa-miR-876-3p and hsa-miR-2117, which target HA, PB2, MP and NS of H1N1, respectively. Our study provided an interesting hypothesis concerning the miRNA-based antiviral defense mechanism against influenza virus in human, i.e., the binding between human miRNA and viral RNAs may not result in gene silencing but rather may block the viral RNA replication. PMID:24565017

Evaluation of Human and Anthropomorphic Test Device Finite Element Models under Spaceflight Loading Conditions

NASA Technical Reports Server (NTRS)

Putnam, Jacob P.; Untaroiu, Costin; Somers. Jeffrey

2014-01-01

In an effort to develop occupant protection standards for future multipurpose crew vehicles, the National Aeronautics and Space Administration (NASA) has looked to evaluate the test device for human occupant restraint with the modification kit (THOR-K) anthropomorphic test device (ATD) in relevant impact test scenarios. With the allowance and support of the National Highway Traffic Safety Administration, NASA has performed a series of sled impact tests on the latest developed THOR-K ATD. These tests were performed to match test conditions from human volunteer data previously collected by the U.S. Air Force. The objective of this study was to evaluate the THOR-K finite element (FE) model and the Total HUman Model for Safety (THUMS) FE model with respect to the tests performed. These models were evaluated in spinal and frontal impacts against kinematic and kinetic data recorded in ATD and human testing. Methods: The FE simulations were developed based on recorded pretest ATD/human position and sled acceleration pulses measured during testing. Predicted responses by both human and ATD models were compared to test data recorded under the same impact conditions. The kinematic responses of the models were quantitatively evaluated using the ISO-metric curve rating system. In addition, ATD injury criteria and human stress/strain data were calculated to evaluate the risk of injury predicted by the ATD and human model, respectively. Results: Preliminary results show well-correlated response between both FE models and their physical counterparts. In addition, predicted ATD injury criteria and human model stress/strain values are shown to positively relate. Kinematic comparison between human and ATD models indicates promising biofidelic response, although a slightly stiffer response is observed within the ATD. Conclusion: As a compliment to ATD testing, numerical simulation provides efficient means to assess vehicle safety throughout the design process and further improve the design of physical ATDs. The assessment of the THOR-K and THUMS FE models in a spaceflight testing condition is an essential first step to implementing these models in the computational evaluation of spacecraft occupant safety. Promising results suggest future use of these models in the aerospace field.

Prediction of human gait trajectories during the SSP using a neuromusculoskeletal modeling: A challenge for parametric optimization.

PubMed

Seyed, Mohammadali Rahmati; Mostafa, Rostami; Borhan, Beigzadeh

2018-04-27

The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.

Model Predictive Control Based Motion Drive Algorithm for a Driving Simulator

NASA Astrophysics Data System (ADS)

Rehmatullah, Faizan

In this research, we develop a model predictive control based motion drive algorithm for the driving simulator at Toronto Rehabilitation Institute. Motion drive algorithms exploit the limitations of the human vestibular system to formulate a perception of motion within the constrained workspace of a simulator. In the absence of visual cues, the human perception system is unable to distinguish between acceleration and the force of gravity. The motion drive algorithm determines control inputs to displace the simulator platform, and by using the resulting inertial forces and angular rates, creates the perception of motion. By using model predictive control, we can optimize the use of simulator workspace for every maneuver while simulating the vehicle perception. With the ability to handle nonlinear constraints, the model predictive control allows us to incorporate workspace limitations.

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 ...

A control-theory model for human decision-making

NASA Technical Reports Server (NTRS)

Levison, W. H.; Tanner, R. B.

1971-01-01

A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-03-01

Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. The present study developed new statistical models for predicting driving posture. Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables. Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model. The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age. The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment. © 2015, Human Factors and Ergonomics Society.

Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.

PubMed

Wang, Juexin; Luttrell, Joseph; Zhang, Ning; Khan, Saad; Shi, NianQing; Wang, Michael X; Kang, Jing-Qiong; Wang, Zheng; Xu, Dong

2016-01-01

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.

Numerical analysis of ossicular chain lesion of human ear

NASA Astrophysics Data System (ADS)

Liu, Yingxi; Li, Sheng; Sun, Xiuzhen

2009-04-01

Lesion of ossicular chain is a common ear disease impairing the sense of hearing. A comprehensive numerical model of human ear can provide better understanding of sound transmission. In this study, we propose a three-dimensional finite element model of human ear that incorporates the canal, tympanic membrane, ossicular bones, middle ear suspensory ligaments/muscles, middle ear cavity and inner ear fluid. Numerical analysis is conducted and employed to predict the effects of middle ear cavity, malleus handle defect, hypoplasia of the long process of incus, and stapedial crus defect on sound transmission. The present finite element model is shown to be reasonable in predicting the ossicular mechanics of human ear.

Large-scale model quality assessment for improving protein tertiary structure prediction.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-06-15

Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. © The Author 2015. Published by Oxford University Press.

A polynomial based model for cell fate prediction in human diseases.

PubMed

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

PubMed Central

Ganesh, Gowrishankar

2017-01-01

Abstract The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions. PMID:29340300

The Five Key Questions of Human Performance Modeling.

PubMed

Wu, Changxu

2018-01-01

Via building computational (typically mathematical and computer simulation) models, human performance modeling (HPM) quantifies, predicts, and maximizes human performance, human-machine system productivity and safety. This paper describes and summarizes the five key questions of human performance modeling: 1) Why we build models of human performance; 2) What the expectations of a good human performance model are; 3) What the procedures and requirements in building and verifying a human performance model are; 4) How we integrate a human performance model with system design; and 5) What the possible future directions of human performance modeling research are. Recent and classic HPM findings are addressed in the five questions to provide new thinking in HPM's motivations, expectations, procedures, system integration and future directions.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Strategy generalization across orientation tasks: testing a computational cognitive model.

PubMed

Gunzelmann, Glenn

2008-07-08

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human performance was measured on an orientation task requiring participants to identify the location of a target either on a map (find-on-map) or within an egocentric view of a space (find-in-scene). A general strategy instantiated in a computational cognitive model of the find-on-map task, based on the results from Gunzelmann and Anderson (2006), was adapted to perform both tasks and used to generate performance predictions for a new study. The qualitative fit of the model to the human data supports the view that participants were able to tailor a general strategy to the requirements of particular spatial tasks. The quantitative differences between the predictions of the model and the performance of human participants in the new experiment expose individual differences in sample populations. The model provides a means of accounting for those differences and a framework for understanding how human spatial abilities are applied to naturalistic spatial tasks that involve reasoning with maps. 2008 Cognitive Science Society, Inc.

Prioritizing Conservation of Ungulate Calving Resources in Multiple-Use Landscapes

PubMed Central

Dzialak, Matthew R.; Harju, Seth M.; Osborn, Robert G.; Wondzell, John J.; Hayden-Wing, Larry D.; Winstead, Jeffrey B.; Webb, Stephen L.

2011-01-01

Background Conserving animal populations in places where human activity is increasing is an ongoing challenge in many parts of the world. We investigated how human activity interacted with maternal status and individual variation in behavior to affect reliability of spatially-explicit models intended to guide conservation of critical ungulate calving resources. We studied Rocky Mountain elk (Cervus elaphus) that occupy a region where 2900 natural gas wells have been drilled. Methodology/Principal Findings We present novel applications of generalized additive modeling to predict maternal status based on movement, and of random-effects resource selection models to provide population and individual-based inference on the effects of maternal status and human activity. We used a 2×2 factorial design (treatment vs. control) that included elk that were either parturient or non-parturient and in areas either with or without industrial development. Generalized additive models predicted maternal status (parturiency) correctly 93% of the time based on movement. Human activity played a larger role than maternal status in shaping resource use; elk showed strong spatiotemporal patterns of selection or avoidance and marked individual variation in developed areas, but no such pattern in undeveloped areas. This difference had direct consequences for landscape-level conservation planning. When relative probability of use was calculated across the study area, there was disparity throughout 72–88% of the landscape in terms of where conservation intervention should be prioritized depending on whether models were based on behavior in developed areas or undeveloped areas. Model validation showed that models based on behavior in developed areas had poor predictive accuracy, whereas the model based on behavior in undeveloped areas had high predictive accuracy. Conclusions/Significance By directly testing for differences between developed and undeveloped areas, and by modeling resource selection in a random-effects framework that provided individual-based inference, we conclude that: 1) amplified selection or avoidance behavior and individual variation, as responses to increasing human activity, complicate conservation planning in multiple-use landscapes, and 2) resource selection behavior in places where human activity is predictable or less dynamic may provide a more reliable basis from which to prioritize conservation action. PMID:21297866

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI

PubMed Central

Pauli, Wolfgang M.; Larsen, Tobias; Tyszka, J. Michael; O’Doherty, John P.

2017-01-01

Prediction-error signals consistent with formal models of “reinforcement learning” (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models—namely, “actor/critic” models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning. PMID:29049406

Comparative Computational Modeling of Airflows and Vapor Dosimetry in the Respiratory Tracts of Rat, Monkey, and Human

PubMed Central

Corley, Richard A.

2012-01-01

Computational fluid dynamics (CFD) models are useful for predicting site-specific dosimetry of airborne materials in the respiratory tract and elucidating the importance of species differences in anatomy, physiology, and breathing patterns. We improved the imaging and model development methods to the point where CFD models for the rat, monkey, and human now encompass airways from the nose or mouth to the lung. A total of 1272, 2172, and 135 pulmonary airways representing 17±7, 19±9, or 9±2 airway generations were included in the rat, monkey and human models, respectively. A CFD/physiologically based pharmacokinetic model previously developed for acrolein was adapted for these anatomically correct extended airway models. Model parameters were obtained from the literature or measured directly. Airflow and acrolein uptake patterns were determined under steady-state inhalation conditions to provide direct comparisons with prior data and nasal-only simulations. Results confirmed that regional uptake was sensitive to airway geometry, airflow rates, acrolein concentrations, air:tissue partition coefficients, tissue thickness, and the maximum rate of metabolism. Nasal extraction efficiencies were predicted to be greatest in the rat, followed by the monkey, and then the human. For both nasal and oral breathing modes in humans, higher uptake rates were predicted for lower tracheobronchial tissues than either the rat or monkey. These extended airway models provide a unique foundation for comparing material transport and site-specific tissue uptake across a significantly greater range of conducting airways in the rat, monkey, and human than prior CFD models. PMID:22584687

Effect of clothing material on thermal responses of the human body

NASA Astrophysics Data System (ADS)

Fengzhi, Li; Yi, Li

2005-09-01

The influence of clothing material on thermal responses of the human body are investigated by using an integrated model of a clothed thermoregulatory human body. A modified 25-nodes model considering the sweat accumulation on the skin surface is applied to simulate the human physiological regulatory responses. The heat and moisture coupled transfer mechanisms, including water vapour diffusion, the moisture evaporation/condensation, the moisture sorbtion/desorption by fibres, liquid sweat transfer under capillary pressure, and latent heat absorption/release due to phase change, are considered in the clothing model. On comparing prediction results with the experimental data in the literature, the proposed model seems able to predict dynamic heat and moisture transfer between the human body and the clothing system. The human body's thermal responses and clothing temperature and moisture variations are compared for different clothing materials during transient periods. We concluded that the hygroscopicity of clothing materials influences the human thermoregulation process significantly during environmental transients.

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

PubMed

Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

2004-10-07

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

Identifying and modeling the structural discontinuities of human interactions

NASA Astrophysics Data System (ADS)

Grauwin, Sebastian; Szell, Michael; Sobolevsky, Stanislav; Hövel, Philipp; Simini, Filippo; Vanhoof, Maarten; Smoreda, Zbigniew; Barabási, Albert-László; Ratti, Carlo

2017-04-01

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.

Identifying and modeling the structural discontinuities of human interactions

PubMed Central

Grauwin, Sebastian; Szell, Michael; Sobolevsky, Stanislav; Hövel, Philipp; Simini, Filippo; Vanhoof, Maarten; Smoreda, Zbigniew; Barabási, Albert-László; Ratti, Carlo

2017-01-01

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales. PMID:28443647

Identifying and modeling the structural discontinuities of human interactions.

PubMed

Grauwin, Sebastian; Szell, Michael; Sobolevsky, Stanislav; Hövel, Philipp; Simini, Filippo; Vanhoof, Maarten; Smoreda, Zbigniew; Barabási, Albert-László; Ratti, Carlo

2017-04-26

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.

PubMed

Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Faria, Nuno; Bielejec, Filip; Baele, Guy; Russell, Colin A; Smith, Derek J; Pybus, Oliver G; Brockmann, Dirk; Suchard, Marc A

2014-02-01

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.

Closed loop models for analyzing the effects of simulator characteristics. [digital simulation of human operators

NASA Technical Reports Server (NTRS)

Baron, S.; Muralidharan, R.; Kleinman, D. L.

1978-01-01

The optimal control model of the human operator is used to develop closed loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed loop multirate simulation in which the optimal control model simulates the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload.

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.

The insertion of human dynamics models in the flight control loops of V/STOL research aircraft. Appendix 2: The optimal control model of a pilot in V/STOL aircraft control loops

NASA Technical Reports Server (NTRS)

Zipf, Mark E.

1989-01-01

An overview is presented of research work focussed on the design and insertion of classical models of human pilot dynamics within the flight control loops of V/STOL aircraft. The pilots were designed and configured for use in integrated control system research and design. The models of human behavior that were considered are: McRuer-Krendel (a single variable transfer function model); and Optimal Control Model (a multi-variable approach based on optimal control and stochastic estimation theory). These models attempt to predict human control response characteristics when confronted with compensatory tracking and state regulation tasks. An overview, mathematical description, and discussion of predictive limitations of the pilot models is presented. Design strategies and closed loop insertion configurations are introduced and considered for various flight control scenarios. Models of aircraft dynamics (both transfer function and state space based) are developed and discussed for their use in pilot design and application. Pilot design and insertion are illustrated for various flight control objectives. Results of pilot insertion within the control loops of two V/STOL research aricraft (Sikorski Black Hawk UH-60A, McDonnell Douglas Harrier II AV-8B) are presented and compared against actual pilot flight data. Conclusions are reached on the ability of the pilot models to adequately predict human behavior when confronted with similar control objectives.

Applications of artificial intelligence in safe human-robot interactions.

PubMed

Najmaei, Nima; Kermani, Mehrdad R

2011-04-01

The integration of industrial robots into the human workspace presents a set of unique challenges. This paper introduces a new sensory system for modeling, tracking, and predicting human motions within a robot workspace. A reactive control scheme to modify a robot's operations for accommodating the presence of the human within the robot workspace is also presented. To this end, a special class of artificial neural networks, namely, self-organizing maps (SOMs), is employed for obtaining a superquadric-based model of the human. The SOM network receives information of the human's footprints from the sensory system and infers necessary data for rendering the human model. The model is then used in order to assess the danger of the robot operations based on the measured as well as predicted human motions. This is followed by the introduction of a new reactive control scheme that results in the least interferences between the human and robot operations. The approach enables the robot to foresee an upcoming danger and take preventive actions before the danger becomes imminent. Simulation and experimental results are presented in order to validate the effectiveness of the proposed method.

Development of time-trend model for analysing and predicting case pattern of dog bite injury induced rabies-like-illness in Liberia, 2014-2017.

PubMed

Jomah, N D; Ojo, J F; Odigie, E A; Olugasa, B O

2014-12-01

The post-civil war records of dog bite injuries (DBI) and rabies-like-illness (RLI) among humans in Liberia is a vital epidemiological resource for developing a predictive model to guide the allocation of resources towards human rabies control. Whereas DBI and RLI are high, they are largely under-reported. The objective of this study was to develop a time model of the case-pattern and apply it to derive predictors of time-trend point distribution of DBI-RLI cases. A retrospective 6 years data of DBI distribution among humans countrywide were converted to quarterly series using a transformation technique of Minimizing Squared First Difference statistic. The generated dataset was used to train a time-trend model of the DBI-RLI syndrome in Liberia. An additive detenninistic time-trend model was selected due to its performance compared to multiplication model of trend and seasonal movement. Parameter predictors were run on least square method to predict DBI cases for a prospective 4 years period, covering 2014-2017. The two-stage predictive model of DBI case-pattern between 2014 and 2017 was characterised by a uniform upward trend within Liberia's coastal and hinterland Counties over the forecast period. This paper describes a translational application of the time-trend distribution pattern of DBI epidemics, 2008-2013 reported in Liberia, on which a predictive model was developed. A computationally feasible two-stage time-trend permutation approach is proposed to estimate the time-trend parameters and conduct predictive inference on DBI-RLI in Liberia.

Predicting when biliary excretion of parent drug is a major route of elimination in humans.

PubMed

Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

2014-09-01

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Mouse Models as Predictors of Human Responses: Evolutionary Medicine.

PubMed

Uhl, Elizabeth W; Warner, Natalie J

Mice offer a number of advantages and are extensively used to model human diseases and drug responses. Selective breeding and genetic manipulation of mice have made many different genotypes and phenotypes available for research. However, in many cases, mouse models have failed to be predictive. Important sources of the prediction problem have been the failure to consider the evolutionary basis for species differences, especially in drug metabolism, and disease definitions that do not reflect the complexity of gene expression underlying disease phenotypes. Incorporating evolutionary insights into mouse models allow for unique opportunities to characterize the effects of diet, different gene expression profiles, and microbiomics underlying human drug responses and disease phenotypes.

Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

PubMed

Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

2018-02-01

Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

POPULATION EXPOSURES TO PARTICULATE MATTER: A COMPARISON OF EXPOSURE MODEL PREDICTIONS AND MEASUREMENT DATA

EPA Science Inventory

The US EPA National Exposure Research Laboratory (NERL) is currently developing an integrated human exposure source-to-dose modeling system (HES2D). This modeling system will incorporate models that use a probabilistic approach to predict population exposures to environmental ...

Can the PHS model (ISO7933) predict reasonable thermophysiological responses while wearing protective clothing in hot environments?

PubMed

Wang, Faming; Kuklane, Kalev; Gao, Chuansi; Holmér, Ingvar

2011-02-01

In this paper, the prediction accuracy of the PHS (predicted heat strain) model on human physiological responses while wearing protective clothing ensembles was examined. Six human subjects (aged 29 ± 3 years) underwent three experimental trials in three different protective garments (clothing thermal insulation I(cl) ranges from 0.63 to 2.01 clo) in two hot environments (40 °C, relative humidities: 30% and 45%). The observed and predicted mean skin temperature, core body temperature and sweat rate were presented and statistically compared. A significant difference was found in the metabolic rate between FIRE (firefighting clothing) and HV (high visibility clothing) or MIL (military clothing) (p < 0.001). Also, the development of heart rate demonstrated the significant effects of the exposure time and clothing ensembles. In addition, the predicted evaporation rate during HV, MIL and FIRE was much lower than the experimental values. Hence, the current PHS model is not applicable for protective clothing with intrinsic thermal insulations above 1.0 clo. The results showed that the PHS model generated unreliable predictions on body core temperature when human subjects wore thick protective clothing such as firefighting clothing (I(cl) > 1.0 clo). The predicted mean skin temperatures in three clothing ensembles HV, MIL and FIRE were also outside the expected limits. Thus, there is a need for further extension for the clothing insulation validation range of the PHS model. It is recommended that the PHS model should be amended and validated by individual algorithms, physical or physiological parameters, and further subject studies.

Predictive modeling of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands.

PubMed

Thorne, Lesley H; Johnston, David W; Urban, Dean L; Tyne, Julian; Bejder, Lars; Baird, Robin W; Yin, Suzanne; Rickards, Susan H; Deakos, Mark H; Mobley, Joseph R; Pack, Adam A; Chapla Hill, Marie

2012-01-01

Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood.

Predictive Modeling of Spinner Dolphin (Stenella longirostris) Resting Habitat in the Main Hawaiian Islands

PubMed Central

Thorne, Lesley H.; Johnston, David W.; Urban, Dean L.; Tyne, Julian; Bejder, Lars; Baird, Robin W.; Yin, Suzanne; Rickards, Susan H.; Deakos, Mark H.; Mobley, Joseph R.; Pack, Adam A.; Chapla Hill, Marie

2012-01-01

Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood. PMID:22937022

NEXT GENERATION MULTIMEDIA/MULTIPATHWAY EXPOSURE MODELING

EPA Science Inventory

The Stochastic Human Exposure and Dose Simulation model for pesticides (SHEDS-Pesticides) supports the efforts of EPA to better understand human exposures and doses to multimedia, multipathway pollutants. It is a physically-based, probabilistic computer model that predicts, for u...

Probabilistic modeling of discourse-aware sentence processing.

PubMed

Dubey, Amit; Keller, Frank; Sturt, Patrick

2013-07-01

Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. Copyright © 2013 Cognitive Science Society, Inc.

A predictive model of human performance.

NASA Technical Reports Server (NTRS)

Walters, R. F.; Carlson, L. D.

1971-01-01

An attempt is made to develop a model describing the overall responses of humans to exercise and environmental stresses for prediction of exhaustion vs an individual's physical characteristics. The principal components of the model are a steady state description of circulation and a dynamic description of thermal regulation. The circulatory portion of the system accepts changes in work load and oxygen pressure, while the thermal portion is influenced by external factors of ambient temperature, humidity and air movement, affecting skin blood flow. The operation of the model is discussed and its structural details are given.

Genetically Engineered Cancer Models, But Not Xenografts, Faithfully Predict Anticancer Drug Exposure in Melanoma Tumors

PubMed Central

Combest, Austin J.; Roberts, Patrick J.; Dillon, Patrick M.; Sandison, Katie; Hanna, Suzan K.; Ross, Charlene; Habibi, Sohrab; Zamboni, Beth; Müller, Markus; Brunner, Martin; Sharpless, Norman E.

2012-01-01

Background. Rodent studies are a vital step in the development of novel anticancer therapeutics and are used in pharmacokinetic (PK), toxicology, and efficacy studies. Traditionally, anticancer drug development has relied on xenograft implantation of human cancer cell lines in immunocompromised mice for efficacy screening of a candidate compound. The usefulness of xenograft models for efficacy testing, however, has been questioned, whereas genetically engineered mouse models (GEMMs) and orthotopic syngeneic transplants (OSTs) may offer some advantages for efficacy assessment. A critical factor influencing the predictability of rodent tumor models is drug PKs, but a comprehensive comparison of plasma and tumor PK parameters among xenograft models, OSTs, GEMMs, and human patients has not been performed. Methods. In this work, we evaluated the plasma and tumor dispositions of an antimelanoma agent, carboplatin, in patients with cutaneous melanoma compared with four different murine melanoma models (one GEMM, one human cell line xenograft, and two OSTs). Results. Using microdialysis to sample carboplatin tumor disposition, we found that OSTs and xenografts were poor predictors of drug exposure in human tumors, whereas the GEMM model exhibited PK parameters similar to those seen in human tumors. Conclusions. The tumor PKs of carboplatin in a GEMM of melanoma more closely resembles the tumor disposition in patients with melanoma than transplanted tumor models. GEMMs show promise in becoming an improved prediction model for intratumoral PKs and response in patients with solid tumors. PMID:22993143

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study

PubMed Central

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-01-01

Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2. Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most promising. PMID:22382688

An optimal state estimation model of sensory integration in human postural balance

NASA Astrophysics Data System (ADS)

Kuo, Arthur D.

2005-09-01

We propose a model for human postural balance, combining state feedback control with optimal state estimation. State estimation uses an internal model of body and sensor dynamics to process sensor information and determine body orientation. Three sensory modalities are modeled: joint proprioception, vestibular organs in the inner ear, and vision. These are mated with a two degree-of-freedom model of body dynamics in the sagittal plane. Linear quadratic optimal control is used to design state feedback and estimation gains. Nine free parameters define the control objective and the signal-to-noise ratios of the sensors. The model predicts statistical properties of human sway in terms of covariance of ankle and hip motion. These predictions are compared with normal human responses to alterations in sensory conditions. With a single parameter set, the model successfully reproduces the general nature of postural motion as a function of sensory environment. Parameter variations reveal that the model is highly robust under normal sensory conditions, but not when two or more sensors are inaccurate. This behavior is similar to that of normal human subjects. We propose that age-related sensory changes may be modeled with decreased signal-to-noise ratios, and compare the model's behavior with degraded sensors against experimental measurements from older adults. We also examine removal of the model's vestibular sense, which leads to instability similar to that observed in bilateral vestibular loss subjects. The model may be useful for predicting which sensors are most critical for balance, and how much they can deteriorate before posture becomes unstable.

Development of a Zealand White Rabbit Deposition Model to Study Inhalation Anthrax

PubMed Central

Asgharian, Bahman; Price, Owen; Kabilan, Senthil; Jacob, Richard E.; Einstein, Daniel R.; Kuprat, A.P.; Corley, Richard A.

2016-01-01

Despite using rabbits in several inhalation exposure experiments to study diseases such as anthrax, there is a lack of understanding regarding deposition characteristics and fate of inhaled particles (bio-aerosols and viruses) in the respiratory tracts of rabbits. Such information allows dosimetric extrapolation to humans to inform human outcomes. The lung geometry of the New Zealand white rabbit (referred to simply as rabbits throughout the article) was constructed using recently acquired scanned images of the conducting airways of rabbits and available information on its acinar region. In addition, functional relationships were developed for the lung and breathing parameters of rabbits as a function of body weight. The lung geometry and breathing parameters were used to extend the existing deposition model for humans and several other species to rabbits. Evaluation of the deposition model for rabbits was made by comparing predictions with available measurements in the literature. Deposition predictions in the lungs of rabbits indicated smaller deposition fractions compared to those found in humans across various particle diameter ranges. The application of the deposition model for rabbits was demonstrated by extrapolating deposition predictions in rabbits to find equivalent human exposure concentrations assuming the same dose-response relationship between the two species. Human equivalent exposure concentration levels were found to be much smaller than those for rabbits. PMID:26895308

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

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

Eck, Brendan L.; Fahmi, Rachid; Miao, Jun

2015-10-15

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated usingmore » a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.« less

Improving in vitro to in vivo extrapolation by incorporating toxicokinetic measurements: A case study of lindane-induced neurotoxicity

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

Croom, Edward L.; Shafer, Timothy J.; Evans, Marina V.

Approaches for extrapolating in vitro toxicity testing results for prediction of human in vivo outcomes are needed. The purpose of this case study was to employ in vitro toxicokinetics and PBPK modeling to perform in vitro to in vivo extrapolation (IVIVE) of lindane neurotoxicity. Lindane cell and media concentrations in vitro, together with in vitro concentration-response data for lindane effects on neuronal network firing rates, were compared to in vivo data and model simulations as an exercise in extrapolation for chemical-induced neurotoxicity in rodents and humans. Time- and concentration-dependent lindane dosimetry was determined in primary cultures of rat cortical neuronsmore » in vitro using “faux” (without electrodes) microelectrode arrays (MEAs). In vivo data were derived from literature values, and physiologically based pharmacokinetic (PBPK) modeling was used to extrapolate from rat to human. The previously determined EC{sub 50} for increased firing rates in primary cultures of cortical neurons was 0.6 μg/ml. Media and cell lindane concentrations at the EC{sub 50} were 0.4 μg/ml and 7.1 μg/ml, respectively, and cellular lindane accumulation was time- and concentration-dependent. Rat blood and brain lindane levels during seizures were 1.7–1.9 μg/ml and 5–11 μg/ml, respectively. Brain lindane levels associated with seizures in rats and those predicted for humans (average = 7 μg/ml) by PBPK modeling were very similar to in vitro concentrations detected in cortical cells at the EC{sub 50} dose. PBPK model predictions matched literature data and timing. These findings indicate that in vitro MEA results are predictive of in vivo responses to lindane and demonstrate a successful modeling approach for IVIVE of rat and human neurotoxicity. - Highlights: • In vitro to in vivo extrapolation for lindane neurotoxicity was performed. • Dosimetry of lindane in a micro-electrode array (MEA) test system was assessed. • Cell concentrations at the MEA EC{sub 50} equaled rat brain levels associated with seizure. • PBPK-predicted human brain levels at seizure also equaled EC{sub 50} cell concentrations. • In vitro MEA results are predictive of lindane in vivo dose–response in rats/humans.« less

in vitro Models if Human Embryonic Mesenchymal Transitions in Morphogenesis

EPA Science Inventory

Our ability to predict human developmental consequences produced by exposure to environmental chemicals is limited by the current experimental and computational models.Human heart defects are among the most common type of birth defects and affect 1% of children (~40,000 children)...

Questions regarding the predictive value of one evolved complex adaptive system for a second: exemplified by the SOD1 mouse.

PubMed

Greek, Ray; Hansen, Lawrence A

2013-11-01

We surveyed the scientific literature regarding amyotrophic lateral sclerosis, the SOD1 mouse model, complex adaptive systems, evolution, drug development, animal models, and philosophy of science in an attempt to analyze the SOD1 mouse model of amyotrophic lateral sclerosis in the context of evolved complex adaptive systems. Humans and animals are examples of evolved complex adaptive systems. It is difficult to predict the outcome from perturbations to such systems because of the characteristics of complex systems. Modeling even one complex adaptive system in order to predict outcomes from perturbations is difficult. Predicting outcomes to one evolved complex adaptive system based on outcomes from a second, especially when the perturbation occurs at higher levels of organization, is even more problematic. Using animal models to predict human outcomes to perturbations such as disease and drugs should have a very low predictive value. We present empirical evidence confirming this and suggest a theory to explain this phenomenon. We analyze the SOD1 mouse model of amyotrophic lateral sclerosis in order to illustrate this position. Copyright © 2013 The Authors. Published by Elsevier Ltd.. 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

The Microphysiology Systems Database for Analyzing and Modeling Compound Interactions with Human and Animal Organ Models

PubMed Central

Vernetti, Lawrence; Bergenthal, Luke; Shun, Tong Ying; Taylor, D. Lansing

2016-01-01

Abstract Microfluidic human organ models, microphysiology systems (MPS), are currently being developed as predictive models of drug safety and efficacy in humans. To design and validate MPS as predictive of human safety liabilities requires safety data for a reference set of compounds, combined with in vitro data from the human organ models. To address this need, we have developed an internet database, the MPS database (MPS-Db), as a powerful platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. The present study demonstrates the capability of the MPS-Db in early safety testing using a human liver MPS to relate the effects of tolcapone and entacapone in the in vitro model to human in vivo effects. These two compounds were chosen to be evaluated as a representative pair of marketed drugs because they are structurally similar, have the same target, and were found safe or had an acceptable risk in preclinical and clinical trials, yet tolcapone induced unacceptable levels of hepatotoxicity while entacapone was found to be safe. Results demonstrate the utility of the MPS-Db as an essential resource for relating in vitro organ model data to the multiple biochemical, preclinical, and clinical data sources on in vivo drug effects. PMID:28781990

Effects of human dynamics on epidemic spreading in Côte d'Ivoire

NASA Astrophysics Data System (ADS)

Li, Ruiqi; Wang, Wenxu; Di, Zengru

2017-02-01

Understanding and predicting outbreaks of contagious diseases are crucial to the development of society and public health, especially for underdeveloped countries. However, challenging problems are encountered because of complex epidemic spreading dynamics influenced by spatial structure and human dynamics (including both human mobility and human interaction intensity). We propose a systematical model to depict nationwide epidemic spreading in Côte d'Ivoire, which integrates multiple factors, such as human mobility, human interaction intensity, and demographic features. We provide insights to aid in modeling and predicting the epidemic spreading process by data-driven simulation and theoretical analysis, which is otherwise beyond the scope of local evaluation and geometrical views. We show that the requirement that the average local basic reproductive number to be greater than unity is not necessary for outbreaks of epidemics. The observed spreading phenomenon can be roughly explained as a heterogeneous diffusion-reaction process by redefining mobility distance according to the human mobility volume between nodes, which is beyond the geometrical viewpoint. However, the heterogeneity of human dynamics still poses challenges to precise prediction.

UTILIZATION OF A PBPK MODEL TO PREDICT THE DISTRIBUTION OF 2, 3, 7-8 TETRACHLORODIBENZO-P-DIOXIN (TCDD) IN HUMANS DURING CRITICAL WINDOWS OF DEVELOPMENT

EPA Science Inventory

Utilization of A PBPK model to predict the distribution of 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) in humans during critical windows of development.
C Emond1, MJ DeVito2 and LS Birnbaum2
1National Research Council, US EPA, ORD, NHEERL, (ETD, PK), RTP, NC, 27711, USA 2 US...

Perceptual quality prediction on authentically distorted images using a bag of features approach

PubMed Central

Ghadiyaram, Deepti; Bovik, Alan C.

2017-01-01

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417

A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

NASA Technical Reports Server (NTRS)

Corker, Kevin; Pisanich, Gregory; Condon, Gregory W. (Technical Monitor)

1995-01-01

A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

The human physiological impact of global deoxygenation.

PubMed

Martin, Daniel; McKenna, Helen; Livina, Valerie

2017-01-01

There has been a clear decline in the volume of oxygen in Earth's atmosphere over the past 20 years. Although the magnitude of this decrease appears small compared to the amount of oxygen in the atmosphere, it is difficult to predict how this process may evolve, due to the brevity of the collected records. A recently proposed model predicts a non-linear decay, which would result in an increasingly rapid fall-off in atmospheric oxygen concentration, with potentially devastating consequences for human health. We discuss the impact that global deoxygenation, over hundreds of generations, might have on human physiology. Exploring the changes between different native high-altitude populations provides a paradigm of how humans might tolerate worsening hypoxia over time. Using this model of atmospheric change, we predict that humans may continue to survive in an unprotected atmosphere for ~3600 years. Accordingly, without dramatic changes to the way in which we interact with our planet, humans may lose their dominance on Earth during the next few millennia.

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

Corley, Richard A.; Kabilan, Senthil; Kuprat, Andrew P.

Coupling computational fluid dynamics (CFD) with physiologically based pharmacokinetic (PBPK) models is useful for predicting site-specific dosimetry of airborne materials in the respiratory tract and elucidating the importance of species differences in anatomy, physiology, and breathing patterns. Historically, these models were limited to discrete regions of the respiratory system. CFD/PBPK models have now been developed for the rat, monkey, and human that encompass airways from the nose or mouth to the lung. A PBPK model previously developed to describe acrolein uptake in nasal tissues was adapted to the extended airway models as an example application. Model parameters for each anatomicmore » region were obtained from the literature, measured directly, or estimated from published data. Airflow and site-specific acrolein uptake patterns were determined under steadystate inhalation conditions to provide direct comparisons with prior data and nasalonly simulations. Results confirmed that regional uptake was dependent upon airflow rates and acrolein concentrations with nasal extraction efficiencies predicted to be greatest in the rat, followed by the monkey, then the human. For human oral-breathing simulations, acrolein uptake rates in oropharyngeal and laryngeal tissues were comparable to nasal tissues following nasal breathing under the same exposure conditions. For both breathing modes, higher uptake rates were predicted for lower tracheo-bronchial tissues of humans than either the rat or monkey. These extended airway models provide a unique foundation for comparing dosimetry across a significantly more extensive range of conducting airways in the rat, monkey, and human than prior CFD models.« less

Relating Data and Models to Characterize Parameter and Prediction Uncertainty

EPA Science Inventory

Applying PBPK models in risk analysis requires that we realistically assess the uncertainty of relevant model predictions in as quantitative a way as possible. The reality of human variability may add a confusing feature to the overall uncertainty assessment, as uncertainty and v...

Geometry-based pressure drop prediction in mildly diseased human coronary arteries.

PubMed

Schrauwen, J T C; Wentzel, J J; van der Steen, A F W; Gijsen, F J H

2014-06-03

Pressure drop (△p) estimations in human coronary arteries have several important applications, including determination of appropriate boundary conditions for CFD and estimation of fractional flow reserve (FFR). In this study a △p prediction was made based on geometrical features derived from patient-specific imaging data. Twenty-two mildly diseased human coronary arteries were imaged with computed tomography and intravascular ultrasound. Each artery was modelled in three consecutive steps: from straight to tapered, to stenosed, to curved model. CFD was performed to compute the additional △p in each model under steady flow for a wide range of Reynolds numbers. The correlations between the added geometrical complexity and additional △p were used to compute a predicted △p. This predicted △p based on geometry was compared to CFD results. The mean △p calculated with CFD was 855±666Pa. Tapering and curvature added significantly to the total △p, accounting for 31.4±19.0% and 18.0±10.9% respectively at Re=250. Using tapering angle, maximum area stenosis and angularity of the centerline, we were able to generate a good estimate for the predicted △p with a low mean but high standard deviation: average error of 41.1±287.8Pa at Re=250. Furthermore, the predicted △p was used to accurately estimate FFR (r=0.93). The effect of the geometric features was determined and the pressure drop in mildly diseased human coronary arteries was predicted quickly based solely on geometry. This pressure drop estimation could serve as a boundary condition in CFD to model the impact of distal epicardial vessels. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrating Environmental and Mosquito Data to Model Disease: Evaluating Alternative Modeling Approaches for Forecasting West Nile Virus in South Dakota, USA

NASA Astrophysics Data System (ADS)

Davis, J. K.; Vincent, G. P.; Hildreth, M.; Kightlinger, L.; Carlson, C.; Wimberly, M. C.

2017-12-01

South Dakota has the highest annual incidence of human cases of West Nile virus (WNV) in all US states, and human cases can vary wildly among years; predicting WNV risk in advance is a necessary exercise if public health officials are to respond efficiently and effectively to risk. Case counts are associated with environmental factors that affect mosquitoes, avian hosts, and the virus itself. They are also correlated with entomological risk indices obtained by trapping and testing mosquitoes. However, neither weather nor insect data alone provide a sufficient basis to make timely and accurate predictions, and combining them into models of human disease is not necessarily straightforward. Here we present lessons learned in three years of making real-time forecasts of this threat to public health. Various methods of integrating data from NASA's North American Land Data Assimilation System (NLDAS) with mosquito surveillance data were explored in a model comparison framework. We found that a model of human disease summarizing weather data (by polynomial distributed lags with seasonally-varying coefficients) and mosquito data (by a mixed-effects model that smooths out these sparse and highly-variable data) made accurate predictions of risk, and was generalizable enough to be recommended in similar applications. A model based on lagged effects of temperature and humidity provided the most accurate predictions. We also found that model accuracy was improved by allowing coefficients to vary smoothly throughout the season, giving different weights to different predictor variables during different parts of the season.

Estimating natural monthly streamflows in California and the likelihood of anthropogenic modification

USGS Publications Warehouse

Carlisle, Daren M.; Wolock, David M.; Howard, Jeannette K.; Grantham, Theodore E.; Fesenmyer, Kurt; Wieczorek, Michael

2016-12-12

Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be experiencing anthropogenic flow modification.

A changing climate: impacts on human exposures to O3 using an integrated modeling methodology

EPA Science Inventory

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposu...

Accurate Prediction of Drug-Induced Liver Injury Using Stem Cell-Derived Populations

PubMed Central

Szkolnicka, Dagmara; Farnworth, Sarah L.; Lucendo-Villarin, Baltasar; Storck, Christopher; Zhou, Wenli; Iredale, John P.; Flint, Oliver

2014-01-01

Despite major progress in the knowledge and management of human liver injury, there are millions of people suffering from chronic liver disease. Currently, the only cure for end-stage liver disease is orthotopic liver transplantation; however, this approach is severely limited by organ donation. Alternative approaches to restoring liver function have therefore been pursued, including the use of somatic and stem cell populations. Although such approaches are essential in developing scalable treatments, there is also an imperative to develop predictive human systems that more effectively study and/or prevent the onset of liver disease and decompensated organ function. We used a renewable human stem cell resource, from defined genetic backgrounds, and drove them through developmental intermediates to yield highly active, drug-inducible, and predictive human hepatocyte populations. Most importantly, stem cell-derived hepatocytes displayed equivalence to primary adult hepatocytes, following incubation with known hepatotoxins. In summary, we have developed a serum-free, scalable, and shippable cell-based model that faithfully predicts the potential for human liver injury. Such a resource has direct application in human modeling and, in the future, could play an important role in developing renewable cell-based therapies. PMID:24375539

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

Structure and non-structure of centrosomal proteins.

PubMed

Dos Santos, Helena G; Abia, David; Janowski, Robert; Mortuza, Gulnahar; Bertero, Michela G; Boutin, Maïlys; Guarín, Nayibe; Méndez-Giraldez, Raúl; Nuñez, Alfonso; Pedrero, Juan G; Redondo, Pilar; Sanz, María; Speroni, Silvia; Teichert, Florian; Bruix, Marta; Carazo, José M; Gonzalez, Cayetano; Reina, José; Valpuesta, José M; Vernos, Isabelle; Zabala, Juan C; Montoya, Guillermo; Coll, Miquel; Bastolla, Ugo; Serrano, Luis

2013-01-01

Here we perform a large-scale study of the structural properties and the expression of proteins that constitute the human Centrosome. Centrosomal proteins tend to be larger than generic human proteins (control set), since their genes contain in average more exons (20.3 versus 14.6). They are rich in predicted disordered regions, which cover 57% of their length, compared to 39% in the general human proteome. They also contain several regions that are dually predicted to be disordered and coiled-coil at the same time: 55 proteins (15%) contain disordered and coiled-coil fragments that cover more than 20% of their length. Helices prevail over strands in regions homologous to known structures (47% predicted helical residues against 17% predicted as strands), and even more in the whole centrosomal proteome (52% against 7%), while for control human proteins 34.5% of the residues are predicted as helical and 12.8% are predicted as strands. This difference is mainly due to residues predicted as disordered and helical (30% in centrosomal and 9.4% in control proteins), which may correspond to alpha-helix forming molecular recognition features (α-MoRFs). We performed expression assays for 120 full-length centrosomal proteins and 72 domain constructs that we have predicted to be globular. These full-length proteins are often insoluble: Only 39 out of 120 expressed proteins (32%) and 19 out of 72 domains (26%) were soluble. We built or retrieved structural models for 277 out of 361 human proteins whose centrosomal localization has been experimentally verified. We could not find any suitable structural template with more than 20% sequence identity for 84 centrosomal proteins (23%), for which around 74% of the residues are predicted to be disordered or coiled-coils. The three-dimensional models that we built are available at http://ub.cbm.uam.es/centrosome/models/index.php.

On Spatially Explicit Models of Epidemic and Endemic Cholera: The Haiti and Lake Kivu Case Studies.

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Bertuzzo, E.; Mari, L.; Finger, F.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.

2014-12-01

The first part of the Lecture deals with the predictive ability of mechanistic models for the Haitian cholera epidemic. Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. A formal model comparison framework provides a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels. Intensive computations and objective model comparisons show that parsimonious spatially explicit models accounting for spatial connections have superior explanatory power than spatially disconnected ones for short-to intermediate calibration windows. In general, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. The second part deals with approaches suitable to describe patterns of endemic cholera. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of lake Kivu. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. Fourteen models, accounting for different environmental drivers, are selected in calibration. Among these, the one accounting for seasonality, El Nino Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation.

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.

Simulation of urinary excretion of 1-hydroxypyrene in various scenarios of exposure to polycyclic aromatic hydrocarbons with a generic, cross-chemical predictive PBTK-model.

PubMed

Jongeneelen, Frans; ten Berge, Wil

2012-08-01

A physiologically based toxicokinetic (PBTK) model can predict blood and urine concentrations, given a certain exposure scenario of inhalation, dermal and/or oral exposure. The recently developed PBTK-model IndusChemFate is a unified model that mimics the uptake, distribution, metabolism and elimination of a chemical in a reference human of 70 kg. Prediction of the uptake by inhalation is governed by pulmonary exchange to blood. Oral uptake is simulated as a bolus dose that is taken up at a first-order rate. Dermal uptake is estimated by the use of a novel dermal physiologically based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Partitioning of the chemical and metabolite(s) over blood and tissues is estimated by a Quantitative Structure-Property Relationship (QSPR) algorithm. The aim of this study was to test the generic PBTK-model by comparing measured urinary levels of 1-hydroxypyrene in various inhalation and dermal exposure scenarios with the result of model simulations. In the last three decades, numerous biomonitoring studies of PAH-exposed humans were published that used the bioindicator 1-hydroxypyrene (1-OH-pyrene) in urine. Longitudinal studies that encompass both dosimetry and biomonitoring with repeated sampling in time were selected to test the accuracy of the PBTK-model by comparing the reported concentrations of 1-OHP in urine with the model-predicted values. Two controlled human volunteer studies and three field studies of workers exposed to polycyclic aromatic hydrocarbons (PAH) were included. The urinary pyrene-metabolite levels of a controlled human inhalation study, a transdermal uptake study of bitumen fume, efficacy of respirator use in electrode paste workers, cokery workers in shale oil industry and a longitudinal study of five coke liquefaction workers were compared to the PBTK-predicted values. The simulations showed that the model-predicted concentrations of urinary pyrene and metabolites over time, as well as peak-concentrations and total excreted amount in different exposure scenarios of inhalation and transdermal exposure were in all comparisons within an order of magnitude. The model predicts that only a very small fraction is excreted in urine as parent pyrene and as free 1-OH-pyrene. The predominant urinary metabolite is 1-OH-pyrene-glucuronide. Enterohepatic circulation of 1-OH-pyrene-glucuronide seems the reason of the delayed release from the body. It appeared that urinary excretion of pyrene and pyrene-metabolites in humans is predictable with the PBTK-model. The model outcomes have a satisfying accuracy for early testing, in so-called 1st tier simulations and in range finding. This newly developed generic PBTK-model IndusChemFate is a tool that can be used to do early explorations of the significance of uptake of pyrene in the human body following industrial or environmental exposure scenarios. And it can be used to optimize the sampling time and urine sampling frequency of a biomonitoring program.

Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles.

PubMed

Mirinejad, Hossein; Jayakumar, Paramsothy; Ersal, Tulga

2018-04-01

This paper presents a behavioral model representing the human steering performance in teleoperated unmanned ground vehicles (UGVs). Human steering performance in teleoperation is considerably different from the performance in regular onboard driving situations due to significant communication delays in teleoperation systems and limited information human teleoperators receive from the vehicle sensory system. Mathematical models capturing the teleoperation performance are a key to making the development and evaluation of teleoperated UGV technologies fully simulation based and thus more rapid and cost-effective. However, driver models developed for the typical onboard driving case do not readily address this need. To fill the gap, this paper adopts a cognitive model that was originally developed for a typical highway driving scenario and develops a tuning strategy that adjusts the model parameters in the absence of human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path-following task. Based on data collected from a human subject test study, it is shown that the tuned model can predict both the trend of changes in driver performance for different driving conditions and the best steering performance of human subjects in all driving conditions considered. The proposed model with the tuning strategy has a satisfactory performance in predicting human steering behavior in the task of teleoperated path following of UGVs. The established model is a suited candidate to be used in place of human drivers for simulation-based studies of UGV mobility in teleoperation systems.

Physicochemical properties of dietary phytochemicals can predict their passive absorption in the human small intestine.

PubMed

Selby-Pham, Sophie N B; Miller, Rosalind B; Howell, Kate; Dunshea, Frank; Bennett, Louise E

2017-05-16

A diet high in phytochemical-rich plant foods is associated with reducing the risk of chronic diseases such as cardiovascular and neurodegenerative diseases, obesity, diabetes and cancer. Oxidative stress and inflammation (OSI) is the common component underlying these chronic diseases. Whilst the positive health effects of phytochemicals and their metabolites have been demonstrated to regulate OSI, the timing and absorption for best effect is not well understood. We developed a model to predict the time to achieve maximal plasma concentration (T max ) of phytochemicals in fruits and vegetables. We used a training dataset containing 67 dietary phytochemicals from 31 clinical studies to develop the model and validated the model using three independent datasets comprising a total of 108 dietary phytochemicals and 98 pharmaceutical compounds. The developed model based on dietary intake forms and the physicochemical properties lipophilicity and molecular mass accurately predicts T max of dietary phytochemicals and pharmaceutical compounds over a broad range of chemical classes. This is the first direct model to predict T max of dietary phytochemicals in the human body. The model informs the clinical dosing frequency for optimising uptake and sustained presence of dietary phytochemicals in circulation, to maximise their bio-efficacy for positively affect human health and managing OSI in chronic diseases.

Tandem internal models execute motor learning in the cerebellum.

PubMed

Honda, Takeru; Nagao, Soichi; Hashimoto, Yuji; Ishikawa, Kinya; Yokota, Takanori; Mizusawa, Hidehiro; Ito, Masao

2018-06-25

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition. Copyright © 2018 the Author(s). Published by PNAS.

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

PubMed Central

Biewener, Andrew A.; Wakeling, James M.

2017-01-01

ABSTRACT Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force–length and force–velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasound-based measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed–high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types. PMID:28202584

Least-cost transportation networks predict spatial interaction of invasion vectors.

PubMed

Drake, D Andrew R; Mandrak, Nicholas E

2010-12-01

Human-mediated dispersal among aquatic ecosystems often results in biotic transfer between drainage basins. Such activities may circumvent biogeographic factors, with considerable ecological, evolutionary, and economic implications. However, the efficacy of predictions concerning community changes following inter-basin movements are limited, often because the dispersal mechanism is poorly understood (e.g., quantified only partially). To date, spatial-interaction models that predict the movement of humans as vectors of biotic transfer have not incorporated patterns of human movement through transportation networks. As a necessary first step to determine the role of anglers as invasion vectors across a land-lake ecosystem, we investigate their movement potential within Ontario, Canada. To determine possible model improvements resulting from inclusion of network travel, spatial-interaction models were constructed using standard Euclidean (e.g., straight-line) distance measures and also with distances derived from least-cost routing of human transportation networks. Model comparisons determined that least-cost routing both provided the most parsimonious model and also excelled at forecasting spatial interactions, with a proportion of 0.477 total movement deviance explained. The distribution of movements was characterized by many relatively short to medium travel distances (median = 292.6 km) with fewer lengthier distances (75th percentile = 484.6 km, 95th percentile = 775.2 km); however, even the shortest movements were sufficient to overcome drainage-basin boundaries. Ranking of variables in order of their contribution within the most parsimonious model determined that distance traveled, origin outflow, lake attractiveness, and sportfish richness significantly influence movement patterns. Model improvements associated with least-cost routing of human transportation networks imply that patterns of human-mediated invasion are fundamentally linked to the spatial configuration and relative impedance of human transportation networks, placing increased importance on understanding their contribution to the invasion process.

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

PubMed Central

Saylor, Kyle; Zhang, Chenming

2017-01-01

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down into different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies’ ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. PMID:27473014

ASSESSING A COMPUTER MODEL FOR PREDICTING HUMAN EXPOSURE TO PM2.5

EPA Science Inventory

This paper compares outputs of a model for predicting PM2.5 exposure with experimental data obtained from exposure studies of selected subpopulations. The exposure model is built on a WWW platform called pCNEM, "A PC Version of pNEM." Exposure models created by pCNEM are sim...

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

The evolution of human phenotypic plasticity: age and nutritional status at maturity.

PubMed

Gage, Timothy B

2003-08-01

Several evolutionary optimal models of human plasticity in age and nutritional status at reproductive maturation are proposed and their dynamics examined. These models differ from previously published models because fertility is not assumed to be a function of body size or nutritional status. Further, the models are based on explicitly human demographic patterns, that is, model human life-tables, model human fertility tables, and, a nutrient flow-based model of maternal nutritional status. Infant survival (instead of fertility as in previous models) is assumed to be a function of maternal nutritional status. Two basic models are examined. In the first the cost of reproduction is assumed to be a constant proportion of total nutrient flow. In the second the cost of reproduction is constant for each birth. The constant proportion model predicts a negative slope of age and nutritional status at maturation. The constant cost per birth model predicts a positive slope of age and nutritional status at maturation. Either model can account for the secular decline in menarche observed over the last several centuries in Europe. A search of the growth literature failed to find definitive empirical documentation of human phenotypic plasticity in age and nutritional status at maturation. Most research strategies confound genetics with phenotypic plasticity. The one study that reports secular trends suggests a marginally insignificant, but positive slope. This view tends to support the constant cost per birth model.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

Optimality Principles for Model-Based Prediction of Human Gait

PubMed Central

Ackermann, Marko; van den Bogert, Antonie J.

2010-01-01

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient’s gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait. PMID:20074736

Non-Markovian character in human mobility: Online and offline.

PubMed

Zhao, Zhi-Dan; Cai, Shi-Min; Lu, Yang

2015-06-01

The dynamics of human mobility characterizes the trajectories that humans follow during their daily activities and is the foundation of processes from epidemic spreading to traffic prediction and information recommendation. In this paper, we investigate a massive data set of human activity, including both online behavior of browsing websites and offline one of visiting towers based mobile terminations. The non-Markovian character observed from both online and offline cases is suggested by the scaling law in the distribution of dwelling time at individual and collective levels, respectively. Furthermore, we argue that the lower entropy and higher predictability in human mobility for both online and offline cases may originate from this non-Markovian character. However, the distributions of individual entropy and predictability show the different degrees of non-Markovian character between online and offline cases. To account for non-Markovian character in human mobility, we apply a protype model with three basic ingredients, namely, preferential return, inertial effect, and exploration to reproduce the dynamic process of online and offline human mobilities. The simulations show that the model has an ability to obtain characters much closer to empirical observations.

Systematic Reviews of Animal Models: Methodology versus Epistemology

PubMed Central

Greek, Ray; Menache, Andre

2013-01-01

Systematic reviews are currently favored methods of evaluating research in order to reach conclusions regarding medical practice. The need for such reviews is necessitated by the fact that no research is perfect and experts are prone to bias. By combining many studies that fulfill specific criteria, one hopes that the strengths can be multiplied and thus reliable conclusions attained. Potential flaws in this process include the assumptions that underlie the research under examination. If the assumptions, or axioms, upon which the research studies are based, are untenable either scientifically or logically, then the results must be highly suspect regardless of the otherwise high quality of the studies or the systematic reviews. We outline recent criticisms of animal-based research, namely that animal models are failing to predict human responses. It is this failure that is purportedly being corrected via systematic reviews. We then examine the assumption that animal models can predict human outcomes to perturbations such as disease or drugs, even under the best of circumstances. We examine the use of animal models in light of empirical evidence comparing human outcomes to those from animal models, complexity theory, and evolutionary biology. We conclude that even if legitimate criticisms of animal models were addressed, through standardization of protocols and systematic reviews, the animal model would still fail as a predictive modality for human response to drugs and disease. Therefore, systematic reviews and meta-analyses of animal-based research are poor tools for attempting to reach conclusions regarding human interventions. PMID:23372426

Learning a Continuous-Time Streaming Video QoE Model.

PubMed

Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C

2018-05-01

Over-the-top adaptive video streaming services are frequently impacted by fluctuating network conditions that can lead to rebuffering events (stalling events) and sudden bitrate changes. These events visually impact video consumers' quality of experience (QoE) and can lead to consumer churn. The development of models that can accurately predict viewers' instantaneous subjective QoE under such volatile network conditions could potentially enable the more efficient design of quality-control protocols for media-driven services, such as YouTube, Amazon, Netflix, and so on. However, most existing models only predict a single overall QoE score on a given video and are based on simple global video features, without accounting for relevant aspects of human perception and behavior. We have created a QoE evaluator, called the time-varying QoE Indexer, that accounts for interactions between stalling events, analyzes the spatial and temporal content of a video, predicts the perceptual video quality, models the state of the client-side data buffer, and consequently predicts continuous-time quality scores that agree quite well with human opinion scores. The new QoE predictor also embeds the impact of relevant human cognitive factors, such as memory and recency, and their complex interactions with the video content being viewed. We evaluated the proposed model on three different video databases and attained standout QoE prediction performance.

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

War, space, and the evolution of Old World complex societies.

PubMed

Turchin, Peter; Currie, Thomas E; Turner, Edward A L; Gavrilets, Sergey

2013-10-08

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today, typically organized as states? Why is there so much variation in the ability of different human populations to construct viable states? Existing theories are usually formulated as verbal models and, as a result, do not yield sharply defined, quantitative predictions that could be unambiguously tested with data. Here we develop a cultural evolutionary model that predicts where and when the largest-scale complex societies arose in human history. The central premise of the model, which we test, is that costly institutions that enabled large human groups to function without splitting up evolved as a result of intense competition between societies-primarily warfare. Warfare intensity, in turn, depended on the spread of historically attested military technologies (e.g., chariots and cavalry) and on geographic factors (e.g., rugged landscape). The model was simulated within a realistic landscape of the Afroeurasian landmass and its predictions were tested against a large dataset documenting the spatiotemporal distribution of historical large-scale societies in Afroeurasia between 1,500 BCE and 1,500 CE. The model-predicted pattern of spread of large-scale societies was very similar to the observed one. Overall, the model explained 65% of variance in the data. An alternative model, omitting the effect of diffusing military technologies, explained only 16% of variance. Our results support theories that emphasize the role of institutions in state-building and suggest a possible explanation why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita.

War, space, and the evolution of Old World complex societies

PubMed Central

Turchin, Peter; Currie, Thomas E.; Turner, Edward A. L.; Gavrilets, Sergey

2013-01-01

How did human societies evolve from small groups, integrated by face-to-face cooperation, to huge anonymous societies of today, typically organized as states? Why is there so much variation in the ability of different human populations to construct viable states? Existing theories are usually formulated as verbal models and, as a result, do not yield sharply defined, quantitative predictions that could be unambiguously tested with data. Here we develop a cultural evolutionary model that predicts where and when the largest-scale complex societies arose in human history. The central premise of the model, which we test, is that costly institutions that enabled large human groups to function without splitting up evolved as a result of intense competition between societies—primarily warfare. Warfare intensity, in turn, depended on the spread of historically attested military technologies (e.g., chariots and cavalry) and on geographic factors (e.g., rugged landscape). The model was simulated within a realistic landscape of the Afroeurasian landmass and its predictions were tested against a large dataset documenting the spatiotemporal distribution of historical large-scale societies in Afroeurasia between 1,500 BCE and 1,500 CE. The model-predicted pattern of spread of large-scale societies was very similar to the observed one. Overall, the model explained 65% of variance in the data. An alternative model, omitting the effect of diffusing military technologies, explained only 16% of variance. Our results support theories that emphasize the role of institutions in state-building and suggest a possible explanation why a long history of statehood is positively correlated with political stability, institutional quality, and income per capita. PMID:24062433

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

Humanized mouse lines and their application for prediction of human drug metabolism and toxicological risk assessment

PubMed Central

Cheung, Connie; Gonzalez, Frank J

2008-01-01

Cytochrome P450s (P450s) are important enzymes involved in the metabolism of xenobiotics, particularly clinically used drugs, and are also responsible for metabolic activation of chemical carcinogens and toxins. Many xenobiotics can activate nuclear receptors that in turn induce the expression of genes encoding xenobiotic metabolizing enzymes and drug transporters. Marked species differences in the expression and regulation of cytochromes P450 and xenobiotic nuclear receptors exist. Thus obtaining reliable rodent models to accurately reflect human drug and carcinogen metabolism is severely limited. Humanized transgenic mice were developed in an effort to create more reliable in vivo systems to study and predict human responses to xenobiotics. Human P450s or human xenobiotic-activated nuclear receptors were introduced directly or replaced the corresponding mouse gene, thus creating “humanized” transgenic mice. Mice expressing human CYP1A1/CYP1A2, CYP2E1, CYP2D6, CYP3A4, CY3A7, PXR, PPARα were generated and characterized. These humanized mouse models offers a broad utility in the evaluation and prediction of toxicological risk that may aid in the development of safer drugs. PMID:18682571

Electric field prediction for a human body-electric machine system.

PubMed

Ioannides, Maria G; Papadopoulos, Peter J; Dimitropoulou, Eugenia

2004-01-01

A system consisting of an electric machine and a human body is studied and the resulting electric field is predicted. A 3-phase induction machine operating at full load is modeled considering its geometry, windings, and materials. A human model is also constructed approximating its geometry and the electric properties of tissues. Using the finite element technique the electric field distribution in the human body is determined for a distance of 1 and 5 m from the machine and its effects are studied. Particularly, electric field potential variations are determined at specific points inside the human body and for these points the electric field intensity is computed and compared to the limit values for exposure according to international standards.

HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy.

PubMed

Hu, Huan; Zhang, Li; Ai, Haixin; Zhang, Hui; Fan, Yetian; Zhao, Qi; Liu, Hongsheng

2018-03-27

LncRNA plays an important role in many biological and disease progression by binding to related proteins. However, the experimental methods for studying lncRNA-protein interactions are time-consuming and expensive. Although there are a few models designed to predict the interactions of ncRNA-protein, they all have some common drawbacks that limit their predictive performance. In this study, we present a model called HLPI-Ensemble designed specifically for human lncRNA-protein interactions. HLPI-Ensemble adopts the ensemble strategy based on three mainstream machine learning algorithms of Support Vector Machines (SVM), Random Forests (RF) and Extreme Gradient Boosting (XGB) to generate HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble, respectively. The results of 10-fold cross-validation show that HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble achieved AUCs of 0.95, 0.96 and 0.96, respectively, in the test dataset. Furthermore, we compared the performance of the HLPI-Ensemble models with the previous models through external validation dataset. The results show that the false positives (FPs) of HLPI-Ensemble models are much lower than that of the previous models, and other evaluation indicators of HLPI-Ensemble models are also higher than those of the previous models. It is further showed that HLPI-Ensemble models are superior in predicting human lncRNA-protein interaction compared with previous models. The HLPI-Ensemble is publicly available at: http://ccsipb.lnu.edu.cn/hlpiensemble/ .

Parametric convergence sensitivity and validation of a finite element model of the human lumbar spine.

PubMed

Ayturk, Ugur M; Puttlitz, Christian M

2011-08-01

The primary objective of this study was to generate a finite element model of the human lumbar spine (L1-L5), verify mesh convergence for each tissue constituent and perform an extensive validation using both kinematic/kinetic and stress/strain data. Mesh refinement was accomplished via convergence of strain energy density (SED) predictions for each spinal tissue. The converged model was validated based on range of motion, intradiscal pressure, facet force transmission, anterolateral cortical bone strain and anterior longitudinal ligament deformation predictions. Changes in mesh resolution had the biggest impact on SED predictions under axial rotation loading. Nonlinearity of the moment-rotation curves was accurately simulated and the model predictions on the aforementioned parameters were in good agreement with experimental data. The validated and converged model will be utilised to study the effects of degeneration on the lumbar spine biomechanics, as well as to investigate the mechanical underpinning of the contemporary treatment strategies.

Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Wolpert, David H.; Backhaus, Scott; Bent, Russell; Bono, James; Tracey, Brendan

2011-01-01

This paper introduces a novel framework for modeling interacting humans in a multi-stage game environment by combining concepts from game theory and reinforcement learning. The proposed model has the following desirable characteristics: (1) Bounded rational players, (2) strategic (i.e., players account for one anothers reward functions), and (3) is computationally feasible even on moderately large real-world systems. To do this we extend level-K reasoning to policy space to, for the first time, be able to handle multiple time steps. This allows us to decompose the problem into a series of smaller ones where we can apply standard reinforcement learning algorithms. We investigate these ideas in a cyber-battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering.

PubMed

Markkula, Gustav; Boer, Erwin; Romano, Richard; Merat, Natasha

2018-06-01

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework's main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Labacqz, J. Victor (Technical Monitor)

1997-01-01

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control.

PubMed

Killeen, G F; McKenzie, F E; Foy, B D; Schieffelin, C; Billingsley, P F; Beier, J C

2000-05-01

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other's effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (+/- SD) that are 1.13 +/- 0.37 (range = 0.84-1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education.

Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis

PubMed Central

Schearer, Eric M.; Liao, Yu-Wei; Perreault, Eric J.; Tresch, Matthew C.; Memberg, William D.; Kirsch, Robert F.; Lynch, Kevin M.

2016-01-01

We present a method to identify the dynamics of a human arm controlled by an implanted functional electrical stimulation neuroprosthesis. The method uses Gaussian process regression to predict shoulder and elbow torques given the shoulder and elbow joint positions and velocities and the electrical stimulation inputs to muscles. We compare the accuracy of torque predictions of nonparametric, semiparametric, and parametric model types. The most accurate of the three model types is a semiparametric Gaussian process model that combines the flexibility of a black box function approximator with the generalization power of a parameterized model. The semiparametric model predicted torques during stimulation of multiple muscles with errors less than 20% of the total muscle torque and passive torque needed to drive the arm. The identified model allows us to define an arbitrary reaching trajectory and approximately determine the muscle stimulations required to drive the arm along that trajectory. PMID:26955041

Models for predicting disinfection byproduct (DBP) formation in drinking waters: a chronological review.

PubMed

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2009-07-01

Disinfection for the supply of safe drinking water forms a variety of known and unknown byproducts through reactions between the disinfectants and natural organic matter. Chronic exposure to disinfection byproducts through the ingestion of drinking water, inhalation and dermal contact during regular indoor activities (e.g., showering, bathing, cooking) may pose cancer and non-cancer risks to human health. Since their discovery in drinking water in 1974, numerous studies have presented models to predict DBP formation in drinking water. To date, more than 48 scientific publications have reported 118 models to predict DBP formation in drinking waters. These models were developed through laboratory and field-scale experiments using raw, pretreated and synthetic waters. This paper aims to review DBP predictive models, analyze the model variables, assess the model advantages and limitations, and to determine their applicability to different water supply systems. The paper identifies the current challenges and future research needs to better control DBP formation. Finally, important directions for future research are recommended to protect human health and to follow the best management practices.

The human placental perfusion model: a systematic review and development of a model to predict in vivo transfer of therapeutic drugs.

PubMed

Hutson, J R; Garcia-Bournissen, F; Davis, A; Koren, G

2011-07-01

Dual perfusion of a single placental lobule is the only experimental model to study human placental transfer of substances in organized placental tissue. To date, there has not been any attempt at a systematic evaluation of this model. The aim of this study was to systematically evaluate the perfusion model in predicting placental drug transfer and to develop a pharmacokinetic model to account for nonplacental pharmacokinetic parameters in the perfusion results. In general, the fetal-to-maternal drug concentration ratios matched well between placental perfusion experiments and in vivo samples taken at the time of delivery of the infant. After modeling for differences in maternal and fetal/neonatal protein binding and blood pH, the perfusion results were able to accurately predict in vivo transfer at steady state (R² = 0.85, P < 0.0001). Placental perfusion experiments can be used to predict placental drug transfer when adjusting for extra parameters and can be useful for assessing drug therapy risks and benefits in pregnancy.

A stochastic whole-body physiologically based pharmacokinetic model to assess the impact of inter-individual variability on tissue dosimetry over the human lifespan.

PubMed

Beaudouin, Rémy; Micallef, Sandrine; Brochot, Céline

2010-06-01

Physiologically based pharmacokinetic (PBPK) models have proven to be successful in integrating and evaluating the influence of age- or gender-dependent changes with respect to the pharmacokinetics of xenobiotics throughout entire lifetimes. Nevertheless, for an effective application of toxicokinetic modelling to chemical risk assessment, a PBPK model has to be detailed enough to include all the multiple tissues that could be targeted by the various xenobiotics present in the environment. For this reason, we developed a PBPK model based on a detailed compartmentalization of the human body and parameterized with new relationships describing the time evolution of physiological and anatomical parameters. To take into account the impact of human variability on the predicted toxicokinetics, we defined probability distributions for key parameters related to the xenobiotics absorption, distribution, metabolism and excretion. The model predictability was evaluated by a direct comparison between computational predictions and experimental data for the internal concentrations of two chemicals (1,3-butadiene and 2,3,7,8-tetrachlorodibenzo-p-dioxin). A good agreement between predictions and observed data was achieved for different scenarios of exposure (e.g., acute or chronic exposure and different populations). Our results support that the general stochastic PBPK model can be a valuable computational support in the area of chemical risk analysis. (c)2010 Elsevier Inc. All rights reserved.

Development of a Zealand white rabbit deposition model to study inhalation anthrax

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

Asgharian, Bahman; Price, Owen; Kabilan, Senthil

Despite using rabbits in several inhalation exposure experiments to study diseases such as anthrax, there is a lack of understanding regarding deposition characteristics and fate of inhaled particles (bio-aerosols and viruses) in the respiratory tracts of rabbits. Such information allows dosimetric extrapolation to humans to inform human outcomes. The lung geometry of the New Zealand white rabbit (referred to simply as rabbits throughout the article) was constructed using recently acquired scanned images of the conducting airways of rabbits and available information on its acinar region. In addition, functional relationships were developed for the lung and breathing parameters of rabbits asmore » a function of body weight. The lung geometry and breathing parameters were used to extend the existing deposition model for humans and several other species to rabbits. Evaluation of the deposition model for rabbits was made by comparing predictions with available measurements in the literature. Deposition predictions in the lungs of rabbits indicated smaller deposition fractions compared to those found in humans across various particle diameter ranges. The application of the deposition model for rabbits was demonstrated by extrapolating deposition predictions in rabbits to find equivalent human exposure concentrations assuming the same dose-response relationship between the two species. Human equivalent exposure concentration levels were found to be much smaller than those for rabbits.« less

In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

An integrated approach to rotorcraft human factors research

NASA Technical Reports Server (NTRS)

Hart, Sandra G.; Hartzell, E. James; Voorhees, James W.; Bucher, Nancy M.; Shively, R. Jay

1988-01-01

As the potential of civil and military helicopters has increased, more complex and demanding missions in increasingly hostile environments have been required. Users, designers, and manufacturers have an urgent need for information about human behavior and function to create systems that take advantage of human capabilities, without overloading them. Because there is a large gap between what is known about human behavior and the information needed to predict pilot workload and performance in the complex missions projected for pilots of advanced helicopters, Army and NASA scientists are actively engaged in Human Factors Research at Ames. The research ranges from laboratory experiments to computational modeling, simulation evaluation, and inflight testing. Information obtained in highly controlled but simpler environments generates predictions which can be tested in more realistic situations. These results are used, in turn, to refine theoretical models, provide the focus for subsequent research, and ensure operational relevance, while maintaining predictive advantages. The advantages and disadvantages of each type of research are described along with examples of experimental results.

Modeling and Evaluating Pilot Performance in NextGen: Review of and Recommendations Regarding Pilot Modeling Efforts, Architectures, and Validation Studies

NASA Technical Reports Server (NTRS)

Wickens, Christopher; Sebok, Angelia; Keller, John; Peters, Steve; Small, Ronald; Hutchins, Shaun; Algarin, Liana; Gore, Brian Francis; Hooey, Becky Lee; Foyle, David C.

2013-01-01

NextGen operations are associated with a variety of changes to the national airspace system (NAS) including changes to the allocation of roles and responsibilities among operators and automation, the use of new technologies and automation, additional information presented on the flight deck, and the entire concept of operations (ConOps). In the transition to NextGen airspace, aviation and air operations designers need to consider the implications of design or system changes on human performance and the potential for error. To ensure continued safety of the NAS, it will be necessary for researchers to evaluate design concepts and potential NextGen scenarios well before implementation. One approach for such evaluations is through human performance modeling. Human performance models (HPMs) provide effective tools for predicting and evaluating operator performance in systems. HPMs offer significant advantages over empirical, human-in-the-loop testing in that (1) they allow detailed analyses of systems that have not yet been built, (2) they offer great flexibility for extensive data collection, (3) they do not require experimental participants, and thus can offer cost and time savings. HPMs differ in their ability to predict performance and safety with NextGen procedures, equipment and ConOps. Models also vary in terms of how they approach human performance (e.g., some focus on cognitive processing, others focus on discrete tasks performed by a human, while others consider perceptual processes), and in terms of their associated validation efforts. The objectives of this research effort were to support the Federal Aviation Administration (FAA) in identifying HPMs that are appropriate for predicting pilot performance in NextGen operations, to provide guidance on how to evaluate the quality of different models, and to identify gaps in pilot performance modeling research, that could guide future research opportunities. This research effort is intended to help the FAA evaluate pilot modeling efforts and select the appropriate tools for future modeling efforts to predict pilot performance in NextGen operations.

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot-induced oscillations (PIO) is formulated. Finally, a model-based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot induced oscillations is formulated. Finally, a model based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Referenceless perceptual fog density prediction model

NASA Astrophysics Data System (ADS)

Choi, Lark Kwon; You, Jaehee; Bovik, Alan C.

2014-02-01

We propose a perceptual fog density prediction model based on natural scene statistics (NSS) and "fog aware" statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.

Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response

PubMed Central

Geerts, Hugo; Spiros, Athan; Roberts, Patrick; Twyman, Roy; Alphs, Larry; Grace, Anthony A.

2012-01-01

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development. PMID:23251349

Blind image quality assessment without training on human opinion scores

NASA Astrophysics Data System (ADS)

Mittal, Anish; Soundararajan, Rajiv; Muralidhar, Gautam S.; Bovik, Alan C.; Ghosh, Joydeep

2013-03-01

We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These `completely blind' models compete well with standard non-blind image quality indices in terms of subjective predictive performance when tested on the large publicly available `LIVE' Image Quality database.

Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human-Automation Interaction.

PubMed

Sebok, Angelia; Wickens, Christopher D

2017-03-01

The objectives were to (a) implement theoretical perspectives regarding human-automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance. Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation. The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system. Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions. The three model-based tools offer useful ways to predict operator performance in complex systems. The three tools offer ways to predict the effects of different automation designs on operator performance.

Finite element analysis of moment-rotation relationships for human cervical spine.

PubMed

Zhang, Qing Hang; Teo, Ee Chon; Ng, Hong Wan; Lee, Vee Sin

2006-01-01

A comprehensive, geometrically accurate, nonlinear C0-C7 FE model of head and cervical spine based on the actual geometry of a human cadaver specimen was developed. The motions of each cervical vertebral level under pure moment loading of 1.0 Nm applied incrementally on the skull to simulate the movements of the head and cervical spine under flexion, tension, axial rotation and lateral bending with the inferior surface of the C7 vertebral body fully constrained were analysed. The predicted range of motion (ROM) for each motion segment were computed and compared with published experimental data. The model predicted the nonlinear moment-rotation relationship of human cervical spine. Under the same loading magnitude, the model predicted the largest rotation in extension, followed by flexion and axial rotation, and least ROM in lateral bending. The upper cervical spines are more flexible than the lower cervical levels. The motions of the two uppermost motion segments account for half (or even higher) of the whole cervical spine motion under rotational loadings. The differences in the ROMs among the lower cervical spines (C3-C7) were relatively small. The FE predicted segmental motions effectively reflect the behavior of human cervical spine and were in agreement with the experimental data. The C0-C7 FE model offers potentials for biomedical and injury studies.

Using APEX to Model Anticipated Human Error: Analysis of a GPS Navigational Aid

NASA Technical Reports Server (NTRS)

VanSelst, Mark; Freed, Michael; Shefto, Michael (Technical Monitor)

1997-01-01

The interface development process can be dramatically improved by predicting design facilitated human error at an early stage in the design process. The approach we advocate is to SIMULATE the behavior of a human agent carrying out tasks with a well-specified user interface, ANALYZE the simulation for instances of human error, and then REFINE the interface or protocol to minimize predicted error. This approach, incorporated into the APEX modeling architecture, differs from past approaches to human simulation in Its emphasis on error rather than e.g. learning rate or speed of response. The APEX model consists of two major components: (1) a powerful action selection component capable of simulating behavior in complex, multiple-task environments; and (2) a resource architecture which constrains cognitive, perceptual, and motor capabilities to within empirically demonstrated limits. The model mimics human errors arising from interactions between limited human resources and elements of the computer interface whose design falls to anticipate those limits. We analyze the design of a hand-held Global Positioning System (GPS) device used for radical and navigational decisions in small yacht recalls. The analysis demonstrates how human system modeling can be an effective design aid, helping to accelerate the process of refining a product (or procedure).

New Integrated Modeling Capabilities: MIDAS' Recent Behavioral Enhancements

NASA Technical Reports Server (NTRS)

Gore, Brian F.; Jarvis, Peter A.

2005-01-01

The Man-machine Integration Design and Analysis System (MIDAS) is an integrated human performance modeling software tool that is based on mechanisms that underlie and cause human behavior. A PC-Windows version of MIDAS has been created that integrates the anthropometric character "Jack (TM)" with MIDAS' validated perceptual and attention mechanisms. MIDAS now models multiple simulated humans engaging in goal-related behaviors. New capabilities include the ability to predict situations in which errors and/or performance decrements are likely due to a variety of factors including concurrent workload and performance influencing factors (PIFs). This paper describes a new model that predicts the effects of microgravity on a mission specialist's performance, and its first application to simulating the task of conducting a Life Sciences experiment in space according to a sequential or parallel schedule of performance.

Alterations in choice behavior by manipulations of world model.

PubMed

Green, C S; Benson, C; Kersten, D; Schrater, P

2010-09-14

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users.

PubMed

Veksler, Vladislav D; Buchler, Norbou; Hoffman, Blaine E; Cassenti, Daniel N; Sample, Char; Sugrim, Shridat

2018-01-01

Computational models of cognitive processes may be employed in cyber-security tools, experiments, and simulations to address human agency and effective decision-making in keeping computational networks secure. Cognitive modeling can addresses multi-disciplinary cyber-security challenges requiring cross-cutting approaches over the human and computational sciences such as the following: (a) adversarial reasoning and behavioral game theory to predict attacker subjective utilities and decision likelihood distributions, (b) human factors of cyber tools to address human system integration challenges, estimation of defender cognitive states, and opportunities for automation, (c) dynamic simulations involving attacker, defender, and user models to enhance studies of cyber epidemiology and cyber hygiene, and (d) training effectiveness research and training scenarios to address human cyber-security performance, maturation of cyber-security skill sets, and effective decision-making. Models may be initially constructed at the group-level based on mean tendencies of each subject's subgroup, based on known statistics such as specific skill proficiencies, demographic characteristics, and cultural factors. For more precise and accurate predictions, cognitive models may be fine-tuned to each individual attacker, defender, or user profile, and updated over time (based on recorded behavior) via techniques such as model tracing and dynamic parameter fitting.

Chimeric mice transplanted with human hepatocytes as a model for prediction of human drug metabolism and pharmacokinetics.

PubMed

Sanoh, Seigo; Ohta, Shigeru

2014-03-01

Preclinical studies in animal models are used routinely during drug development, but species differences of pharmacokinetics (PK) between animals and humans have to be taken into account in interpreting the results. Human hepatocytes are also widely used to examine metabolic activities mediated by cytochrome P450 (P450) and other enzymes, but such in vitro metabolic studies also have limitations. Recently, chimeric mice with humanized liver (h-chimeric mice), generated by transplantation of human donor hepatocytes, have been developed as a model for the prediction of metabolism and PK in humans, using both in vitro and in vivo approaches. The expression of human-specific metabolic enzymes and metabolic activities was confirmed in humanized liver of h-chimeric mice with high replacement ratios, and several reports indicate that the profiles of P450 and non-P450 metabolism in these mice adequately reflect those in humans. Further, the combined use of h-chimeric mice and r-chimeric mice, in which endogenous hepatocytes are replaced with rat hepatocytes, is a promising approach for evaluation of species differences in drug metabolism. Recent work has shown that data obtained in h-chimeric mice enable the semi-quantitative prediction of not only metabolites, but also PK parameters, such as hepatic clearance, of drug candidates in humans, although some limitations remain because of differences in the metabolic activities, hepatic blood flow and liver structure between humans and mice. In addition, fresh h-hepatocytes can be isolated reproducibly from h-chimeric mice for metabolic studies. Copyright © 2013 John Wiley & Sons, Ltd.

Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease.

PubMed

Abraham, Gad; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

2013-02-01

A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic-net penalized support-vector machine models, a mixed-effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false-positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome-wide SNP profiles across eight complex diseases within cross-validation, lasso and elastic-net models achieved substantially better predictive ability in celiac disease, type 1 diabetes, and Crohn's disease, and had equivalent predictive ability in the rest, with the results in celiac disease strongly replicating between independent datasets. We investigated the effect of linkage disequilibrium on the predictive models, showing that the penalized methods leverage this information to their advantage, compared with methods that assume SNP independence. Our findings show that sparse penalized approaches are robust across different disease architectures, producing as good as or better phenotype predictions and variance explained. This has fundamental ramifications for the selection and future development of methods to genetically predict human disease. © 2012 WILEY PERIODICALS, INC.

Construction and evaluation of thoracic injury risk curves for a finite element human body model in frontal car crashes.

PubMed

Mendoza-Vazquez, Manuel; Davidsson, Johan; Brolin, Karin

2015-12-01

There is a need to improve the protection to the thorax of occupants in frontal car crashes. Finite element human body models are a more detailed representation of humans than anthropomorphic test devices (ATDs). On the other hand, there is no clear consensus on the injury criteria and the thresholds to use with finite element human body models to predict rib fractures. The objective of this study was to establish a set of injury risk curves to predict rib fractures using a modified Total HUman Model for Safety (THUMS). Injury criteria at the global, structural and material levels were computed with a modified THUMS in matched Post Mortem Human Subjects (PMHSs) tests. Finally, the quality of each injury risk curve was determined. For the included PMHS tests and the modified THUMS, DcTHOR and shear stress were the criteria at the global and material levels that reached an acceptable quality. The injury risk curves at the structural level did not reach an acceptable quality. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Modeling and Simulation to Predict Operator Performance and Automation-Induced Complacency With Robotic Automation: A Case Study and Empirical Validation.

PubMed

Wickens, Christopher D; Sebok, Angelia; Li, Huiyang; Sarter, Nadine; Gacy, Andrew M

2015-09-01

The aim of this study was to develop and validate a computational model of the automation complacency effect, as operators work on a robotic arm task, supported by three different degrees of automation. Some computational models of complacency in human-automation interaction exist, but those are formed and validated within the context of fairly simplified monitoring failures. This research extends model validation to a much more complex task, so that system designers can establish, without need for human-in-the-loop (HITL) experimentation, merits and shortcomings of different automation degrees. We developed a realistic simulation of a space-based robotic arm task that could be carried out with three different levels of trajectory visualization and execution automation support. Using this simulation, we performed HITL testing. Complacency was induced via several trials of correctly performing automation and then was assessed on trials when automation failed. Following a cognitive task analysis of the robotic arm operation, we developed a multicomponent model of the robotic operator and his or her reliance on automation, based in part on visual scanning. The comparison of model predictions with empirical results revealed that the model accurately predicted routine performance and predicted the responses to these failures after complacency developed. However, the scanning models do not account for the entire attention allocation effects of complacency. Complacency modeling can provide a useful tool for predicting the effects of different types of imperfect automation. The results from this research suggest that focus should be given to supporting situation awareness in automation development. © 2015, Human Factors and Ergonomics Society.

Predicting Human Clearance of OATP substrates using Cynomolgus monkey: In vitro-in vivo scaling of hepatic uptake clearance.

PubMed

de Bruyn, Tom; Ufuk, Ayse; Cantrill, Carina; Kosa, Rachel E; Bi, Yi-An; Niosi, Mark; Modi, Sweta; Rodrigues, A David; Tremaine, Larry M; Varma, Manthena Vs; Galetin, Aleksandra; Houston, J Brian

2018-05-02

This work explores the utility of the cynomolgus monkey as a preclinical model to predict hepatic uptake clearance mediated by organic anion transporting polypeptide (OATP) transporters. Nine OATP substrates (rosuvastatin, pravastatin, repaglinide, fexofenadine, cerivastatin, telmisartan, pitavastatin, bosentan and valsartan) were investigated in plated cynomolgus monkey and human hepatocytes. Total uptake clearance and passive diffusion were measured in vitro from initial rates in the absence and presence of the OATP inhibitor rifamycin SV, respectively. Total uptake clearance values in plated hepatocytes ranged over three orders of magnitude in both species with a similar rank order and good agreement in the relative contribution of active transport to total uptake between cynomolgus monkey and human. In vivo hepatic clearance for these nine drugs was determined in cynomolgus monkey after intravenous dosing. Hepatic clearances showed a similar range to human parameters and good predictions from respective hepatocyte parameters (with 2.7 and 3.8-fold bias on average, respectively). The use of cross species empirical scaling factors (based on either dataset average or individual drug scaling factor from cynomolgus monkey data) improved prediction (less bias, better concordance) of human hepatic clearance from human hepatocyte data alone. In vitro intracellular binding in hepatocytes also correlated well between species. It is concluded that the minimal species differences observed for the current dataset between cynomolgus monkey and human hepatocyte uptake, both in vitro and in vivo, support future use of this preclinical model to delineate drug hepatic uptake and enable prediction of human in vivo intrinsic hepatic clearance. The American Society for Pharmacology and Experimental Therapeutics.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Impact Response Comparison Between Parametric Human Models and Postmortem Human Subjects with a Wide Range of Obesity Levels.

PubMed

Zhang, Kai; Cao, Libo; Wang, Yulong; Hwang, Eunjoo; Reed, Matthew P; Forman, Jason; Hu, Jingwen

2017-10-01

Field data analyses have shown that obesity significantly increases the occupant injury risks in motor vehicle crashes, but the injury assessment tools for people with obesity are largely lacking. The objectives of this study were to use a mesh morphing method to rapidly generate parametric finite element models with a wide range of obesity levels and to evaluate their biofidelity against impact tests using postmortem human subjects (PMHS). Frontal crash tests using three PMHS seated in a vehicle rear seat compartment with body mass index (BMI) from 24 to 40 kg/m 2 were selected. To develop the human models matching the PMHS geometry, statistical models of external body shape, rib cage, pelvis, and femur were applied to predict the target geometry using age, sex, stature, and BMI. A mesh morphing method based on radial basis functions was used to rapidly morph a baseline human model into the target geometry. The model-predicted body excursions and injury measures were compared to the PMHS tests. Comparisons of occupant kinematics and injury measures between the tests and simulations showed reasonable correlations across the wide range of BMI levels. The parametric human models have the capability to account for the obesity effects on the occupant impact responses and injury risks. © 2017 The Obesity Society.

Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

PubMed

Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

2016-05-01

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

Human mobility prediction from region functions with taxi trajectories.

PubMed

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions.

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including: air pollution concentrations; human activity patterns, such as the amount of time spent outdoors vs. indoors, commuting, wal...

A Rational Analysis of Rule-Based Concept Learning

ERIC Educational Resources Information Center

Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L.

2008-01-01

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images.

PubMed

Dick, Taylor J M; Biewener, Andrew A; Wakeling, James M

2017-05-01

Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force-length and force-velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasound-based measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed-high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types. © 2017. Published by The Company of Biologists Ltd.

Dose-Dependent Model of Caffeine Effects on Human Vigilance during Total Sleep Deprivation

DTIC Science & Technology

2014-05-20

does not consider the absorption of caffeine . This is a reasonable approximation for caffeine when ingested via coffee , tea, energy drinks, and most...Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation Sridhar Ramakrishnan a, Srinivas Laxminarayan a, Nancy J...We modeled the dose-dependent effects of caffeine on human vigilance. The model predicted the effects of both single and repeated caffeine doses

Landscape-scale accessibility of livestock to tigers: implications of spatial grain for modeling predation risk to mitigate human-carnivore conflict.

PubMed

Miller, Jennifer R B; Jhala, Yadvendradev V; Jena, Jyotirmay; Schmitz, Oswald J

2015-03-01

Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human-carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator-prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human-carnivore conflict.

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.

The influence of ligament modelling strategies on the predictive capability of finite element models of the human knee joint.

PubMed

Naghibi Beidokhti, Hamid; Janssen, Dennis; van de Groes, Sebastiaan; Hazrati, Javad; Van den Boogaard, Ton; Verdonschot, Nico

2017-12-08

In finite element (FE) models knee ligaments can represented either by a group of one-dimensional springs, or by three-dimensional continuum elements based on segmentations. Continuum models closer approximate the anatomy, and facilitate ligament wrapping, while spring models are computationally less expensive. The mechanical properties of ligaments can be based on literature, or adjusted specifically for the subject. In the current study we investigated the effect of ligament modelling strategy on the predictive capability of FE models of the human knee joint. The effect of literature-based versus specimen-specific optimized material parameters was evaluated. Experiments were performed on three human cadaver knees, which were modelled in FE models with ligaments represented either using springs, or using continuum representations. In spring representation collateral ligaments were each modelled with three and cruciate ligaments with two single-element bundles. Stiffness parameters and pre-strains were optimized based on laxity tests for both approaches. Validation experiments were conducted to evaluate the outcomes of the FE models. Models (both spring and continuum) with subject-specific properties improved the predicted kinematics and contact outcome parameters. Models incorporating literature-based parameters, and particularly the spring models (with the representations implemented in this study), led to relatively high errors in kinematics and contact pressures. Using a continuum modelling approach resulted in more accurate contact outcome variables than the spring representation with two (cruciate ligaments) and three (collateral ligaments) single-element-bundle representations. However, when the prediction of joint kinematics is of main interest, spring ligament models provide a faster option with acceptable outcome. Copyright © 2017 Elsevier Ltd. All rights reserved.

Minipig as a potential translatable model for monoclonal antibody pharmacokinetics after intravenous and subcutaneous administration

PubMed Central

Benincosa, Lisa; Birnböck, Herbert; Boswell, C Andrew; Bumbaca, Daniela; Cowan, Kyra J; Danilenko, Dimitry M; Daugherty, Ann L; Fielder, Paul J; Grimm, Hans Peter; Joshi, Amita; Justies, Nicole; Kolaitis, Gerry; Lewin-Koh, Nicholas; Li, Jing; McVay, Sami; O'Mahony, Jennifer; Otteneder, Michael; Pantze, Michael; Putnam, Wendy S; Qiu, Zhihua J; Ruppel, Jane; Singer, Thomas; Stauch, Oliver; Theil, Frank-Peter; Visich, Jennifer; Yang, Jihong; Ying, Yong

2012-01-01

Subcutaneous (SC) delivery is a common route of administration for therapeutic monoclonal antibodies (mAbs) with pharmacokinetic (PK)/pharmacodynamic (PD) properties requiring long-term or frequent drug administration. An ideal in vivo preclinical model for predicting human PK following SC administration may be one in which the skin and overall physiological characteristics are similar to that of humans. In this study, the PK properties of a series of therapeutic mAbs following intravenous (IV) and SC administration in Göttingen minipigs were compared with data obtained previously from humans. The present studies demonstrated: (1) minipig is predictive of human linear clearance; (2) the SC bioavailabilities in minipigs are weakly correlated with those in human; (3) minipig mAb SC absorption rates are generally higher than those in human and (4) the SC bioavailability appears to correlate with systemic clearance in minipigs. Given the important role of the neonatal Fc-receptor (FcRn) in the PK of mAbs, the in vitro binding affinities of these IgGs against porcine, human and cynomolgus monkey FcRn were tested. The result showed comparable FcRn binding affinities across species. Further, mAbs with higher isoelectric point tended to have faster systemic clearance and lower SC bioavailability in both minipig and human. Taken together, these data lend increased support for the use of the minipig as an alternative predictive model for human IV and SC PK of mAbs. PMID:22453096

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Pisanich, Gregory; Lebacqz, J. Victor (Technical Monitor)

1998-01-01

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

Human Pluripotent Stem Cell-Based Assay Predicts Developmental Toxicity Potential of ToxCast Chemicals (ACT meeting)

EPA Science Inventory

Worldwide initiatives to screen for toxicity potential among the thousands of chemicals currently in use require inexpensive and high-throughput in vitro models to meet their goals. The devTOX quickPredict platform is an in vitro human pluripotent stem cell-based assay used to as...

Evaluation of 1066 ToxCast Chemicals in a human stem cell assay for developmental toxicity (SOT)

EPA Science Inventory

To increase the diversity of assays used to assess potential developmental toxicity, the ToxCast chemical library was screened in the Stemina devTOX quickPREDICT assay using human embryonic stem (hES) cells. A model for predicting teratogenicity was based on a training set of 23 ...

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.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

PubMed Central

Alves, Vinicius M.; Capuzzi, Stephen J.; Muratov, Eugene; Braga, Rodolpho C.; Thornton, Thomas; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2016-01-01

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential. PMID:28630595

Air pollution exposure prediction approaches used in air pollution epidemiology studies.

PubMed

Özkaynak, Halûk; Baxter, Lisa K; Dionisio, Kathie L; Burke, Janet

2013-01-01

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

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

Hinderliter, Paul M.; Thrall, Karla D.; Corley, Rick A.

Vinyl acetate has been shown to induce nasal lesions in rodents in inhalation bioassays. A physiologically based pharmacokinetic (PBPK) model for vinyl acetate has been used in human risk assessment, but previous in vivo validation was conducted only in rats. Controlled human exposures to vinyl acetate were conducted to provide validation data for the application of the model in humans. Five volunteers were exposed to 1, 5, and 10 ppm 13 C1 , 13 C2 vinyl acetate via inhalation. A probe inserted into thenasopharyngeal region sampled both 13 C1 , 13 C2 vinyl acetate and the major metabolite 13 C1more » , 13 C2 acetaldehyde during rest and light exercise. Nasopharyngeal air concentrations were analyzed in real time by ion trap mass spectrometry (MS/MS). Experimental concentrations of both vinyl acetate and acetaldehyde were then compared to predicted concentrations calculated from the previously published human model. Model predictions of vinyl acetate nasal extraction compared favorably with measured values of vinyl acetate, as did predictions of nasopharyngeal acetaldehyde when compared to measured acetaldehyde. The results showed that the current PBPK model structure and parameterization are appropriate for vinyl acetate. These analyses were conducted from 1 to 10 ppm vinyl acetate, a range relevant to workplace exposure standards but which would not be expected to saturate vinyl acetate metabolism. Risk assessment based on this model further concluded that 24 h per day exposures up to 1 ppm do not present concern regarding cancer or non-cancer toxicity. Validation of the vinyl acetate human PBPK model provides support for these conclusions.« less

Modeling How, When, and What Is Learned in a Simple Fault-Finding Task

ERIC Educational Resources Information Center

Ritter, Frank E.; Bibby, Peter A.

2008-01-01

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These…

A statistical model including age to predict passenger postures in the rear seats of automobiles.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-06-01

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment. Practitioner Summary: The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.

Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

NASA Technical Reports Server (NTRS)

Johannsen, G.; Govindaraj, T.

1980-01-01

The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

Consolidated Human Activity Database (CHAD) for use in human exposure and health studies and predictive models

EPA Pesticide Factsheets

EPA scientists have compiled detailed data on human behavior from 22 separate exposure and time-use studies into CHAD. The database includes more than 54,000 individual study days of detailed human behavior.

Dynamic motion planning of 3D human locomotion using gradient-based optimization.

PubMed

Kim, Hyung Joo; Wang, Qian; Rahmatalla, Salam; Swan, Colby C; Arora, Jasbir S; Abdel-Malek, Karim; Assouline, Jose G

2008-06-01

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.

Prediction of Losartan-Active Carboxylic Acid Metabolite Exposure Following Losartan Administration Using Static and Physiologically Based Pharmacokinetic Models.

PubMed

Nguyen, Hoa Q; Lin, Jian; Kimoto, Emi; Callegari, Ernesto; Tse, Susanna; Obach, R Scott

2017-09-01

The aim of this study was to evaluate a strategy based on static and dynamic physiologically based pharmacokinetic (PBPK) modeling for the prediction of metabolite and parent drug area under the time-concentration curve ratio (AUC m /AUC p ) and their PK profiles in humans using in vitro data when active transport processes are involved in disposition. The strategy was applied to losartan and its pharmacologically active metabolite carboxylosartan as test compounds. Hepatobiliary transport including transport-mediated uptake, canilicular and basolateral efflux, and metabolic clearance estimates were obtained from in vitro studies using human liver microsomes and sandwich-cultured hepatocytes. Human renal clearance of carboxylosartan was estimated from dog renal clearance using allometric scaling approach. All clearance mechanisms were mechanistically incorporated in a static model to predict the relative exposure of carboxylosartan versus losartan (AUC m /AUC p ). The predicted AUC m /AUC p were consistent with the observed data following intravenous and oral administration of losartan. Moreover, the in vitro parameters were used as initial parameters in PBPK permeability-limited disposition models to predict the concentration-time profiles for both parent and its active metabolite after oral administration of losartan. The PBPK model was able to recover the plasma profiles of both losartan and carboxylosartan, further substantiating the validity of this approach. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

PubMed

Li, Xiang; Xu, Youjun; Lai, Luhua; Pei, Jianfeng

2018-05-30

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .

In silico models for the prediction of dose-dependent human hepatotoxicity

NASA Astrophysics Data System (ADS)

Cheng, Ailan; Dixon, Steven L.

2003-12-01

The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure. We discuss our latest findings in this area and present a new, fully general in silico model which is able to predict the occurrence of dose-dependent human hepatotoxicity with greater than 80% accuracy. Utilizing an ensemble recursive partitioning approach, the model classifies compounds as toxic or non-toxic and provides a confidence level to indicate which predictions are most likely to be correct. Only 2D structural information is required and predictions can be made quite rapidly, so this approach is entirely appropriate for data mining applications and for profiling large synthetic and/or virtual libraries.

Using Blur to Affect Perceived Distance and Size

PubMed Central

HELD, ROBERT T.; COOPER, EMILY A.; O’BRIEN, JAMES F.; BANKS, MARTIN S.

2011-01-01

We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image’s contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene’s contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model’s predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently. PMID:21552429

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.

PubMed

Chande, Ruchi D; Hargraves, Rosalyn Hobson; Ortiz-Robinson, Norma; Wayne, Jennifer S

2017-01-01

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone App.

PubMed

Choo, Min Soo; Jeong, Seong Jin; Cho, Sung Yong; Yoo, Changwon; Jeong, Chang Wook; Ku, Ja Hyeon; Oh, Seung-June

2017-04-01

We aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO) and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing. Formulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app. A total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179) and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues. External validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

A viscoelastic model for the prediction of transcranial ultrasound propagation: application for the estimation of shear acoustic properties in the human skull

NASA Astrophysics Data System (ADS)

Pichardo, Samuel; Moreno-Hernández, Carlos; Drainville, Robert Andrew; Sin, Vivian; Curiel, Laura; Hynynen, Kullervo

2017-09-01

A better understanding of ultrasound transmission through the human skull is fundamental to develop optimal imaging and therapeutic applications. In this study, we present global attenuation values and functions that correlate apparent density calculated from computed tomography scans to shear speed of sound. For this purpose, we used a model for sound propagation based on the viscoelastic wave equation (VWE) assuming isotropic conditions. The model was validated using a series of measurements with plates of different plastic materials and angles of incidence of 0°, 15° and 50°. The optimal functions for transcranial ultrasound propagation were established using the VWE, scan measurements of transcranial propagation with an angle of incidence of 40° and a genetic optimization algorithm. Ten (10) locations over three (3) skulls were used for ultrasound frequencies of 270 kHz and 836 kHz. Results with plastic materials demonstrated that the viscoelastic modeling predicted both longitudinal and shear propagation with an average (±s.d.) error of 9(±7)% of the wavelength in the predicted delay and an error of 6.7(±5)% in the estimation of transmitted power. Using the new optimal functions of speed of sound and global attenuation for the human skull, the proposed model predicted the transcranial ultrasound transmission for a frequency of 270 kHz with an expected error in the predicted delay of 5(±2.7)% of the wavelength. The sound propagation model predicted accurately the sound propagation regardless of either shear or longitudinal sound transmission dominated. For 836 kHz, the model predicted accurately in average with an error in the predicted delay of 17(±16)% of the wavelength. Results indicated the importance of the specificity of the information at a voxel level to better understand ultrasound transmission through the skull. These results and new model will be very valuable tools for the future development of transcranial applications of ultrasound therapy and imaging.

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

PubMed

Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

2017-09-01

Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

An investigation of human body model morphing for the assessment of abdomen responses to impact against a population of test subjects.

PubMed

Beillas, Philippe; Berthet, Fabien

2017-05-29

Human body models have the potential to better describe the human anatomy and variability than dummies. However, data sets available to verify the human response to impact are typically limited in numbers, and they are not size or gender specific. The objective of this study was to investigate the use of model morphing methodologies within that context. In this study, a simple human model scaling methodology was developed to morph two detailed human models (Global Human Body Model Consortium models 50th male, M50, and 5th female, F05) to the dimensions of post mortem human surrogates (PMHS) used in published literature. The methodology was then successfully applied to 52 PMHS tested in 14 impact conditions loading the abdomen. The corresponding 104 simulations were compared to the responses of the PMHS and to the responses of the baseline models without scaling (28 simulations). The responses were analysed using the CORA method and peak values. The results suggest that model scaling leads to an improvement of the predicted force and deflection but has more marginal effects on the predicted abdominal compressions. M50 and F05 models scaled to the same PMHS were also found to have similar external responses, but large differences were found between the two sets of models for the strain energy densities in the liver and the spleen for mid-abdomen impact simulations. These differences, which were attributed to the anatomical differences in the abdomen of the baseline models, highlight the importance of the selection of the impact condition for simulation studies, especially if the organ location is not known in the test. While the methodology could be further improved, it shows the feasibility of using model scaling methodologies to compare human models of different sizes and to evaluate scaling approaches within the context of human model validation.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Integrating Economic Models with Biophysical Models in the Willamette Water 2100 Project

NASA Astrophysics Data System (ADS)

Jaeger, W. K.; Plantinga, A.

2013-12-01

This paper highlights the human system modeling components for Willamette Water 2100, a comprehensive, highly integrated study of hydrological, ecological, and human factors affecting water scarcity in the Willamette River Basin (WRB). The project is developing a spatiotemporal simulation model to predict future trajectories of water scarcity, and to evaluate mitigation policies. Economic models of land use and water use are the main human system models in WW2100. Water scarcity depends on both supply and demand for water, and varies greatly across time and space (Jaeger et al., 2013). Thus, the locations of human water use can have enormous influence on where and when water is used, and hence where water scarcity may arise. Modeling the locations of human uses of water (e.g., urban versus agricultural) as well as human values and choices, are the principal quantitative ways that social science can contribute to research of this kind. Our models are empirically-based models of human resource allocation. Each model reflects private behavior (choices by households, farms, firms), institutions (property rights, laws, markets, regulations), public infrastructure (dams, canals, highways), and also 'external drivers' that influence the local economy (migration, population growth, national markets and policies). This paper describes the main model components, emphasizing similarities between human and biophysical components of the overall project, and the model's linkages and feedbacks relevant to our predictions of changes in water scarcity between now and 2100. Results presented include new insights from individual model components as well as available results from the integrated system model. Issues include water scarcity and water quality (temperature) for out-of-stream and instream uses, the impact of urban expansion on water use and potential flood damage. Changes in timing and variability of spring discharge with climate change, as well as changes in human uses of lands in flood-prone areas, will alter the tradeoff for the optimal use of reservoir storage capacity. We emphasize three concepts: i) institutions, ii) scarcity, and iii) the role of social science in projects of this kind. Institutions represent the main instrument or tool that humans use to influence how resources are used, to reduce waste, promote efficiency, and foster predictability. Water scarcity when defined in human normative terms. The concept provides a lens through which to recognize the wide range of ways that water scarcity can arise and persist even in water-abundant settings. We conclude with observations about the role of social science in research on biophysical and human systems. Reference Jaeger, W.K., et al., 2013. Toward a formal definition of water scarcity in natural-human systems. Water Resources Research, Volume 49. Published online: 8 JUL 2013 | DOI: 10.1002/wrcr.20249

Humanizing the zebrafish liver shifts drug metabolic profiles and improves pharmacokinetics of CYP3A4 substrates.

PubMed

Poon, Kar Lai; Wang, Xingang; Ng, Ashley S; Goh, Wei Huang; McGinnis, Claudia; Fowler, Stephen; Carney, Tom J; Wang, Haishan; Ingham, Phillip W

2017-03-01

Understanding and predicting whether new drug candidates will be safe in the clinic is a critical hurdle in pharmaceutical development, that relies in part on absorption, distribution, metabolism, excretion and toxicology studies in vivo. Zebrafish is a relatively new model system for drug metabolism and toxicity studies, offering whole organism screening coupled with small size and potential for high-throughput screening. Through toxicity and absorption analyses of a number of drugs, we find that zebrafish is generally predictive of drug toxicity, although assay outcomes are influenced by drug lipophilicity which alters drug uptake. In addition, liver microsome assays reveal specific differences in metabolism of compounds between human and zebrafish livers, likely resulting from the divergence of the cytochrome P450 superfamily between species. To reflect human metabolism more accurately, we generated a transgenic "humanized" zebrafish line that expresses the major human phase I detoxifying enzyme, CYP3A4, in the liver. Here, we show that this humanized line shows an elevated metabolism of CYP3A4-specific substrates compared to wild-type zebrafish. The generation of this first described humanized zebrafish liver suggests such approaches can enhance the accuracy of the zebrafish model for toxicity prediction.

Predictive data modeling of human type II diabetes related statistics

NASA Astrophysics Data System (ADS)

Jaenisch, Kristina L.; Jaenisch, Holger M.; Handley, James W.; Albritton, Nathaniel G.

2009-04-01

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

A SIMPLIFIED MODEL FOR PREDICTING MALARIA ENTOMOLOGIC INOCULATION RATES BASED ON ENTOMOLOGIC AND PARASITOLOGIC PARAMETERS RELEVANT TO CONTROL

PubMed Central

KILLEEN, GERRY F.; McKENZIE, F. ELLIS; FOY, BRIAN D.; SCHIEFFELIN, CATHERINE; BILLINGSLEY, PETER F.; BEIER, JOHN C.

2008-01-01

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other’s effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (± SD) that are 1.13 ± 0.37 (range = 0.84–1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education. PMID:11289661

DemQSAR: predicting human volume of distribution and clearance of drugs

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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

PubMed

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

2011-12-01

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

A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.

PubMed

Chen, Xing; Huang, Yu-An; You, Zhu-Hong; Yan, Gui-Ying; Wang, Xue-Song

2017-03-01

Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now. In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 ± 0.0054, 0.8301 ± 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement. Matlab codes and dataset explored in this work are available at http://dwz.cn/4oX5mS . xingchen@amss.ac.cn or zhuhongyou@gmail.com or wangxuesongcumt@163.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Use of the HepaRG Cell Line to Assess Potential Human Hepatotoxicity of ToxCast™ Chemicals

EPA Science Inventory

The HepaRG cell line is a promising model system for predicting human hepatotoxicity in part because of the greater capacity to metabolize chemicals than other cell models. We hypothesized that this cell line would be a relevant model for toxicity testing of industrial chemicals....

Agent-based simulation for human-induced hazard analysis.

PubMed

Bulleit, William M; Drewek, Matthew W

2011-02-01

Terrorism could be treated as a hazard for design purposes. For instance, the terrorist hazard could be analyzed in a manner similar to the way that seismic hazard is handled. No matter how terrorism is dealt with in the design of systems, the need for predictions of the frequency and magnitude of the hazard will be required. And, if the human-induced hazard is to be designed for in a manner analogous to natural hazards, then the predictions should be probabilistic in nature. The model described in this article is a prototype model that used agent-based modeling (ABM) to analyze terrorist attacks. The basic approach in this article of using ABM to model human-induced hazards has been preliminarily validated in the sense that the attack magnitudes seem to be power-law distributed and attacks occur mostly in regions where high levels of wealth pass through, such as transit routes and markets. The model developed in this study indicates that ABM is a viable approach to modeling socioeconomic-based infrastructure systems for engineering design to deal with human-induced hazards. © 2010 Society for Risk Analysis.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.« less

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

Human mobility prediction from region functions with taxi trajectories

PubMed Central

Wang, Minjie; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions. PMID:29190708

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

Comparison of Biophysical Characteristics and Predicted Thermophysiological Responses of Three Prototype Body Armor Systems Versus Baseline U.S. Army Body Armor Systems

DTIC Science & Technology

2015-06-19

effective and scientifically valid method of making comparisons of clothing and equipment changes prior to conducting human research. predictive modeling...valid method of making comparisons of clothing and equipment changes prior to conducting human research. 2 INTRODUCTION Modern day...clothing and equipment changes prior to conducting human research. METHODS Ensembles Three different body armor (BA) plus clothing ensembles were

Locally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose

DTIC Science & Technology

2012-09-10

Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States ABSTRACT: Toxicological ...species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related...compounds. However, many popular quantitative structure−activity relationship ( QSAR ) methods that build a single global model by fitting all training

Hepatobiliary Clearance Prediction: Species Scaling From Monkey, Dog, and Rat, and In Vitro-In Vivo Extrapolation of Sandwich-Cultured Human Hepatocytes Using 17 Drugs.

PubMed

Kimoto, Emi; Bi, Yi-An; Kosa, Rachel E; Tremaine, Larry M; Varma, Manthena V S

2017-09-01

Hepatobiliary elimination can be a major clearance pathway dictating the pharmacokinetics of drugs. Here, we first compared the dose eliminated in bile in preclinical species (monkey, dog, and rat) with that in human and further evaluated single-species scaling (SSS) to predict human hepatobiliary clearance. Six compounds dosed in bile duct-cannulated (BDC) monkeys showed biliary excretion comparable to human; and the SSS of hepatobiliary clearance with plasma fraction unbound correction yielded reasonable predictions (within 3-fold). Although dog SSS also showed reasonable predictions, rat overpredicted hepatobiliary clearance for 13 of 24 compounds. Second, we evaluated the translatability of in vitro sandwich-cultured human hepatocytes (SCHHs) to predict human hepatobiliary clearance for 17 drugs. For drugs with no significant active uptake in SCHH studies (i.e., with or without rifamycin SV), measured intrinsic biliary clearance was directly scalable with good predictability (absolute average fold error [AAFE] = 1.6). Drugs showing significant active uptake in SCHH, however, showed improved predictability when scaled based on extended clearance term (AAFE = 2.0), which incorporated sinusoidal uptake along with a global scaling factor for active uptake and the canalicular efflux clearance. In conclusion, SCHH is a useful tool to predict human hepatobiliary clearance, whereas BDC monkey model may provide further confidence in the prospective predictions. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Man-Machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2011-01-01

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface transportation systems as exemplified by the Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by the assistive technologies. One formalized approach to study the impact of assistive technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human-system designs are proposed, developed, and tested. One HPM tool termed the Man-machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human-system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM and simulation environment that facilitates the design, visualization, and computational evaluation of complex man-machine system concepts in simulated operational environments. The paper will discuss a range of aviation specific applications including an approach used to model human error for NASA s Aviation Safety Program, and what-if analyses to evaluate flight deck technologies for NextGen operations. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human-system designs that evaluate assistive technologies: that of (1) model transparency and (2) model validation.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Reductions in global biodiversity loss predicted from conservation spending.

PubMed

Waldron, Anthony; Miller, Daniel C; Redding, Dave; Mooers, Arne; Kuhn, Tyler S; Nibbelink, Nate; Roberts, J Timmons; Tobias, Joseph A; Gittleman, John L

2017-11-16

Halting global biodiversity loss is central to the Convention on Biological Diversity and United Nations Sustainable Development Goals, but success to date has been very limited. A critical determinant of success in achieving these goals is the financing that is committed to maintaining biodiversity; however, financing decisions are hindered by considerable uncertainty over the likely impact of any conservation investment. For greater effectiveness, we need an evidence-based model that shows how conservation spending quantitatively reduces the rate of biodiversity loss. Here we demonstrate such a model, and empirically quantify how conservation investment reduced biodiversity loss in 109 countries (signatories to the Convention on Biological Diversity and Sustainable Development Goals), by a median average of 29% per country between 1996 and 2008. We also show that biodiversity changes in signatory countries can be predicted with high accuracy, using a dual model that balances the effects of conservation investment against those of economic, agricultural and population growth (human development pressures). Decision-makers can use this model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, and then compare these forecasts to any chosen policy target. We find that the impact of spending decreases as human development pressures grow, which implies that funding may need to increase over time. The model offers a flexible tool for balancing the Sustainable Development Goals of human development and maintaining biodiversity, by predicting the dynamic changes in conservation finance that will be needed as human development proceeds.

Reductions in global biodiversity loss predicted from conservation spending

NASA Astrophysics Data System (ADS)

Waldron, Anthony; Miller, Daniel C.; Redding, Dave; Mooers, Arne; Kuhn, Tyler S.; Nibbelink, Nate; Roberts, J. Timmons; Tobias, Joseph A.; Gittleman, John L.

2017-11-01

Halting global biodiversity loss is central to the Convention on Biological Diversity and United Nations Sustainable Development Goals, but success to date has been very limited. A critical determinant of success in achieving these goals is the financing that is committed to maintaining biodiversity; however, financing decisions are hindered by considerable uncertainty over the likely impact of any conservation investment. For greater effectiveness, we need an evidence-based model that shows how conservation spending quantitatively reduces the rate of biodiversity loss. Here we demonstrate such a model, and empirically quantify how conservation investment between 1996 and 2008 reduced biodiversity loss in 109 countries (signatories to the Convention on Biological Diversity and Sustainable Development Goals), by a median average of 29% per country. We also show that biodiversity changes in signatory countries can be predicted with high accuracy, using a dual model that balances the effects of conservation investment against those of economic, agricultural and population growth (human development pressures). Decision-makers can use this model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, and then compare these forecasts to any chosen policy target. We find that the impact of spending decreases as human development pressures grow, which implies that funding may need to increase over time. The model offers a flexible tool for balancing the Sustainable Development Goals of human development and maintaining biodiversity, by predicting the dynamic changes in conservation finance that will be needed as human development proceeds.

Predicting Potential Risk Areas of Human Plague for the Western Usambara Mountains, Lushoto District, Tanzania

PubMed Central

Neerinckx, Simon; Peterson, A. Townsend; Gulinck, Hubert; Deckers, Jozef; Kimaro, Didas; Leirs, Herwig

2010-01-01

A natural focus of plague exists in the Western Usambara Mountains of Tanzania. Despite intense research, questions remain as to why and how plague emerges repeatedly in the same suite of villages. We used human plague incidence data for 1986–2003 in an ecological-niche modeling framework to explore the geographic distribution and ecology of human plague. Our analyses indicate that plague occurrence is related directly to landscape-scale environmental features, yielding a predictive understanding of one set of environmental factors affecting plague transmission in East Africa. Although many environmental variables contribute significantly to these models, the most important are elevation and Enhanced Vegetation Index derivatives. Projections of these models across broader regions predict only 15.5% (under a majority-rule threshold) or 31,997 km2 of East Africa as suitable for plague transmission, but they successfully anticipate most known foci in the region, making possible the development of a risk map of plague. PMID:20207880

Emerging approaches in predictive toxicology.

PubMed

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

Emerging Approaches in Predictive Toxicology

PubMed Central

Zhang, Luoping; McHale, Cliona M.; Greene, Nigel; Snyder, Ronald D.; Rich, Ivan N.; Aardema, Marilyn J.; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2016-01-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. PMID:25044351

Cadmium transfer from contaminated soils to the human body through rice consumption in southern Jiangsu Province, China.

PubMed

Li, Tianyuan; Chang, Qing; Yuan, Xuyin; Li, Jizhou; Ayoko, Godwin A; Frost, Ray L; Chen, Hongyan; Zhang, Xinjian; Song, Yinxian; Song, Wenzhi

2017-06-21

Consumption of crops grown in cadmium-contaminated soils is an important Cd exposure route to humans. The present study utilizes statistical analysis and in vitro digestion experiments to uncover the transfer processes of Cd from soils to the human body through rice consumption. Here, a model was created to predict the levels of bioaccessible Cd in rice grains using phytoavailable Cd quantities in the soil. During the in vitro digestion, a relatively constant ratio between the total and bioaccessible Cd in rice was observed. About 14.89% of Cd in soils was found to be transferred into rice grains and up to 3.19% could be transferred from rice grains to the human body. This model was able to sufficiently predict rice grain cadmium concentrations based on CaCl 2 extracted zinc and cadmium concentrations in soils (R 2 = 0.862). The bioaccessible Cd concentration in rice grains was also able to be predicted using CaCl 2 extracted cadmium from soil (R 2 = 0.892). The models established in this study demonstrated that CaCl 2 is a suitable indicator of total rice Cd concentrations and bioaccessible rice grain Cd concentrations. The chain model approach proposed in this study can be used for the fast and accurate evaluation of human Cd exposure through rice consumption based on the soil conditions in contaminated regions.

Extension of a PBPK model for ethylene glycol and glycolic acid to include the competitive formation and clearance of metabolites associated with kidney toxicity in rats and humans

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

Corley, R.A., E-mail: rick.corley@pnl.gov; Saghir, S.A.; Bartels, M.J.

2011-02-01

A previously developed PBPK model for ethylene glycol and glycolic acid was extended to include glyoxylic acid, oxalic acid, and the precipitation of calcium oxalate that is associated with kidney toxicity in rats and humans. The development and evaluation of the PBPK model was based upon previously published pharmacokinetic studies coupled with measured blood and tissue partition coefficients and rates of in vitro metabolism of glyoxylic acid to oxalic acid, glycine and other metabolites using primary hepatocytes isolated from male Wistar rats and humans. Precipitation of oxalic acid with calcium in the kidneys was assumed to occur only at concentrationsmore » exceeding the thermodynamic solubility product for calcium oxalate. This solubility product can be affected by local concentrations of calcium and other ions that are expressed in the model using an ion activity product estimated from toxicity studies such that calcium oxalate precipitation would be minimal at dietary exposures below the NOAEL for kidney toxicity in the sensitive male Wistar rat. The resulting integrated PBPK predicts that bolus oral or dietary exposures to ethylene glycol would result in typically 1.4-1.6-fold higher peak oxalate levels and 1.6-2-fold higher AUC's for calcium oxalate in kidneys of humans as compared with comparably exposed male Wistar rats over a dose range of 1-1000 mg/kg. The converse (male Wistar rats predicted to have greater oxalate levels in the kidneys than humans) was found for inhalation exposures although no accumulation of calcium oxalate is predicted to occur until exposures are well in excess of the theoretical saturated vapor concentration of 200 mg/m{sup 3}. While the current model is capable of such cross-species, dose, and route-of-exposure comparisons, it also highlights several areas of potential research that will improve confidence in such predictions, especially at low doses relevant for most human exposures.« less

Predicting Adaptive Response to Fadrozole Exposure:Computational Model of the Fathead MinnowsHypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict doseresponse and time-course (...

Humans running in place on water at simulated reduced gravity.

PubMed

Minetti, Alberto E; Ivanenko, Yuri P; Cappellini, Germana; Dominici, Nadia; Lacquaniti, Francesco

2012-01-01

On Earth only a few legged species, such as water strider insects, some aquatic birds and lizards, can run on water. For most other species, including humans, this is precluded by body size and proportions, lack of appropriate appendages, and limited muscle power. However, if gravity is reduced to less than Earth's gravity, running on water should require less muscle power. Here we use a hydrodynamic model to predict the gravity levels at which humans should be able to run on water. We test these predictions in the laboratory using a reduced gravity simulator. We adapted a model equation, previously used by Glasheen and McMahon to explain the dynamics of Basilisk lizard, to predict the body mass, stride frequency and gravity necessary for a person to run on water. Progressive body-weight unloading of a person running in place on a wading pool confirmed the theoretical predictions that a person could run on water, at lunar (or lower) gravity levels using relatively small rigid fins. Three-dimensional motion capture of reflective markers on major joint centers showed that humans, similarly to the Basilisk Lizard and to the Western Grebe, keep the head-trunk segment at a nearly constant height, despite the high stride frequency and the intensive locomotor effort. Trunk stabilization at a nearly constant height differentiates running on water from other, more usual human gaits. The results showed that a hydrodynamic model of lizards running on water can also be applied to humans, despite the enormous difference in body size and morphology.

Predictive Relationships between Secondary School Students' Human Values, Motivational Beliefs, and Self-Regulated Learning Strategies

ERIC Educational Resources Information Center

Tanriseven, Isil; Dilmac, Bulent

2013-01-01

The purpose of this study was to investigate the exploratory and predictive relationships between secondary school students' human values and their motivational beliefs and self-regulated learning strategies and thus to test the relevant model was developed. A correlational filed study was used in this research. The sample of the research…

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.

The Model Human Processor and the Older Adult: Parameter Estimation and Validation within a Mobile Phone Task

ERIC Educational Resources Information Center

Jastrzembski, Tiffany S.; Charness, Neil

2007-01-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20;…

Development of the AFRL Aircrew Perfomance and Protection Data Bank

DTIC Science & Technology

2007-12-01

Growth model and statistical model of hypobaric chamber simulations. It offers a quick and readily accessible online DCS risk assessment tool for...are used for the DCS prediction instead of the original model. ADRAC is based on more than 20 years of hypobaric chamber studies using human...prediction based on the combined Bubble Growth model and statistical model of hypobaric chamber simulations was integrated into the Data Bank. It

Modeling Interdependent and Periodic Real-World Action Sequences

PubMed Central

Kurashima, Takeshi; Althoff, Tim; Leskovec, Jure

2018-01-01

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions. PMID:29780977

Principal Component Analysis in Construction of 3D Human Knee Joint Models Using a Statistical Shape Model Method

PubMed Central

Tsai, Tsung-Yuan; Li, Jing-Sheng; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Li, Guoan

2013-01-01

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the 3D joint surface model has been reported in literature. In this study, we constructed a SSM database using 152 human CT knee joint models, including the femur, tibia and patella and analyzed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 seconds using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus it may have a broad application in computer assisted knee surgeries that require 3D surface models of the knee. PMID:24156375

MODELING H-ARS USING HEMATOLOGICAL PARAMETERS: A COMPARISON BETWEEN THE NON-HUMAN PRIMATE AND MINIPIG.

PubMed

Bolduc, David L; Bünger, Rolf; Moroni, Maria; Blakely, William F

2016-12-01

Multiple hematological biomarkers (i.e. complete blood counts and serum chemistry parameters) were used in a multivariate linear-regression fit to create predictive algorithms for estimating the severity of hematopoietic acute radiation syndrome (H-ARS) using two different species (i.e. Göttingen Minipig and non-human primate (NHP) (Macacca mulatta)). Biomarker data were analyzed prior to irradiation and between 1-60 days (minipig) and 1-30 days (NHP) after irradiation exposures of 1.6-3.5 Gy (minipig) and 6.5 Gy (NHP) 60 Co gamma ray doses at 0.5-0.6 Gy min -1 and 0.4 Gy min -1 , respectively. Fitted radiation risk and injury categorization (RRIC) values and RRIC prediction percent accuracies were compared between the two models. Both models estimated H-ARS severity with over 80% overall predictive power and with receiver operating characteristic curve area values of 0.884 and 0.825. These results based on two animal radiation models support the concept for the use of a hematopoietic-based algorithm for predicting the risk of H-ARS in humans. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set

PubMed Central

Hancock, Penelope A.; Rehman, Yasmin; Hall, Ian M.; Edeghere, Obaghe; Danon, Leon; House, Thomas A.; Keeling, Matthew J.

2014-01-01

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. PMID:25211122

Climate suitability and human influences combined explain the range expansion of an invasive horticultural plant

Treesearch

Carolyn M. Beans; Francis F. Kilkenny; Laura F. Galloway

2012-01-01

Ecological niche models are commonly used to identify regions at risk of species invasions. Relying on climate alone may limit a model's success when additional variables contribute to invasion. While a climate-based model may predict the future spread of an invasive plant, we hypothesized that a model that combined climate with human influences would most...

Computationally modeling interpersonal trust.

PubMed

Lee, Jin Joo; Knox, W Bradley; Wormwood, Jolie B; Breazeal, Cynthia; Desteno, David

2013-01-01

We present a computational model capable of predicting-above human accuracy-the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naiveté of this domain knowledge. We then present the construction of hidden Markov models to investigate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Effect of bulk modulus on deformation of the brain under rotational accelerations

NASA Astrophysics Data System (ADS)

Ganpule, S.; Daphalapurkar, N. P.; Cetingul, M. P.; Ramesh, K. T.

2018-01-01

Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2-mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber bundles for modeling white matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformations in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Affective forecasting in an orangutan: predicting the hedonic outcome of novel juice mixes.

PubMed

Sauciuc, Gabriela-Alina; Persson, Tomas; Bååth, Rasmus; Bobrowicz, Katarzyna; Osvath, Mathias

2016-11-01

Affective forecasting is an ability that allows the prediction of the hedonic outcome of never-before experienced situations, by mentally recombining elements of prior experiences into possible scenarios, and pre-experiencing what these might feel like. It has been hypothesised that this ability is uniquely human. For example, given prior experience with the ingredients, but in the absence of direct experience with the mixture, only humans are said to be able to predict that lemonade tastes better with sugar than without it. Non-human animals, on the other hand, are claimed to be confined to predicting-exclusively and inflexibly-the outcome of previously experienced situations. Relying on gustatory stimuli, we devised a non-verbal method for assessing affective forecasting and tested comparatively one Sumatran orangutan and ten human participants. Administered as binary choices, the test required the participants to mentally construct novel juice blends from familiar ingredients and to make hedonic predictions concerning the ensuing mixes. The orangutan's performance was within the range of that shown by the humans. Both species made consistent choices that reflected independently measured taste preferences for the stimuli. Statistical models fitted to the data confirmed the predictive accuracy of such a relationship. The orangutan, just like humans, thus seems to have been able to make hedonic predictions concerning never-before experienced events.

Predicting homeowners' approval of fuel management at the wild-urban interface using the theory of reasoned action.

Treesearch

Christine A. Vogt; Greg Winter; Jeremy S. Fried

2005-01-01

Social science models are increasingly needed as a framework for explaining and predicting how members of the public respond to the natural environment and their communities. The theory of reasoned action is widely used in human dimensions research on natural resource problems and work is ongoing to increase the predictive power of models based on this theory. This...

Effect of smoking parameters on the particle size distribution and predicted airway deposition of mainstream cigarette smoke.

PubMed

Kane, David B; Asgharian, Bahman; Price, Owen T; Rostami, Ali; Oldham, Michael J

2010-02-01

It is known that puffing conditions such as puff volume, duration, and frequency vary substantially among individual smokers. This study investigates how these parameters affect the particle size distribution and concentration of fresh mainstream cigarette smoke (MCS) and how these changes affect the predicted deposition of MCS particles in a model human respiratory tract. Measurements of the particle size distribution made with an electrical low pressure impactor for a variety of puffing conditions are presented. The average flow rate of the puff is found to be the major factor effecting the measured particle size distribution of the MCS. The results of these measurements were then used as input to a deterministic dosimetry model (MPPD) to estimate the changes in the respiratory tract deposition fraction of smoke particles. The MPPD dosimetry model was modified by incorporating mechanisms involved in respiratory tract deposition of MCS: hygroscopic growth, coagulation, evaporation of semivolatiles, and mixing of the smoke with inhaled dilution air. The addition of these mechanisms to MPPD resulted in reasonable agreement between predicted airway deposition and human smoke retention measurements. The modified MPPD model predicts a modest 10% drop in the total deposition efficiency in a model human respiratory tract as the puff flow rate is increased from 1050 to 3100 ml/min, for a 2-s puff.

Predicting the Impacts of Intravehicular Displays on Driving Performance with Human Performance Modeling

NASA Technical Reports Server (NTRS)

Mitchell, Diane Kuhl; Wojciechowski, Josephine; Samms, Charneta

2012-01-01

A challenge facing the U.S. National Highway Traffic Safety Administration (NHTSA), as well as international safety experts, is the need to educate car drivers about the dangers associated with performing distraction tasks while driving. Researchers working for the U.S. Army Research Laboratory have developed a technique for predicting the increase in mental workload that results when distraction tasks are combined with driving. They implement this technique using human performance modeling. They have predicted workload associated with driving combined with cell phone use. In addition, they have predicted the workload associated with driving military vehicles combined with threat detection. Their technique can be used by safety personnel internationally to demonstrate the dangers of combining distracter tasks with driving and to mitigate the safety risks.

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Partitioning of polar and non-polar neutral organic chemicals into human and cow milk.

PubMed

Geisler, Anett; Endo, Satoshi; Goss, Kai-Uwe

2011-10-01

The aim of this work was to develop a predictive model for milk/water partition coefficients of neutral organic compounds. Batch experiments were performed for 119 diverse organic chemicals in human milk and raw and processed cow milk at 37°C. No differences (<0.3 log units) in the partition coefficients of these types of milk were observed. The polyparameter linear free energy relationship model fit the calibration data well (SD=0.22 log units). An experimental validation data set including hormones and hormone active compounds was predicted satisfactorily by the model. An alternative modelling approach based on log K(ow) revealed a poorer performance. The model presented here provides a significant improvement in predicting enrichment of potentially hazardous chemicals in milk. In combination with physiologically based pharmacokinetic modelling this improvement in the estimation of milk/water partitioning coefficients may allow a better risk assessment for a wide range of neutral organic chemicals. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bridging the gap between computation and clinical biology: validation of cable theory in humans

PubMed Central

Finlay, Malcolm C.; Xu, Lei; Taggart, Peter; Hanson, Ben; Lambiase, Pier D.

2013-01-01

Introduction: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. Methods: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S2 extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. Results: Activation dynamics following a premature S3 were significantly different from those after an S2. Patient specific models demonstrated accurate prediction of the S3 activation wave, (Pearson's R2 = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R2 = 0.99). Conclusions: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models. PMID:24027527

Real-Time Monitoring and Prediction of the Pilot Vehicle System (PVS) Closed-Loop Stability

NASA Astrophysics Data System (ADS)

Mandal, Tanmay Kumar

Understanding human control behavior is an important step for improving the safety of future aircraft. Considerable resources are invested during the design phase of an aircraft to ensure that the aircraft has desirable handling qualities. However, human pilots exhibit a wide range of control behaviors that are a function of external stimulus, aircraft dynamics, and human psychological properties (such as workload, stress factor, confidence, and sense of urgency factor). This variability is difficult to address comprehensively during the design phase and may lead to undesirable pilot-aircraft interaction, such as pilot-induced oscillations (PIO). This creates the need to keep track of human pilot performance in real-time to monitor the pilot vehicle system (PVS) stability. This work focused on studying human pilot behavior for the longitudinal axis of a remotely controlled research aircraft and using human-in-the-loop (HuIL) simulations to obtain information about the human controlled system (HCS) stability. The work in this dissertation is divided into two main parts: PIO analysis and human control model parameters estimation. To replicate different flight conditions, this study included time delay and elevator rate limiting phenomena, typical of actuator dynamics during the experiments. To study human control behavior, this study employed the McRuer model for single-input single-output manual compensatory tasks. McRuer model is a lead-lag controller with time delay which has been shown to adequately model manual compensatory tasks. This dissertation presents a novel technique to estimate McRuer model parameters in real-time and associated validation using HuIL simulations to correctly predict HCS stability. The McRuer model parameters were estimated in real-time using a Kalman filter approach. The estimated parameters were then used to analyze the stability of the closed-loop HCS and verify them against the experimental data. Therefore, the main contribution of this dissertation is the design of an unscented Kalman filter-based algorithm to estimate McRuer model parameters in real time, and a framework to validate this algorithm for single-input single-output manual compensatory tasks to predict instabilities.

Stem cell-derived models to improve mechanistic understanding and prediction of human drug-induced liver injury.

PubMed

Goldring, Christopher; Antoine, Daniel J; Bonner, Frank; Crozier, Jonathan; Denning, Chris; Fontana, Robert J; Hanley, Neil A; Hay, David C; Ingelman-Sundberg, Magnus; Juhila, Satu; Kitteringham, Neil; Silva-Lima, Beatriz; Norris, Alan; Pridgeon, Chris; Ross, James A; Young, Rowena Sison; Tagle, Danilo; Tornesi, Belen; van de Water, Bob; Weaver, Richard J; Zhang, Fang; Park, B Kevin

2017-02-01

Current preclinical drug testing does not predict some forms of adverse drug reactions in humans. Efforts at improving predictability of drug-induced tissue injury in humans include using stem cell technology to generate human cells for screening for adverse effects of drugs in humans. The advent of induced pluripotent stem cells means that it may ultimately be possible to develop personalized toxicology to determine interindividual susceptibility to adverse drug reactions. However, the complexity of idiosyncratic drug-induced liver injury means that no current single-cell model, whether of primary liver tissue origin, from liver cell lines, or derived from stem cells, adequately emulates what is believed to occur during human drug-induced liver injury. Nevertheless, a single-cell model of a human hepatocyte which emulates key features of a hepatocyte is likely to be valuable in assessing potential chemical risk; furthermore, understanding how to generate a relevant hepatocyte will also be critical to efforts to build complex multicellular models of the liver. Currently, hepatocyte-like cells differentiated from stem cells still fall short of recapitulating the full mature hepatocellular phenotype. Therefore, we convened a number of experts from the areas of preclinical and clinical hepatotoxicity and safety assessment, from industry, academia, and regulatory bodies, to specifically explore the application of stem cells in hepatotoxicity safety assessment and to make recommendations for the way forward. In this short review, we particularly discuss the importance of benchmarking stem cell-derived hepatocyte-like cells to their terminally differentiated human counterparts using defined phenotyping, to make sure the cells are relevant and comparable between labs, and outline why this process is essential before the cells are introduced into chemical safety assessment. (Hepatology 2017;65:710-721). © 2016 by the American Association for the Study of Liver Diseases.

A distributed analysis of Human impact on global sediment dynamics

NASA Astrophysics Data System (ADS)

Cohen, S.; Kettner, A.; Syvitski, J. P.

2012-12-01

Understanding riverine sediment dynamics is an important undertaking for both socially-relevant issues such as agriculture, water security and infrastructure management and for scientific analysis of landscapes, river ecology, oceanography and other disciplines. Providing good quantitative and predictive tools in therefore timely particularly in light of predicted climate and landuse changes. Ever increasing human activity during the Anthropocene have affected sediment dynamics in two major ways: (1) an increase is hillslope erosion due to agriculture, deforestation and landscape engineering and (2) trapping of sediment in dams and other man-made reservoirs. The intensity and dynamics between these man-made factors vary widely across the globe and in time and are therefore hard to predict. Using sophisticated numerical models is therefore warranted. Here we use a distributed global riverine sediment flux and water discharge model (WBMsed) to compare a pristine (without human input) and disturbed (with human input) simulations. Using these 50 year simulations we will show and discuss the complex spatial and temporal patterns of human effect on riverine sediment flux and water discharge.

Simulations in Cyber-Security: A Review of Cognitive Modeling of Network Attackers, Defenders, and Users

PubMed Central

Veksler, Vladislav D.; Buchler, Norbou; Hoffman, Blaine E.; Cassenti, Daniel N.; Sample, Char; Sugrim, Shridat

2018-01-01

Computational models of cognitive processes may be employed in cyber-security tools, experiments, and simulations to address human agency and effective decision-making in keeping computational networks secure. Cognitive modeling can addresses multi-disciplinary cyber-security challenges requiring cross-cutting approaches over the human and computational sciences such as the following: (a) adversarial reasoning and behavioral game theory to predict attacker subjective utilities and decision likelihood distributions, (b) human factors of cyber tools to address human system integration challenges, estimation of defender cognitive states, and opportunities for automation, (c) dynamic simulations involving attacker, defender, and user models to enhance studies of cyber epidemiology and cyber hygiene, and (d) training effectiveness research and training scenarios to address human cyber-security performance, maturation of cyber-security skill sets, and effective decision-making. Models may be initially constructed at the group-level based on mean tendencies of each subject's subgroup, based on known statistics such as specific skill proficiencies, demographic characteristics, and cultural factors. For more precise and accurate predictions, cognitive models may be fine-tuned to each individual attacker, defender, or user profile, and updated over time (based on recorded behavior) via techniques such as model tracing and dynamic parameter fitting. PMID:29867661

Evaluation of the Interactionist Model of Socioeconomic Status and Problem Behavior: A Developmental Cascade across Generations

PubMed Central

Martin, Monica J.; Conger, Rand D.; Schofield, Thomas J.; Dogan, Shannon J.; Widaman, Keith F.; Donnellan, M. Brent; Neppl, Tricia K.

2010-01-01

The current multigenerational study evaluates the utility of the Interactionist Model of Socioeconomic Influence on human development (IMSI) in explaining problem behaviors across generations. The IMSI proposes that the association between socioeconomic status (SES) and human development involves a dynamic interplay that includes both social causation (SES influences human development) and social selection (individual characteristics affect SES). As part of the developmental cascade proposed by the IMSI, the findings from this investigation showed that G1 adolescent problem behavior predicted later G1 SES, family stress, and parental emotional investments, as well as the next generation of children's problem behavior. These results are consistent with a social selection view. Consistent with the social causation perspective, we found a significant relation between G1 SES and family stress, and in turn, family stress predicted G2 problem behavior. Finally, G1 adult SES predicted both material and emotional investments in the G2 child. In turn, emotional investments predicted G2 problem behavior, as did material investments. Some of the predicted pathways varied by G1 parent gender. The results are consistent with the view that processes of both social selection and social causation account for the association between SES and human development. PMID:20576188

The viscoelastic standard nonlinear solid model: predicting the response of the lumbar intervertebral disk to low-frequency vibrations.

PubMed

Groth, Kevin M; Granata, Kevin P

2008-06-01

Due to the mathematical complexity of current musculoskeletal spine models, there is a need for computationally efficient models of the intervertebral disk (IVD). The aim of this study is to develop a mathematical model that will adequately describe the motion of the IVD under axial cyclic loading as well as maintain computational efficiency for use in future musculoskeletal spine models. Several studies have successfully modeled the creep characteristics of the IVD using the three-parameter viscoelastic standard linear solid (SLS) model. However, when the SLS model is subjected to cyclic loading, it underestimates the load relaxation, the cyclic modulus, and the hysteresis of the human lumbar IVD. A viscoelastic standard nonlinear solid (SNS) model was used to predict the response of the human lumbar IVD subjected to low-frequency vibration. Nonlinear behavior of the SNS model was simulated by a strain-dependent elastic modulus on the SLS model. Parameters of the SNS model were estimated from experimental load deformation and stress-relaxation curves obtained from the literature. The SNS model was able to predict the cyclic modulus of the IVD at frequencies of 0.01 Hz, 0.1 Hz, and 1 Hz. Furthermore, the SNS model was able to quantitatively predict the load relaxation at a frequency of 0.01 Hz. However, model performance was unsatisfactory when predicting load relaxation and hysteresis at higher frequencies (0.1 Hz and 1 Hz). The SLS model of the lumbar IVD may require strain-dependent elastic and viscous behavior to represent the dynamic response to compressive strain.

Defining a Cancer Dependency Map.

PubMed

Tsherniak, Aviad; Vazquez, Francisca; Montgomery, Phil G; Weir, Barbara A; Kryukov, Gregory; Cowley, Glenn S; Gill, Stanley; Harrington, William F; Pantel, Sasha; Krill-Burger, John M; Meyers, Robin M; Ali, Levi; Goodale, Amy; Lee, Yenarae; Jiang, Guozhi; Hsiao, Jessica; Gerath, William F J; Howell, Sara; Merkel, Erin; Ghandi, Mahmoud; Garraway, Levi A; Root, David E; Golub, Todd R; Boehm, Jesse S; Hahn, William C

2017-07-27

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets. Copyright © 2017 Elsevier Inc. All rights reserved.

PREDICTING POPULATION EXPOSURES TO PM10 AND PM 2.5

EPA Science Inventory

An improved model for human exposure to particulate matter (PM), specifically PM10 and PM2.5 is under development by the U.S. EPA/NERL. This model will incorporate data from new PM exposure measurement and exposure factors research. It is intended to be used to predict exposure...

Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition: Computational Modeling

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We developed a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC)...

Relative Chemical Binding Affinities for Trout and Human Estrogen Receptor Using Different Competitive Binding Assays

EPA Science Inventory

Rainbow trout-based assays for estrogenicity are currently being used for development of predictive models based upon quantitative structure activity relationships. A predictive model based on a single species raises the question of whether this information is valid for other spe...

Predicting Adaptive Response to Fadrozole Exposure: Computational Model of the Fathead Minnow Hypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (...

Effects of soil moisture on the diurnal pattern of pesticide emission: Numerical simulation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

Accurate prediction of pesticide volatilization is important for the protection of human and environmental health. Due to the complexity of the volatilization process, sophisticated predictive models are needed, especially for dry soil conditions. A mathematical model was developed to allow simulati...

Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

EPA Science Inventory

The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

PBPK modeling for PFOS and PFOA: validation with human experimental data.

PubMed

Fàbrega, Francesc; Kumar, Vikas; Schuhmacher, Marta; Domingo, José L; Nadal, Martí

2014-10-15

In recent years, because of the potential human toxicity, concern on perfluoroalkyl substances (PFASs) has increased notably with special attention to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). Unfortunately, there is currently an important knowledge gap on the burdens of these chemicals in most human tissues, as the reported studies have been mainly focused on plasma. In order to overcome these limitations, the use of physiologically-based pharmacokinetic (PBPK) models has been extended. The present study was aimed at testing an existing PBPK model for their predictability of PFOS and PFOA in a new case-study, and also to adapt it to estimate the PFAS content in human tissue compartments. Model validation was conducted by means of PFOA and PFOS concentrations in food and human drinking water from Tarragona County (Catalonia, Spain), and being the predicted results compared with those experimentally found in human tissues (blood, liver, kidney, liver and brain) of subjects from the same area of study. The use of human-derived partition coefficient (Pk) data was proven as more suitable for application to this PBPK model than rat-based Pk values. However, the uncertainty and variability of the data are still too high to get conclusive results. Consequently, further efforts should be carried out to reduce parametric uncertainty of PBPK models. More specifically, a deeper knowledge on the distribution of PFOA and PFOS within the human body should be obtained by enlarging the number of biological monitoring studies on PFASs. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An information maximization model of eye movements

NASA Technical Reports Server (NTRS)

Renninger, Laura Walker; Coughlan, James; Verghese, Preeti; Malik, Jitendra

2005-01-01

We propose a sequential information maximization model as a general strategy for programming eye movements. The model reconstructs high-resolution visual information from a sequence of fixations, taking into account the fall-off in resolution from the fovea to the periphery. From this framework we get a simple rule for predicting fixation sequences: after each fixation, fixate next at the location that minimizes uncertainty (maximizes information) about the stimulus. By comparing our model performance to human eye movement data and to predictions from a saliency and random model, we demonstrate that our model is best at predicting fixation locations. Modeling additional biological constraints will improve the prediction of fixation sequences. Our results suggest that information maximization is a useful principle for programming eye movements.

Identification of cis-suppression of human disease mutations by comparative genomics.

PubMed

Jordan, Daniel M; Frangakis, Stephan G; Golzio, Christelle; Cassa, Christopher A; Kurtzberg, Joanne; Davis, Erica E; Sunyaev, Shamil R; Katsanis, Nicholas

2015-08-13

Patterns of amino acid conservation have served as a tool for understanding protein evolution. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity.

[Research on adaptive quasi-linear viscoelastic model for nonlinear viscoelastic properties of in vivo soft tissues].

PubMed

Wang, Heng; Sang, Yuanjun

2017-10-01

The mechanical behavior modeling of human soft biological tissues is a key issue for a large number of medical applications, such as surgery simulation, surgery planning, diagnosis, etc. To develop a biomechanical model of human soft tissues under large deformation for surgery simulation, the adaptive quasi-linear viscoelastic (AQLV) model was proposed and applied in human forearm soft tissues by indentation tests. An incremental ramp-and-hold test was carried out to calibrate the model parameters. To verify the predictive ability of the AQLV model, the incremental ramp-and-hold test, a single large amplitude ramp-and-hold test and a sinusoidal cyclic test at large strain amplitude were adopted in this study. Results showed that the AQLV model could predict the test results under the three kinds of load conditions. It is concluded that the AQLV model is feasible to describe the nonlinear viscoelastic properties of in vivo soft tissues under large deformation. It is promising that this model can be selected as one of the soft tissues models in the software design for surgery simulation or diagnosis.

Predicting impacts of future human population growth and development on occupancy rates of forest-dependent birds

USGS Publications Warehouse

Brown, Michelle L.; Donovan, Therese; Schwenk, W. Scott; Theobald, David M.

2014-01-01

Forest loss and fragmentation are among the largest threats to forest-dwelling wildlife species today, and projected increases in human population growth are expected to increase these threats in the next century. We combined spatially-explicit growth models with wildlife distribution models to predict the effects of human development on 5 forest-dependent bird species in Vermont, New Hampshire, and Massachusetts, USA. We used single-species occupancy models to derive the probability of occupancy for each species across the study area in the years 2000 and 2050. Over half a million new housing units were predicted to be added to the landscape. The maximum change in housing density was nearly 30 houses per hectare; however, 30% of the towns in the study area were projected to add less than 1 housing unit per hectare. In the face of predicted human growth, the overall occupancy of each species decreased by as much as 38% (ranging from 19% to 38% declines in the worst-case scenario) in the year 2050. These declines were greater outside of protected areas than within protected lands. Ninety-seven percent of towns experienced some decline in species occupancy within their borders, highlighting the value of spatially-explicit models. The mean decrease in occupancy probability within towns ranged from 3% for hairy woodpecker to 8% for ovenbird and hermit thrush. Reductions in occupancy probability occurred on the perimeters of cities and towns where exurban development is predicted to increase in the study area. This spatial approach to wildlife planning provides data to evaluate trade-offs between development scenarios and forest-dependent wildlife species.

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.

Attribute And-Or Grammar for Joint Parsing of Human Pose, Parts and Attributes.

PubMed

Park, Seyoung; Nie, Xiaohan; Zhu, Song-Chun

2017-07-25

This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and articulation of body parts, and account for the correlations between poses and attributes. The A-AOG model is an amalgamation of three traditional grammar formulations: (i)Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii)Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii)Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style. The parse graph outputs human detection, pose estimation, and attribute prediction simultaneously, which are intuitive and interpretable. We conduct experiments on two tasks on two datasets, and experimental results demonstrate the advantage of joint modeling in comparison with computing poses and attributes independently. Furthermore, our model obtains better performance over existing methods for both pose estimation and attribute prediction tasks.

A controllable tactile device for human-like tissue realization using smart magneto-rheological fluids: fabrication and modeling

NASA Astrophysics Data System (ADS)

Cha, Seung-Woo; Kang, Seok-Rae; Hwang, Yong-Hoon; Oh, Jong-Seok; Choi, Seung-Bok

2018-06-01

This paper proposes a new tactile device to realize the force of human-like organs using the viscoelastic property by combing a smart magneto-rheological (MR) fluid with a sponge (MR sponge in short). The effectiveness of the sensor is validated through the comparison of the force obtained through measurement and the proposed prediction model. As the first step, a conventional standard linear solid model is adopted to independently investigate the force characteristics of MR fluid and sponge. Force is measured using a 3-axis robot with a force sensor to obtain certain properties of MR fluid and sponge. In addition, to show that the proposed MR sponge can realize the force of human-like tissues, experiments are performed using three specimens, i.e., porcine heart, lung, and liver. Subsequently, a quasi-static model for predicting the field-dependent force of the MR sponge is formulated using empirical values. It is demonstrated through comparison that the proposed force model can accurately predict the force of the specimens without significant error. In addition, a psychophysical test is carried out by ordinary subjects to validate the effectiveness of the proposed tactile device. Results show that the MR sponge tactile device can easily produce various levels of the force of human-like tissues, such as the liver and lung of the porcine, by controlling input current.

A framework for human-hydrologic system model development integrating hydrology and water management: application to the Cutzamala water system in Mexico

NASA Astrophysics Data System (ADS)

Wi, S.; Freeman, S.; Brown, C.

2017-12-01

This study presents a general approach to developing computational models of human-hydrologic systems where human modification of hydrologic surface processes are significant or dominant. A river basin system is represented by a network of human-hydrologic response units (HHRUs) identified based on locations where river regulations happen (e.g., reservoir operation and diversions). Natural and human processes in HHRUs are simulated in a holistic framework that integrates component models representing rainfall-runoff, river routing, reservoir operation, flow diversion and water use processes. We illustrate the approach in a case study of the Cutzamala water system (CWS) in Mexico, a complex inter-basin water transfer system supplying the Mexico City Metropolitan Area (MCMA). The human-hydrologic system model for CWS (CUTZSIM) is evaluated in terms of streamflow and reservoir storages measured across the CWS and to water supplied for MCMA. The CUTZSIM improves the representation of hydrology and river-operation interaction and, in so doing, advances evaluation of system-wide water management consequences under altered climatic and demand regimes. The integrated modeling framework enables evaluation and simulation of model errors throughout the river basin, including errors in representation of the human component processes. Heretofore, model error evaluation, predictive error intervals and the resultant improved understanding have been limited to hydrologic processes. The general framework represents an initial step towards fuller understanding and prediction of the many and varied processes that determine the hydrologic fluxes and state variables in real river basins.

Training of cosmonauts and astronauts

NASA Technical Reports Server (NTRS)

Gurovskiy, N. N.; Link, M. M.

1975-01-01

The biomedical and preflight training of spacecraft crews is discussed based on a survey of scientific and technical literature in the U.S. and U.S.S.R. Experience gained from high velocity and high altitude aircraft flights, predictions of human reactions and theoretical models of human adaptation to the new environment of space, and actual spaceflight experience provided scientists and specialists with data from which the state of human health in space could be predicted and life support measures developed.

A model of the human observer and decision maker

NASA Technical Reports Server (NTRS)

Wewerinke, P. H.

1981-01-01

The decision process is described in terms of classical sequential decision theory by considering the hypothesis that an abnormal condition has occurred by means of a generalized likelihood ratio test. For this, a sufficient statistic is provided by the innovation sequence which is the result of the perception an information processing submodel of the human observer. On the basis of only two model parameters, the model predicts the decision speed/accuracy trade-off and various attentional characteristics. A preliminary test of the model for single variable failure detection tasks resulted in a very good fit of the experimental data. In a formal validation program, a variety of multivariable failure detection tasks was investigated and the predictive capability of the model was demonstrated.

Human and climate impact on global riverine water and sediment fluxes - a distributed analysis

NASA Astrophysics Data System (ADS)

Cohen, S.; Kettner, A.; Syvitski, J. P.

2013-05-01

Understanding riverine water and sediment dynamics is an important undertaking for both socially-relevant issues such as agriculture, water security and infrastructure management and for scientific analysis of climate, landscapes, river ecology, oceanography and other disciplines. Providing good quantitative and predictive tools in therefore timely particularly in light of predicted climate and landuse changes. The intensity and dynamics between man-made and climatic factors vary widely across the globe and are therefore hard to predict. Using sophisticated numerical models is therefore warranted. Here we use a distributed global riverine sediment and water discharge model (WBMsed) to simulate human and climate effect on our planet's large rivers.

Prediction of percutaneous absorption in human using three-dimensional human cultured epidermis LabCyte EPI-MODEL.

PubMed

Hikima, Tomohiro; Kaneda, Noriaki; Matsuo, Kyouhei; Tojo, Kakuji

2012-01-01

The objective of this study is to establish a relationship of the skin penetration parameters between the three-dimensional cultured human epidermis LabCyte EPI-MODEL (LabCyte) and hairless mouse (HLM) skin penetration in vitro and to predict the skin penetration and plasma concentration profile in human. The skin penetration experiments through LabCyte and HLM skin were investigated using 19 drugs that have a different molecular weight and lipophilicity. The penetration flux for LabCyte reached 30 times larger at maximum than that for HLM skin. The human data can be estimated from the in silico approach with the diffusion coefficient (D), the partition coefficient (K) and the skin surface concentration (C) of drugs by assuming the bi-layer skin model for both LabCyte and HLM skin. The human skin penetration of β-estradiol, prednisolone, testosterone and ethynylestradiol was well agreed between the simulated profiles and in vitro experimental data. Plasma concentration profiles of β-estradiol in human were also simulated and well agreed with the clinical data. The present alternative method may decrease human or animal skin experiment for in vitro skin penetration.

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017.

PubMed

Olugasa, Babasola O; Odigie, Eugene A; Lawani, Mike; Ojo, Johnson F

2015-01-01

The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country. A retrospective 5 years data of LF distribution countrywide among humans were used to train a time-trend model of the disease in Liberia. A time-trend quadratic model was selected due to its goodness-of-fit (R2 = 0.89, and P < 0.05) and best performance compared to linear and exponential models. Parameter predictors were run on least square method to predict LF cases for a prospective 5 years period, covering 2013-2017. The two-stage predictive model of LF case-pattern between 2013 and 2017 was characterized by a prospective decline within the South-coast County of Grand Bassa over the forecast period and an upward case-trend within the Northern County of Nimba. Case specific exponential increase was predicted for the first 2 years (2013-2014) with a geometric increase over the next 3 years (2015-2017) in Nimba County. This paper describes a translational application of the space-time distribution pattern of LF epidemics, 2008-2012 reported in Liberia, on which a predictive model was developed. We proposed a computationally feasible two-stage space-time permutation approach to estimate the time-trend parameters and conduct predictive inference on LF in Liberia.

OVERVIEW OF EPA'S HUMAN EXPOSURE AND SOURCE-TO-DOSE MODELING PROGRAM: HEADSUP

EPA Science Inventory

EPA's human exposure and source-to-dose modeling program is designed to provide a scientifically sound approach to understanding how people are actually exposed to pollutants and the magnitude of predicted exposures and dose. The objective of this research project is to develo...

Rhesus macaque and mouse models for down-selecting circumsporozoite protein based malaria vaccines differ significantly in immunogenicity and functional outcomes.

PubMed

Phares, Timothy W; May, Anthony D; Genito, Christopher J; Hoyt, Nathan A; Khan, Farhat A; Porter, Michael D; DeBot, Margot; Waters, Norman C; Saudan, Philippe; Dutta, Sheetij

2017-03-13

Non-human primates, such as the rhesus macaques, are the preferred model for down-selecting human malaria vaccine formulations, but the rhesus model is expensive and does not allow for direct efficacy testing of human malaria vaccines. Transgenic rodent parasites expressing genes of human Plasmodium are now routinely used for efficacy studies of human malaria vaccines. Mice have however rarely predicted success in human malaria trials and there is scepticism whether mouse studies alone are sufficient to move a vaccine candidate into the clinic. A comparison of immunogenicity, fine-specificity and functional activity of two Alum-adjuvanted Plasmodium falciparum circumsporozoite protein (CSP)-based vaccines was conducted in mouse and rhesus models. One vaccine was a soluble recombinant protein (CSP) and the other was the same CSP covalently conjugated to the Qβ phage particle (Qβ-CSP). Mice showed different kinetics of antibody responses and different sensitivity to the NANP-repeat and N-terminal epitopes as compared to rhesus. While mice failed to discern differences between the protective efficacy of CSP versus Qβ-CSP vaccine following direct challenge with transgenic Plasmodium berghei parasites, rhesus serum from the Qβ-CSP-vaccinated animals induced higher in vivo sporozoite neutralization activity. Despite some immunologic parallels between models, these data demonstrate that differences between the immune responses induced in the two models risk conflicting decisions regarding potential vaccine utility in humans. In combination with historical observations, the data presented here suggest that although murine models may be useful for some purposes, non-human primate models may be more likely to predict the human response to investigational vaccines.

Towards the dynamic prediction of wildfire danger. Modeling temporal scenarios of fire-occurrence in Northeast Spain

NASA Astrophysics Data System (ADS)

Martín, Yago; Rodrigues, Marcos

2017-04-01

Up to date models of human-caused ignition probability have commonly been developed from a static or structural point of view, regardless of the time cycles that drive human behavior or environmental conditions. However, human drivers mostly have a temporal dimension, and fuel conditions are subjected to temporal changes as well, which is why a historical/temporal perspective is often required. Previous studies in the region suggest that human driving factors of wildfires have undergone significant shifts in inter-annual occurrence probability models, thus varying over time. On the other hand, an increasing role of environmental conditions has also been reported. This research comprehensively analyzes the intra-annual dimension of fire occurrence and fire-triggering factors using NW Spain as a test area, moving one-step forward towards achieving more accurate predictions, to ultimately develop dynamic predictive models. To this end, several intra-annual presence-only models have been calibrated, exploring seasonal variations of environmental conditions and short-term cycles of human activity (working- vs non-working days). Models were developed from accurately geolocated fire data in the 2008-2012 period, and GIS and remote sensing (MOD1A2 and MOD16) information . Specifically, 8 occurrence data subsets (scenarios) were constructed by splitting fire records into 4 seasons (winter, spring, summer and autumn) then separating each season into 2 new categories (working and non-working days). This allows analyzing the temporal variation of socioeconomic (urban- and agricultural-interfaces, transport and road networks, and human settlements) and environmental (fuel conditions) factors associated with occurrence. Models were calibrated applying the Maximum Entropy algorithm (MaxEnt). The MaxEnt algorithm was selected as it is the most widespread approach to deal with presence-only data, as may be the case of fire occurrence. The dependent variable for each scenario was created on a conceptual framework which assumed that there were no true cases of fire absence. Model accuracy was assessed using a cross-validation k-fold procedure, whereas variable importance was addressed using a jacknife approach combined with AUC estimation. Results reported model performances around 0.8 AUC in all temporal scenarios. In addition, large variability was observed in the contribution of explanatory factors, with accessibility variables and fuel conditions as key factors along models. Overall, we believe our approach is reliable enough to derive dynamic predictions of human-caused fire occurrence probability. To our knowledge, this is the first attempt to combine presence-only models based on XY located fire data, with remote sensing information and intra-annual scenarios also including cycles of human activity.

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans.

PubMed

Stevens, Jeffrey R; Marewski, Julian N; Schooler, Lael J; Gilby, Ian C

2016-08-01

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees ( Pan troglodytes ) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition.

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans

PubMed Central

Marewski, Julian N.; Schooler, Lael J.; Gilby, Ian C.

2016-01-01

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition. PMID:27853606

An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan

NASA Astrophysics Data System (ADS)

Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George

2013-04-01

Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.

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.

Gap model development, validation, and application to succession of secondary subtropical dry forests of Puerto Rico

Treesearch

Jennifer A. Holm; H.H. Shugart; Skip J. Van Bloem; G.R. Larocque

2012-01-01

Because of human pressures, the need to understand and predict the long-term dynamics and development of subtropical dry forests is urgent. Through modifications to the ZELIG simulation model, including the development of species- and site-specific parameters and internal modifications, the capability to model and predict forest change within the 4500-ha Guanica State...

Predictors of immune function in space flight

NASA Astrophysics Data System (ADS)

Shearer, William T.; Zhang, Shaojie; Reuben, James M.; Lee, Bang-Ning; Butel, Janet S.

2007-02-01

Of all of the environmental conditions of space flight that might have an adverse effect upon human immunity and the incidence of infection, space radiation stands out as the single-most important threat. As important as this would be on humans engaged in long and deep space flight, it obviously is not possible to plan Earth-bound radiation and infection studies in humans. Therefore, we propose to develop a murine model that could predict the adverse effects of space flight radiation and reactivation of latent virus infection for humans. Recent observations on the effects of gamma and latent virus infection demonstrate latent virus reactivation and loss of T cell mediated immune responses in a murine model. We conclude that using this small animal method of quantitating the amounts of radiation and latent virus infection and resulting alterations in immune responses, it may be possible to predict the degree of immunosuppression in interplanetary space travel for humans. Moreover, this model could be extended to include other space flight conditions, such as microgravity, sleep deprivation, and isolation, to obtain a more complete assessment of space flight risks for humans.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data.

PubMed

Kim, Jungmin; Park, Juyong; Lee, Wonjae

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.

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

[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.

Strontium-90 Biokinetics from Simulated Wound Intakes in Non-human Primates Compared with Combined Model Predictions from National Council on Radiation Protection and Measurements Report 156 and International Commission on Radiological Protection Publication 67.

PubMed

Allen, Mark B; Brey, Richard R; Gesell, Thomas; Derryberry, Dewayne; Poudel, Deepesh

2016-01-01

This study had a goal to evaluate the predictive capabilities of the National Council on Radiation Protection and Measurements (NCRP) wound model coupled to the International Commission on Radiological Protection (ICRP) systemic model for 90Sr-contaminated wounds using non-human primate data. Studies were conducted on 13 macaque (Macaca mulatta) monkeys, each receiving one-time intramuscular injections of 90Sr solution. Urine and feces samples were collected up to 28 d post-injection and analyzed for 90Sr activity. Integrated Modules for Bioassay Analysis (IMBA) software was configured with default NCRP and ICRP model transfer coefficients to calculate predicted 90Sr intake via the wound based on the radioactivity measured in bioassay samples. The default parameters of the combined models produced adequate fits of the bioassay data, but maximum likelihood predictions of intake were overestimated by a factor of 1.0 to 2.9 when bioassay data were used as predictors. Skeletal retention was also over-predicted, suggesting an underestimation of the excretion fraction. Bayesian statistics and Monte Carlo sampling were applied using IMBA to vary the default parameters, producing updated transfer coefficients for individual monkeys that improved model fit and predicted intake and skeletal retention. The geometric means of the optimized transfer rates for the 11 cases were computed, and these optimized sample population parameters were tested on two independent monkey cases and on the 11 monkeys from which the optimized parameters were derived. The optimized model parameters did not improve the model fit in most cases, and the predicted skeletal activity produced improvements in three of the 11 cases. The optimized parameters improved the predicted intake in all cases but still over-predicted the intake by an average of 50%. The results suggest that the modified transfer rates were not always an improvement over the default NCRP and ICRP model values.

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…

Predicting and Managing Lighting and Visibility for Human Operations in Space

NASA Technical Reports Server (NTRS)

Maida, James C.; Peacock, Brian

2003-01-01

Lighting is critical to human visual performance. On earth this problem is well understood and solutions are well defined and executed. Because the sun rises and sets on average every 45 minutes during Earth orbit, humans working in space must cope with extremely dynamic lighting conditions varying from very low light conditions to severe glare and contrast conditions. For critical operations, it is essential that lighting conditions be predictable and manageable. Mission planners need to detelmine whether low-light video cameras are required or whether additional luminaires, or lamps, need to be flown . Crew and flight directors need to have up to date daylight orbit time lines showing the best and worst viewing conditions for sunlight and shadowing. Where applicable and possible, lighting conditions need to be part of crew training. In addition, it is desirable to optimize the quantity and quality of light because of the potential impacts on crew safety, delivery costs, electrical power and equipment maintainability for both exterior and interior conditions. Addressing these issues, an illumination modeling system has been developed in the Space Human Factors Laboratory at ASA Johnson Space Center. The system is the integration of a physically based ray-tracing package ("Radiance"), developed at the Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system developed by NASA and an extensive database of humans and their work environments. Measured and published data has been collected for exterior and interior surface reflectivity; luminaire beam spread distribution, color and intensity and video camera light sensitivity and has been associated with their corresponding geometric models. Selecting an eye-point and one or more light sources, including sun and earthshine, a snapshot of the light energy reaching the surfaces or reaching the eye point is computed. This energy map is then used to extract the required information needed for useful predictions. Using a validated, comprehensive illumination model integrated with empirically derived data, predictions of lighting and viewing conditions have been successfully used for Shuttle and Space Station planning and assembly operations. It has successfully balanced the needs for adequate human performance with the utili zation of resources. Keywords: Modeling, ray tracing, luminaires, refl ectivity, luminance, illuminance.

Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning

PubMed Central

Griffiths, Oren; Holmes, Nathan; Westbrook, R. Fred

2017-01-01

Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training. PMID:28232809

Validation of the thermophysiological model by Fiala for prediction of local skin temperatures

NASA Astrophysics Data System (ADS)

Martínez, Natividad; Psikuta, Agnes; Kuklane, Kalev; Quesada, José Ignacio Priego; de Anda, Rosa María Cibrián Ortiz; Soriano, Pedro Pérez; Palmer, Rosario Salvador; Corberán, José Miguel; Rossi, René Michel; Annaheim, Simon

2016-12-01

The most complete and realistic physiological data are derived from direct measurements during human experiments; however, they present some limitations such as ethical concerns, time and cost burden. Thermophysiological models are able to predict human thermal response in a wide range of environmental conditions, but their use is limited due to lack of validation. The aim of this work was to validate the thermophysiological model by Fiala for prediction of local skin temperatures against a dedicated database containing 43 different human experiments representing a wide range of conditions. The validation was conducted based on root-mean-square deviation (rmsd) and bias. The thermophysiological model by Fiala showed a good precision when predicting core and mean skin temperature (rmsd 0.26 and 0.92 °C, respectively) and also local skin temperatures for most body sites (average rmsd for local skin temperatures 1.32 °C). However, an increased deviation of the predictions was observed for the forehead skin temperature (rmsd of 1.63 °C) and for the thigh during exercising exposures (rmsd of 1.41 °C). Possible reasons for the observed deviations are lack of information on measurement circumstances (hair, head coverage interference) or an overestimation of the sweat evaporative cooling capacity for the head and thigh, respectively. This work has highlighted the importance of collecting details about the clothing worn and how and where the sensors were attached to the skin for achieving more precise results in the simulations.

Human Hemato-Lymphoid System Mice: Current Use and Future Potential for Medicine

PubMed Central

Rongvaux, Anthony; Takizawa, Hitoshi; Strowig, Till; Willinger, Tim; Eynon, Elizabeth E.

2014-01-01

To directly study complex human hemato-lymphoid system physiology and respective system-associated diseases in vivo, human-to-mouse xenotransplantation models for human blood and blood-forming cells and organs have been developed over the past three decades. We here review the fundamental requirements and the remarkable progress made over the past few years in improving these systems, the current major achievements reached by use of these models, and the future challenges to more closely model and study human health and disease and to achieve predictive preclinical testing of both prevention measures and potential new therapies. PMID:23330956

Reconstituted human corneal epithelium: a new alternative to the Draize eye test for the assessment of the eye irritation potential of chemicals and cosmetic products.

PubMed

Doucet, O; Lanvin, M; Thillou, C; Linossier, C; Pupat, C; Merlin, B; Zastrow, L

2006-06-01

The aim of this study was to evaluate the interest of a new three-dimensional epithelial model cultivated from human corneal cells to replace animal testing in the assessment of eye tolerance. To this end, 65 formulated cosmetic products and 36 chemicals were tested by means of this in vitro model using a simplified toxicokinetic approach. The chemicals were selected from the ECETOC data bank and the EC/HO International validation study list. Very satisfactory results were obtained in terms of concordance with the Draize test data for the formulated cosmetic products. Moreover, the response of the corneal model appeared predictive of human ocular response clinically observed by ophthalmologists. The in vitro scores for the chemicals tested strongly correlated with their respective scores in vivo. For all the compounds tested, the response of the corneal model to irritants was similar regardless of their chemical structure, suggesting a good robustness of the prediction model proposed. We concluded that this new three-dimensional epithelial model, developed from human corneal cells, could be promising for the prediction of eye irritation induced by chemicals and complex formulated products, and that these two types of materials should be tested using a similar protocol. A simple shortening of the exposure period was required for the chemicals assumed to be more aggressively irritant to the epithelial tissues than the cosmetic formulae.

*Combining regional - and local-scale air quality models with exposure models for use in environmental health studies - Title changed from Linking air quality and exposure models for use in environmental health studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, w...

Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure.

PubMed

Dyble, Julianne; Bienfang, Paul; Dusek, Eva; Hitchcock, Gary; Holland, Fred; Laws, Ed; Lerczak, James; McGillicuddy, Dennis J; Minnett, Peter; Moore, Stephanie K; O'Kelly, Charles; Solo-Gabriele, Helena; Wang, John D

2008-11-07

Coupled physical-biological models are capable of linking the complex interactions between environmental factors and physical hydrodynamics to simulate the growth, toxicity and transport of infectious pathogens and harmful algal blooms (HABs). Such simulations can be used to assess and predict the impact of pathogens and HABs on human health. Given the widespread and increasing reliance of coastal communities on aquatic systems for drinking water, seafood and recreation, such predictions are critical for making informed resource management decisions. Here we identify three challenges to making this connection between pathogens/HABs and human health: predicting concentrations and toxicity; identifying the spatial and temporal scales of population and ecosystem interactions; and applying the understanding of population dynamics of pathogens/HABs to management strategies. We elaborate on the need to meet each of these challenges, describe how modeling approaches can be used and discuss strategies for moving forward in addressing these challenges.

Personality and support for universal human rights: a review and test of a structural model.

PubMed

McFarland, Sam

2010-12-01

All individual differences that predict support for international human rights are first reviewed: support for human rights is linked most positively to "globalism" (other international and environmental concerns), "identification with all humanity," principled moral reasoning, benevolence, and dispositional empathy. It is related most negatively to ethnocentrism and its root dispositions, the social dominance orientation, and authoritarianism. Other correlates are also noted. Secondly, a structural model of the effects of authoritarianism, social dominance, ethnocentrism and identification with all humanity upon commitment to human rights is presented and tested. Across 2 studies (Study 1, N=218 nonstudent adults; Study 2, N=102 university students), ethnocentrism and identification with all humanity directly predicted human rights commitment. The effects of authoritarianism upon this commitment were fully mediated through enhanced ethnocentrism and reduced identification with all humanity. The effects of social dominance were similar, but its direct effect upon human rights commitment remained significant and was not, in the second study, mediated by reduced dispositional empathy. © 2010 The Author. Journal of Personality © 2010, Wiley Periodicals, Inc.

Predictive models of safety based on audit findings: Part 2: Measurement of model validity.

PubMed

Hsiao, Yu-Lin; Drury, Colin; Wu, Changxu; Paquet, Victor

2013-07-01

Part 1 of this study sequence developed a human factors/ergonomics (HF/E) based classification system (termed HFACS-MA) for safety audit findings and proved its measurement reliability. In Part 2, we used the human error categories of HFACS-MA as predictors of future safety performance. Audit records and monthly safety incident reports from two airlines submitted to their regulatory authority were available for analysis, covering over 6.5 years. Two participants derived consensus results of HF/E errors from the audit reports using HFACS-MA. We adopted Neural Network and Poisson regression methods to establish nonlinear and linear prediction models respectively. These models were tested for the validity of prediction of the safety data, and only Neural Network method resulted in substantially significant predictive ability for each airline. Alternative predictions from counting of audit findings and from time sequence of safety data produced some significant results, but of much smaller magnitude than HFACS-MA. The use of HF/E analysis of audit findings provided proactive predictors of future safety performance in the aviation maintenance field. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Simulating Physiological Response with a Passive Sensor Manikin and an Adaptive Thermal Manikin to Predict Thermal Sensation and Comfort

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

Rugh, John P; Chaney, Larry; Hepokoski, Mark

2015-04-14

Reliable assessment of occupant thermal comfort can be difficult to obtain within automotive environments, especially under transient and asymmetric heating and cooling scenarios. Evaluation of HVAC system performance in terms of comfort commonly requires human subject testing, which may involve multiple repetitions, as well as multiple test subjects. Instrumentation (typically comprised of an array of temperature sensors) is usually only sparsely applied across the human body, significantly reducing the spatial resolution of available test data. Further, since comfort is highly subjective in nature, a single test protocol can yield a wide variation in results which can only be overcome bymore » increasing the number of test replications and subjects. In light of these difficulties, various types of manikins are finding use in automotive testing scenarios. These manikins can act as human surrogates from which local skin and core temperatures can be obtained, which are necessary for accurately predicting local and whole body thermal sensation and comfort using a physiology-based comfort model (e.g., the Berkeley Comfort Model). This paper evaluates two different types of manikins, i) an adaptive sweating thermal manikin, which is coupled with a human thermoregulation model, running in real-time, to obtain realistic skin temperatures; and, ii) a passive sensor manikin, which is used to measure boundary conditions as they would act on a human, from which skin and core temperatures can be predicted using a thermophysiological model. The simulated physiological responses and comfort obtained from both of these manikin-model coupling schemes are compared to those of a human subject within a vehicle cabin compartment transient heat-up scenario.« less

Robotic lower limb prosthesis design through simultaneous computer optimizations of human and prosthesis costs

NASA Astrophysics Data System (ADS)

Handford, Matthew L.; Srinivasan, Manoj

2016-02-01

Robotic lower limb prostheses can improve the quality of life for amputees. Development of such devices, currently dominated by long prototyping periods, could be sped up by predictive simulations. In contrast to some amputee simulations which track experimentally determined non-amputee walking kinematics, here, we explicitly model the human-prosthesis interaction to produce a prediction of the user’s walking kinematics. We obtain simulations of an amputee using an ankle-foot prosthesis by simultaneously optimizing human movements and prosthesis actuation, minimizing a weighted sum of human metabolic and prosthesis costs. The resulting Pareto optimal solutions predict that increasing prosthesis energy cost, decreasing prosthesis mass, and allowing asymmetric gaits all decrease human metabolic rate for a given speed and alter human kinematics. The metabolic rates increase monotonically with speed. Remarkably, by performing an analogous optimization for a non-amputee human, we predict that an amputee walking with an appropriately optimized robotic prosthesis can have a lower metabolic cost - even lower than assuming that the non-amputee’s ankle torques are cost-free.

Reasoning, learning, and creativity: frontal lobe function and human decision-making.

PubMed

Collins, Anne; Koechlin, Etienne

2012-01-01

The frontal lobes subserve decision-making and executive control--that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior.

Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making

PubMed Central

Collins, Anne; Koechlin, Etienne

2012-01-01

The frontal lobes subserve decision-making and executive control—that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior. PMID:22479152

Modeling of exposure to carbon monoxide in fires

NASA Technical Reports Server (NTRS)

Cagliostro, D. E.

1980-01-01

A mathematical model is developed to predict carboxyhemoglobin concentrations in regions of the body for short exposures to carbon monoxide levels expected during escape from aircraft fires. The model includes the respiratory and circulatory dynamics of absorption and distribution of carbon monoxide and carboxyhemoglobin. Predictions of carboxyhemoglobin concentrations are compared to experimental values obtained for human exposures to constant high carbon monoxide levels. Predictions are within 20% of experimental values. For short exposure times, transient concentration effects are predicted. The effect of stress is studied and found to increase carboxyhemoglobin levels substantially compared to a rest state.

Validation of a mapping and prediction model for human fasciolosis transmission in Andean very high altitude endemic areas using remote sensing data.

PubMed

Fuentes, M V; Malone, J B; Mas-Coma, S

2001-04-27

The present paper aims to validate the usefulness of the Normalized Difference Vegetation Index (NDVI) obtained by satellite remote sensing for the development of local maps of risk and for prediction of human fasciolosis in the Northern Bolivian Altiplano. The endemic area, which is located at very high altitudes (3800-4100 m) between Lake Titicaca and the valley of the city of La Paz, presents the highest prevalences and intensities of fasciolosis known in humans. NDVI images of 1.1 km resolution from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) series of environmental satellites appear to provide adequate information for a study area such as that of the Northern Bolivian Altiplano. The predictive value of the remotely sensed map based on NDVI data appears to be better than that from forecast indices based only on climatic data. A close correspondence was observed between real ranges of human fasciolosis prevalence at 13 localities of known prevalence rates and the predicted ranges of fasciolosis prevalence using NDVI maps. However, results based on NDVI map data predicted zones as risk areas where, in fact, field studies have demonstrated the absence of lymnaeid populations during snail surveys, corroborated by the absence of the parasite in humans and livestock. NDVI data maps represent a useful data component in long-term efforts to develop a comprehensive geographical information system control program model that accurately fits real epidemiological and transmission situations of human fasciolosis in high altitude endemic areas in Andean countries.

DEPOSITION PATTERNS OF RAGWEED POLLEN IN THE HUMAN RESPIRATORY TRACT

EPA Science Inventory

Inhaled particle deposition sites must be identified to effectively treat human airway diseases. e have determined distribution patterns of a selected aeroallergen, ragweed pollen, among human extrathoracic (ET: .e., oro-nasopharyngeal) regions and the lung. A predictive model va...

DEVELOPMENT OF A MICROSCALE EMISSION FACTOR MODEL FOR PARTICULATE MATTER (MICROFACPM) FOR PREDICTING REAL TIME MOTOR VEHICLE EMISSIONS

EPA Science Inventory

Health risk evaluation needs precise measurement and modeling of human exposures in microenvironments to support review of current air quality standards. The particulate matter emissions from motor vehicles are a major component of human exposures in urban microenvironments. Cu...

Encoding and Decoding Models in Cognitive Electrophysiology

PubMed Central

Holdgraf, Christopher R.; Rieger, Jochem W.; Micheli, Cristiano; Martin, Stephanie; Knight, Robert T.; Theunissen, Frederic E.

2017-01-01

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses. PMID:29018336

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.

Computational Fluid Dynamics Modeling of Bacillus anthracis ...

EPA Pesticide Factsheets

Journal Article Three-dimensional computational fluid dynamics and Lagrangian particle deposition models were developed to compare the deposition of aerosolized Bacillus anthracis spores in the respiratory airways of a human with that of the rabbit, a species commonly used in the study of anthrax disease. The respiratory airway geometries for each species were derived from computed tomography (CT) or µCT images. Both models encompassed airways that extended from the external nose to the lung with a total of 272 outlets in the human model and 2878 outlets in the rabbit model. All simulations of spore deposition were conducted under transient, inhalation-exhalation breathing conditions using average species-specific minute volumes. Four different exposure scenarios were modeled in the rabbit based upon experimental inhalation studies. For comparison, human simulations were conducted at the highest exposure concentration used during the rabbit experimental exposures. Results demonstrated that regional spore deposition patterns were sensitive to airway geometry and ventilation profiles. Despite the complex airway geometries in the rabbit nose, higher spore deposition efficiency was predicted in the upper conducting airways of the human at the same air concentration of anthrax spores. This greater deposition of spores in the upper airways in the human resulted in lower penetration and deposition in the tracheobronchial airways and the deep lung than that predict

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Acoustical transmission-line model of the middle-ear cavities and mastoid air cells.

PubMed

Keefe, Douglas H

2015-04-01

An acoustical transmission line model of the middle-ear cavities and mastoid air cell system (MACS) was constructed for the adult human middle ear with normal function. The air-filled cavities comprised the tympanic cavity, aditus, antrum, and MACS. A binary symmetrical airway branching model of the MACS was constructed using an optimization procedure to match the average total volume and surface area of human temporal bones. The acoustical input impedance of the MACS was calculated using a recursive procedure, and used to predict the input impedance of the middle-ear cavities at the location of the tympanic membrane. The model also calculated the ratio of the acoustical pressure in the antrum to the pressure in the middle-ear cavities at the location of the tympanic membrane. The predicted responses were sensitive to the magnitude of the viscothermal losses within the MACS. These predicted input impedance and pressure ratio functions explained the presence of multiple resonances reported in published data, which were not explained by existing MACS models.

Acute radiation risk models

NASA Astrophysics Data System (ADS)

Smirnova, Olga

Biologically motivated mathematical models, which describe the dynamics of the major hematopoietic lineages (the thrombocytopoietic, lymphocytopoietic, granulocytopoietic, and erythropoietic systems) in acutely/chronically irradiated humans are developed. These models are implemented as systems of nonlinear differential equations, which variables and constant parameters have clear biological meaning. It is shown that the developed models are capable of reproducing clinical data on the dynamics of these systems in humans exposed to acute radiation in the result of incidents and accidents, as well as in humans exposed to low-level chronic radiation. Moreover, the averaged value of the "lethal" dose rates of chronic irradiation evaluated within models of these four major hematopoietic lineages coincides with the real minimal dose rate of lethal chronic irradiation. The demonstrated ability of the models of the human thrombocytopoietic, lymphocytopoietic, granulocytopoietic, and erythropoietic systems to predict the dynamical response of these systems to acute/chronic irradiation in wide ranges of doses and dose rates implies that these mathematical models form an universal tool for the investigation and prediction of the dynamics of the major human hematopoietic lineages for a vast pattern of irradiation scenarios. In particular, these models could be applied for the radiation risk assessment for health of astronauts exposed to space radiation during long-term space missions, such as voyages to Mars or Lunar colonies, as well as for health of people exposed to acute/chronic irradiation due to environmental radiological events.

Optical diagnosis of malaria infection in human plasma using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bilal, Muhammad; Saleem, Muhammad; Amanat, Samina Tufail; Shakoor, Huma Abdul; Rashid, Rashad; Mahmood, Arshad; Ahmed, Mushtaq

2015-01-01

We present the prediction of malaria infection in human plasma using Raman spectroscopy. Raman spectra of malaria-infected samples are compared with those of healthy and dengue virus infected ones for disease recognition. Raman spectra were acquired using a laser at 532 nm as an excitation source and 10 distinct spectral signatures that statistically differentiated malaria from healthy and dengue-infected cases were found. A multivariate regression model has been developed that utilized Raman spectra of 20 malaria-infected, 10 non-malarial with fever, 10 healthy, and 6 dengue-infected samples to optically predict the malaria infection. The model yields the correlation coefficient r2 value of 0.981 between the predicted values and clinically known results of trainee samples, and the root mean square error in cross validation was found to be 0.09; both these parameters validated the model. The model was further blindly tested for 30 unknown suspected samples and found to be 86% accurate compared with the clinical results, with the inaccuracy due to three samples which were predicted in the gray region. Standard deviation and root mean square error in prediction for unknown samples were found to be 0.150 and 0.149, which are accepted for the clinical validation of the model.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

PubMed Central

Niu, Yulong; Liu, Chengcheng; Moghimyfiroozabad, Shayan; Yang, Yi

2017-01-01

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/. PMID:28875072

Proposed Framework for Determining Added Mass of Orion Drogue Parachutes

NASA Technical Reports Server (NTRS)

Fraire, Usbaldo, Jr.; Dearman, James; Morris, Aaron

2011-01-01

The Crew Exploration Vehicle (CEV) Parachute Assembly System (CPAS) project is executing a program to qualify a parachute system for a next generation human spacecraft. Part of the qualification process involves predicting parachute riser tension during system descent with flight simulations. Human rating the CPAS hardware requires a high degree of confidence in the simulation models used to predict parachute loads. However, uncertainty exists in the heritage added mass models used for loads predictions due to a lack of supporting documentation and data. Even though CPAS anchors flight simulation loads predictions to flight tests, extrapolation of these models outside the test regime carries the risk of producing non-bounding loads. A set of equations based on empirically derived functions of skirt radius is recommended as the simplest and most viable method to test and derive an enhanced added mass model for an inflating parachute. This will increase confidence in the capability to predict parachute loads. The selected equations are based on those published in A Simplified Dynamic Model of Parachute Inflation by Dean Wolf. An Ames 80x120 wind tunnel test campaign is recommended to acquire the reefing line tension and canopy photogrammetric data needed to quantify the terms in the Wolf equations and reduce uncertainties in parachute loads predictions. Once the campaign is completed, the Wolf equations can be used to predict loads in a typical CPAS Drogue Flight test. Comprehensive descriptions of added mass test techniques from the Apollo Era to the current CPAS project are included for reference.

Modeling human target acquisition in ground-to-air weapon systems

NASA Technical Reports Server (NTRS)

Phatak, A. V.; Mohr, R. L.; Vikmanis, M.; Wei, K. C.

1982-01-01

The problems associated with formulating and validating mathematical models for describing and predicting human target acquisition response are considered. In particular, the extension of the human observer model to include the acquisition phase as well as the tracking segment is presented. Relationship of the Observer model structure to the more complex Standard Optimal Control model formulation and to the simpler Transfer Function/Noise representation is discussed. Problems pertinent to structural identifiability and the form of the parameterization are elucidated. A systematic approach toward the identification of the observer acquisition model parameters from ensemble tracking error data is presented.

Using landscape disturbance and succession models to support forest management

Treesearch

Eric J. Gustafson; Brian R. Sturtevant; Anatoly S. Shvidenko; Robert M. Scheller

2010-01-01

Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales. These interactions can be of such complexity that predictions of future forest ecosystem states are beyond the analytical capability of the human mind. Landscape disturbance and succession models (LDSM) are predictive and...

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Computational Modeling of Hypothalamic-Pituitary-Gonadal Axis to Predict Adaptive Responses in Female Fathead Minnows Exposed to an Aromatase Inhibitor

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose response and time-course...

Combining Satellite Measurements and Numerical Flood Prediction Models to Save Lives and Property from Flooding

NASA Astrophysics Data System (ADS)

Saleh, F.; Garambois, P. A.; Biancamaria, S.

2017-12-01

Floods are considered the major natural threats to human societies across all continents. Consequences of floods in highly populated areas are more dramatic with losses of human lives and substantial property damage. This risk is projected to increase with the effects of climate change, particularly sea-level rise, increasing storm frequencies and intensities and increasing population and economic assets in such urban watersheds. Despite the advances in computational resources and modeling techniques, significant gaps exist in predicting complex processes and accurately representing the initial state of the system. Improving flood prediction models and data assimilation chains through satellite has become an absolute priority to produce accurate flood forecasts with sufficient lead times. The overarching goal of this work is to assess the benefits of the Surface Water Ocean Topography SWOT satellite data from a flood prediction perspective. The near real time methodology is based on combining satellite data from a simulator that mimics the future SWOT data, numerical models, high resolution elevation data and real-time local measurement in the New York/New Jersey area.

Mining data from CFD simulation for aneurysm and carotid bifurcation models.

PubMed

Miloš, Radović; Dejan, Petrović; Nenad, Filipović

2011-01-01

Arterial geometry variability is present both within and across individuals. To analyze the influence of geometric parameters, blood density, dynamic viscosity and blood velocity on wall shear stress (WSS) distribution in the human carotid artery bifurcation and aneurysm, the computer simulations were run to generate the data pertaining to this phenomenon. In our work we evaluate two prediction models for modeling these relationships: neural network model and k-nearest neighbor model. The results revealed that both models have high prediction ability for this prediction task. The achieved results represent progress in assessment of stroke risk for a given patient data in real time.

Application of the relative activity factor approach in scaling from heterologously expressed cytochromes p450 to human liver microsomes: studies on amitriptyline as a model substrate.

PubMed

Venkatakrishnan, K; von Moltke, L L; Greenblatt, D J

2001-04-01

The relative activity factor (RAF) approach is being increasingly used in the quantitative phenotyping of multienzyme drug biotransformations. Using lymphoblast-expressed cytochromes P450 (CYPs) and the tricyclic antidepressant amitriptyline as a model substrate, we have tested the hypothesis that the human liver microsomal rates of a biotransformation mediated by multiple CYP isoforms can be mathematically reconstructed from the rates of the biotransformation catalyzed by individual recombinant CYPs using the RAF approach, and that the RAF approach can be used for the in vitro-in vivo scaling of pharmacokinetic clearance from in vitro intrinsic clearance measurements in heterologous expression systems. In addition, we have compared the results of two widely used methods of quantitative reaction phenotyping, namely, chemical inhibition studies and the prediction of relative contributions of individual CYP isoforms using the RAF approach. For the pathways of N-demethylation (mediated by CYPs 1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4) and E-10 hydroxylation (mediated by CYPs 2B6, 2D6, and 3A4), the model-predicted biotransformation rates in microsomes from a panel of 12 human livers determined from enzyme kinetic parameters of the recombinant CYPs were similar to, and correlated with the observed rates. The model-predicted clearance via N-demethylation was 53% lower than the previously reported in vivo pharmacokinetic estimates. Model-predicted relative contributions of individual CYP isoforms to the net biotransformation rate were similar to, and correlated with the fractional decrement in human liver microsomal reaction rates by chemical inhibitors of the respective CYPs, provided the chemical inhibitors used were specific to their target CYP isoforms.

A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following

NASA Astrophysics Data System (ADS)

Wei, Junqing; Dolan, John M.; Litkouhi, Bakhtiar

2010-04-01

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Culture Representation in Human Reliability Analysis

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

David Gertman; Julie Marble; Steven Novack

Understanding human-system response is critical to being able to plan and predict mission success in the modern battlespace. Commonly, human reliability analysis has been used to predict failures of human performance in complex, critical systems. However, most human reliability methods fail to take culture into account. This paper takes an easily understood state of the art human reliability analysis method and extends that method to account for the influence of culture, including acceptance of new technology, upon performance. The cultural parameters used to modify the human reliability analysis were determined from two standard industry approaches to cultural assessment: Hofstede’s (1991)more » cultural factors and Davis’ (1989) technology acceptance model (TAM). The result is called the Culture Adjustment Method (CAM). An example is presented that (1) reviews human reliability assessment with and without cultural attributes for a Supervisory Control and Data Acquisition (SCADA) system attack, (2) demonstrates how country specific information can be used to increase the realism of HRA modeling, and (3) discusses the differences in human error probability estimates arising from cultural differences.« less

Silent Expectations: Dynamic Causal Modeling of Cortical Prediction and Attention to Sounds That Weren't.

PubMed

Chennu, Srivas; Noreika, Valdas; Gueorguiev, David; Shtyrov, Yury; Bekinschtein, Tristan A; Henson, Richard

2016-08-10

There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known "mismatch response." But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain's electromagnetic activity, we show that it also generates an "omission response" that is contingent on the presence of attention. We model these responses computationally, revealing that mismatch and omission responses only differ in the location of inputs into the same underlying neuronal network. In both cases, we show that attention selectively strengthens the brain's prediction of the future. Copyright © 2016 Chennu et al.

Refined stratified-worm-burden models that incorporate specific biological features of human and snail hosts provide better estimates of Schistosoma diagnosis, transmission, and control.

PubMed

Gurarie, David; King, Charles H; Yoon, Nara; Li, Emily

2016-08-04

Schistosoma parasites sustain a complex transmission process that cycles between a definitive human host, two free-swimming larval stages, and an intermediate snail host. Multiple factors modify their transmission and affect their control, including heterogeneity in host populations and environment, the aggregated distribution of human worm burdens, and features of parasite reproduction and host snail biology. Because these factors serve to enhance local transmission, their inclusion is important in attempting accurate quantitative prediction of the outcomes of schistosomiasis control programs. However, their inclusion raises many mathematical and computational challenges. To address these, we have recently developed a tractable stratified worm burden (SWB) model that occupies an intermediate place between simpler deterministic mean worm burden models and the very computationally-intensive, autonomous agent models. To refine the accuracy of model predictions, we modified an earlier version of the SWB by incorporating factors representing essential in-host biology (parasite mating, aggregation, density-dependent fecundity, and random egg-release) into demographically structured host communities. We also revised the snail component of the transmission model to reflect a saturable form of human-to-snail transmission. The new model allowed us to realistically simulate overdispersed egg-test results observed in individual-level field data. We further developed a Bayesian-type calibration methodology that accounted for model and data uncertainties. The new model methodology was applied to multi-year, individual-level field data on S. haematobium infections in coastal Kenya. We successfully derived age-specific estimates of worm burden distributions and worm fecundity and crowding functions for children and adults. Estimates from the new SWB model were compared with those from the older, simpler SWB with some substantial differences noted. We validated our new SWB estimates in prediction of drug treatment-based control outcomes for a typical Kenyan community. The new version of the SWB model provides a better tool to predict the outcomes of ongoing schistosomiasis control programs. It reflects parasite features that augment and perpetuate transmission, while it also readily incorporates differences in diagnostic testing and human sub-population differences in treatment coverage. Once extended to other Schistosoma species and transmission environments, it will provide a useful and efficient tool for planning control and elimination strategies.

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Task-Specific Response Strategy Selection on the Basis of Recent Training Experience

PubMed Central

Fulvio, Jacqueline M.; Green, C. Shawn; Schrater, Paul R.

2014-01-01

The goal of training is to produce learning for a range of activities that are typically more general than the training task itself. Despite a century of research, predicting the scope of learning from the content of training has proven extremely difficult, with the same task producing narrowly focused learning strategies in some cases and broadly scoped learning strategies in others. Here we test the hypothesis that human subjects will prefer a decision strategy that maximizes performance and reduces uncertainty given the demands of the training task and that the strategy chosen will then predict the extent to which learning is transferable. To test this hypothesis, we trained subjects on a moving dot extrapolation task that makes distinct predictions for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping for training stimuli, and a general model-based strategy that utilizes humans' default predictive model for a class of trajectories. When the number of distinct training trajectories is low, we predict better performance for the mapping strategy, but as the number increases, a predictive model is increasingly favored. Consonant with predictions, subject extrapolations for test trajectories were consistent with using a mapping strategy when trained on a small number of training trajectories and a predictive model when trained on a larger number. The general framework developed here can thus be useful both in interpreting previous patterns of task-specific versus task-general learning, as well as in building future training paradigms with certain desired outcomes. PMID:24391490

Human factors of in-vehicle driver information systems : an executive summary

DOT National Transportation Integrated Search

1997-01-01

This report summarizes a multiyear program concerning driver interfaces for future cars. The goals were to develop (1) human Factors guidelines, (2) methods for testing safety and ease of use, and (3) a model that predicts human performance with thes...

Neural network model for survival and growth of Salmonella 8,20:-:z6 in ground chicken thigh meat during cold storage: extrapolation to other serotypes

USDA-ARS?s Scientific Manuscript database

Mathematical models that predict behavior of human bacterial pathogens in food are valuable tools for assessing and managing this risk to public health. A study was undertaken to develop a model for predicting behavior of Salmonella 8,20:-:z6 in chicken meat during cold storage and to determine how...

Principal component analysis in construction of 3D human knee joint models using a statistical shape model method.

PubMed

Tsai, Tsung-Yuan; Li, Jing-Sheng; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Li, Guoan

2015-01-01

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the three-dimensional (3D) joint surface model has been reported in the literature. In this study, we constructed a SSM database using 152 human computed tomography (CT) knee joint models, including the femur, tibia and patella and analysed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 s using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus, it may have a broad application in computer-assisted knee surgeries that require 3D surface models of the knee.

Quantitative prediction of ionization effect on human skin permeability.

PubMed

Baba, Hiromi; Ueno, Yusuke; Hashida, Mitsuru; Yamashita, Fumiyoshi

2017-04-30

Although skin permeability of an active ingredient can be severely affected by its ionization in a dose solution, most of the existing prediction models cannot predict such impacts. To provide reliable predictors, we curated a novel large dataset of in vitro human skin permeability coefficients for 322 entries comprising chemically diverse permeants whose ionization fractions can be calculated. Subsequently, we generated thousands of computational descriptors, including LogD (octanol-water distribution coefficient at a specific pH), and analyzed the dataset using nonlinear support vector regression (SVR) and Gaussian process regression (GPR) combined with greedy descriptor selection. The SVR model was slightly superior to the GPR model, with externally validated squared correlation coefficient, root mean square error, and mean absolute error values of 0.94, 0.29, and 0.21, respectively. These models indicate that Log D is effective for a comprehensive prediction of ionization effects on skin permeability. In addition, the proposed models satisfied the statistical criteria endorsed in recent model validation studies. These models can evaluate virtually generated compounds at any pH; therefore, they can be used for high-throughput evaluations of numerous active ingredients and optimization of their skin permeability with respect to permeant ionization. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial Evolution of Human Dialects

NASA Astrophysics Data System (ADS)

Burridge, James

2017-07-01

The geographical pattern of human dialects is a result of history. Here, we formulate a simple spatial model of language change which shows that the final result of this historical evolution may, to some extent, be predictable. The model shows that the boundaries of language dialect regions are controlled by a length minimizing effect analogous to surface tension, mediated by variations in population density which can induce curvature, and by the shape of coastline or similar borders. The predictability of dialect regions arises because these effects will drive many complex, randomized early states toward one of a smaller number of stable final configurations. The model is able to reproduce observations and predictions of dialectologists. These include dialect continua, isogloss bundling, fanning, the wavelike spread of dialect features from cities, and the impact of human movement on the number of dialects that an area can support. The model also provides an analytical form for Séguy's curve giving the relationship between geographical and linguistic distance, and a generalization of the curve to account for the presence of a population center. A simple modification allows us to analytically characterize the variation of language use by age in an area undergoing linguistic change.

Humans in Biogeophysical Models: Colonial Period Human-Environment Interactions in the Northeastern United States

NASA Astrophysics Data System (ADS)

Parolari, A.; Greco, F.; Green, M.; Lally, M.; Hermans, C.

2008-12-01

Earth system models increasingly require representation of human activities and the important role they play in the environment. At the most fundamental level, human decisions are driven by the need to acquire basic resources - nutrients, energy, water, and space - each derived from the biogeophysical setting. Modern theories in Ecological Economics place these basic resources at the base of a consumption hierarchy (from subsistence to luxury resources) on which societies and economies are built. Human decisions at all levels of this hierarchy are driven by dynamic environmental, social, and economic factors. Therefore, models merging socio-economic and biogeophysical dynamics are required to predict the evolving relationship between humans and the hydrologic cycle. To provide an example, our study focuses on changes to the hydrologic cycle during the United States colonial period (1600 to 1800). Both direct, intentional, human water use (e.g. water supply, irrigation, or hydropower) and indirect, unintentional effects resulting from the use of other resources (e.g. deforestation or beaver trapping) are considered. We argue that water was not the limiting resource to either the Native or Colonist population growth. However, food and tobacco production and harvesting of beaver pelts led to indirect interventions and consequent changes in the hydrologic cycle. The analysis presented here suggests the importance of incorporating human decision- making dynamics with existing geophysical models to fully understand trajectories of human-environment interactions. Predictive tools of this type are critical to characterizing the long-term signature of humans on the landscape and hydrologic cycle.

Biological and statistical approaches to predicting human lung cancer risk from silica.

PubMed

Kuempel, E D; Tran, C L; Bailer, A J; Porter, D W; Hubbs, A F; Castranova, V

2001-01-01

Chronic inflammation is a key step in the pathogenesis of particle-elicited fibrosis and lung cancer in rats, and possibly in humans. In this study, we compute the excess risk estimates for lung cancer in humans with occupational exposure to crystalline silica, using both rat and human data, and using both a threshold approach and linear models. From a toxicokinetic/dynamic model fit to lung burden and pulmonary response data from a subchronic inhalation study in rats, we estimated the minimum critical quartz lung burden (Mcrit) associated with reduced pulmonary clearance and increased neutrophilic inflammation. A chronic study in rats was also used to predict the human excess risk of lung cancer at various quartz burdens, including mean Mcrit (0.39 mg/g lung). We used a human kinetic lung model to link the equivalent lung burdens to external exposures in humans. We then computed the excess risk of lung cancer at these external exposures, using data of workers exposed to respirable crystalline silica and using Poisson regression and lifetable analyses. Finally, we compared the lung cancer excess risks estimated from male rat and human data. We found that the rat-based linear model estimates were approximately three times higher than those based on human data (e.g., 2.8% in rats vs. 0.9-1% in humans, at mean Mcrit lung burden or associated mean working lifetime exposure of 0.036 mg/m3). Accounting for variability and uncertainty resulted in 100-1000 times lower estimates of human critical lung burden and airborne exposure. This study illustrates that assumptions about the relevant biological mechanism, animal model, and statistical approach can all influence the magnitude of lung cancer risk estimates in humans exposed to crystalline silica.

Esterase Activity and Intracellular Localization in Reconstructed Human Epidermal Cultured Skin Models.

PubMed

Tokudome, Yoshihiro; Katayanagi, Mishina; Hashimoto, Fumie

2015-06-01

Reconstructed human epidermal culture skin models have been developed for cosmetic and pharmaceutical research. This study evaluated the total and carboxyl esterase activities (i.e., Km and Vmax , respectively) and localization in two reconstructed human epidermal culture skin models (LabCyte EPI-MODEL [Japan Tissue Engineering] and EpiDerm [MatTek/Kurabo]). The usefulness of the reconstruction cultured epidermis was also verified by comparison with human and rat epidermis. Homogenized epidermal samples were fractioned by centrifugation. p-nitrophenyl acetate and 4-methylumbelliferyl acetate were used as substrates of total esterase and carboxyl esterase, respectively. Total and carboxyl esterase activities were present in the reconstructed human epidermal culture skin models and were localized in the cytosol. Moreover, the activities and localization were the same as those in human and rat epidermis. LabCyte EPI-MODEL and EpiDerm are potentially useful for esterase activity prediction in human epidermis.

Esterase Activity and Intracellular Localization in Reconstructed Human Epidermal Cultured Skin Models

PubMed Central

Katayanagi, Mishina; Hashimoto, Fumie

2015-01-01

Background Reconstructed human epidermal culture skin models have been developed for cosmetic and pharmaceutical research. Objective This study evaluated the total and carboxyl esterase activities (i.e., Km and Vmax, respectively) and localization in two reconstructed human epidermal culture skin models (LabCyte EPI-MODEL [Japan Tissue Engineering] and EpiDerm [MatTek/Kurabo]). The usefulness of the reconstruction cultured epidermis was also verified by comparison with human and rat epidermis. Methods Homogenized epidermal samples were fractioned by centrifugation. p-nitrophenyl acetate and 4-methylumbelliferyl acetate were used as substrates of total esterase and carboxyl esterase, respectively. Results Total and carboxyl esterase activities were present in the reconstructed human epidermal culture skin models and were localized in the cytosol. Moreover, the activities and localization were the same as those in human and rat epidermis. Conclusion LabCyte EPI-MODEL and EpiDerm are potentially useful for esterase activity prediction in human epidermis. PMID:26082583

A computational language approach to modeling prose recall in schizophrenia

PubMed Central

Rosenstein, Mark; Diaz-Asper, Catherine; Foltz, Peter W.; Elvevåg, Brita

2014-01-01

Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall. PMID:24709122

An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan).

PubMed

Yu, Hwa-Lung; Angulo, José M; Cheng, Ming-Hung; Wu, Jiaping; Christakos, George

2014-05-01

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A new flexible plug and play scheme for modeling, simulating, and predicting gastric emptying

PubMed Central

2014-01-01

Background In-silico models that attempt to capture and describe the physiological behavior of biological organisms, including humans, are intrinsically complex and time consuming to build and simulate in a computing environment. The level of detail of description incorporated in the model depends on the knowledge of the system’s behavior at that level. This knowledge is gathered from the literature and/or improved by knowledge obtained from new experiments. Thus model development is an iterative developmental procedure. The objective of this paper is to describe a new plug and play scheme that offers increased flexibility and ease-of-use for modeling and simulating physiological behavior of biological organisms. Methods This scheme requires the modeler (user) first to supply the structure of the interacting components and experimental data in a tabular format. The behavior of the components described in a mathematical form, also provided by the modeler, is externally linked during simulation. The advantage of the plug and play scheme for modeling is that it requires less programming effort and can be quickly adapted to newer modeling requirements while also paving the way for dynamic model building. Results As an illustration, the paper models the dynamics of gastric emptying behavior experienced by humans. The flexibility to adapt the model to predict the gastric emptying behavior under varying types of nutrient infusion in the intestine (ileum) is demonstrated. The predictions were verified with a human intervention study. The error in predicting the half emptying time was found to be less than 6%. Conclusions A new plug-and-play scheme for biological systems modeling was developed that allows changes to the modeled structure and behavior with reduced programming effort, by abstracting the biological system into a network of smaller sub-systems with independent behavior. In the new scheme, the modeling and simulation becomes an automatic machine readable and executable task. PMID:24917054

Predicting Human Cooperation

PubMed Central

Nay, John J.; Vorobeychik, Yevgeniy

2016-01-01

The Prisoner’s Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner’s Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner’s Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner’s Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner’s Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation. PMID:27171417

Predictive Computational Modeling of Chromatin Folding

NASA Astrophysics Data System (ADS)

di Pierro, Miichele; Zhang, Bin; Wolynes, Peter J.; Onuchic, Jose N.

In vivo, the human genome folds into well-determined and conserved three-dimensional structures. The mechanism driving the folding process remains unknown. We report a theoretical model (MiChroM) for chromatin derived by using the maximum entropy principle. The proposed model allows Molecular Dynamics simulations of the genome using as input the classification of loci into chromatin types and the presence of binding sites of loop forming protein CTCF. The model was trained to reproduce the Hi-C map of chromosome 10 of human lymphoblastoid cells. With no additional tuning the model was able to predict accurately the Hi-C maps of chromosomes 1-22 for the same cell line. Simulations show unknotted chromosomes, phase separation of chromatin types and a preference of chromatin of type A to sit at the periphery of the chromosomes.

Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

DTIC Science & Technology

2016-07-27

make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

How well has the spread of sudden oak death been predicted by the models in northern California?

Treesearch

Yana Valachovic; Richard Cobb; Brendan Twieg

2017-01-01

Since Phytophthora ramorum established in the wildlands of California during the 1990s, the disease has spread rapidly throughout the state’s coastal and adjacent counties, likely by a combination of human-aided events (e.g., nursery plant introductions) and natural dispersal. While human-aided events are almost impossible to predict, dedicated...

Inclusion of an ultraviolet radiation transfer component in an urban forest effects model for predicting tree influences on potential below-canopy exposure to UVB radiation

Treesearch

Gordon M. Heisler; Richard H. Grant; David J. Nowak; Wei Gao; Daniel E. Crane; Jeffery T. Walton

2003-01-01

Evaluating the impact of ultraviolet-B radiation (UVB) on urban populations would be enhanced by improved predictions of the UVB radiation at the level of human activity. This paper reports the status of plans for incorporating a UVB prediction module into an existing Urban Forest Effects (UFORE) model. UFORE currently has modules to quantify urban forest structure,...

A Quantitative Model of Expert Transcription Typing

DTIC Science & Technology

1993-03-08

side of pure psychology, several researchers have argued that transcription typing is a particularly good activity for the study of human skilled...phenomenon with a quantitative METT prediction. The first, quick and dirty analysis gives a good prediction of the copy span, in fact, it is even...typing, it should be demonstrated that the mechanism of the model does not get in the way of good predictions. If situations occur where the entire

Prospective Design of Anti‐Transferrin Receptor Bispecific Antibodies for Optimal Delivery into the Human Brain

PubMed Central

Kanodia, JS; Gadkar, K; Bumbaca, D; Zhang, Y; Tong, RK; Luk, W; Hoyte, K; Lu, Y; Wildsmith, KR; Couch, JA; Watts, RJ; Dennis, MS; Ernst, JA; Scearce‐Levie, K; Atwal, JK; Joseph, S

2016-01-01

Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development. PMID:27299941

Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

PubMed Central

Hadrup, Niels; Taxvig, Camilla; Pedersen, Mikael; Nellemann, Christine; Hass, Ulla; Vinggaard, Anne Marie

2013-01-01

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals. PMID:23990906

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

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

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

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

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Spatially explicit models, generalized reproduction numbers and the prediction of patterns of waterborne disease

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Gatto, M.; Mari, L.; Casagrandi, R.; Righetto, L.; Bertuzzo, E.; Rodriguez-Iturbe, I.

2012-12-01

Metacommunity and individual-based theoretical models are studied in the context of the spreading of infections of water-borne diseases along the ecological corridors defined by river basins and networks of human mobility. The overarching claim is that mathematical models can indeed provide predictive insight into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. To support the claim, we examine the ex-post reliability of published predictions of the 2010-2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. For each modeled epidemic trajectory, it is assessed how well predictions reproduced the observed spatial and temporal features of the outbreak to date. The impact of different approaches is considered to the modeling of the spatial spread of V. cholera, the mechanics of cholera transmission and in accounting for the dynamics of susceptible and infected individuals within different local human communities. A generalized model for Haitian epidemic cholera and the related uncertainty is thus constructed and applied to the year-long dataset of reported cases now available. Specific emphasis will be dedicated to models of human mobility, a fundamental infection mechanism. Lessons learned and open issues are discussed and placed in perspective, supporting the conclusion that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control. Although explicit spatial modeling is made routinely possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is still lacking. Here, we show that the requirement that all the local reproduction numbers R0 be larger than unity is neither necessary nor sufficient for outbreaks to occur when local settlements are connected by networks of primary and secondary infection mechanisms. To determine onset conditions, we derive general analytical expressions for a reproduction matrix G0 explicitly accounting for spatial distributions of human settlements and pathogen transmission via hydrological and human mobility networks. At disease onset, a generalized reproduction number Λ0 (the dominant eigenvalue of G0) must be larger than unity. We also show that geographical outbreak patterns in complex environments are linked to the dominant eigenvector and to spectral properties of G0. Tests against data and computations for the 2010 Haiti and 2000 KwaZulu-Natal cholera outbreaks, as well as against computations for metapopulation networks, demonstrate that eigenvectors of G0 provide a synthetic and effective tool for predicting the disease course in space and time. Networked connectivity models, describing the interplay between hydrology, epidemiology and social behavior sustaining human mobility, thus prove to be key tools for emergency management of waterborne infections.

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

Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

2015-01-01

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.

Predicting human olfactory perception from chemical features of odor molecules.

PubMed

Keller, Andreas; Gerkin, Richard C; Guan, Yuanfang; Dhurandhar, Amit; Turu, Gabor; Szalai, Bence; Mainland, Joel D; Ihara, Yusuke; Yu, Chung Wen; Wolfinger, Russ; Vens, Celine; Schietgat, Leander; De Grave, Kurt; Norel, Raquel; Stolovitzky, Gustavo; Cecchi, Guillermo A; Vosshall, Leslie B; Meyer, Pablo

2017-02-24

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule. Copyright © 2017, American Association for the Advancement of Science.

Ensemble forecast of human West Nile virus cases and mosquito infection rates

NASA Astrophysics Data System (ADS)

Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

2017-02-01

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Ensemble forecast of human West Nile virus cases and mosquito infection rates.

PubMed

DeFelice, Nicholas B; Little, Eliza; Campbell, Scott R; Shaman, Jeffrey

2017-02-24

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

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.

The High-Throughput Stochastic Human Exposure and Dose Simulation Model (SHEDS-HT) & The Chemical and Products Database (CPDat)

EPA Science Inventory

The Stochastic Human Exposure and Dose Simulation Model – High-Throughput (SHEDS-HT) is a U.S. Environmental Protection Agency research tool for predicting screening-level (low-tier) exposures to chemicals in consumer products. This course will present an overview of this m...

Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models (2016 IVIVE Workshop Proceedings)

EPA Science Inventory

Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative st...

Computational Model of Steroidogenesis in Human H295R Cells to Predict Biochemical Response to Endocrine Active Chemicals: Model Development for Metyrapone

EPA Science Inventory

BACKGROUND: An in vitro steroidogenesis assay using the human adrenocortical carcinoma cells H295R is being evaluated as a possible toxicity screening approach to detect and assess the impact of endocrine active chemicals (EAC) capable of altering steroid biosynthesis. Interpreta...

Early stress and human behavioral development: emerging evolutionary perspectives.

PubMed

Del Giudice, M

2014-08-01

Stress experienced early in life exerts a powerful, lasting influence on development. Converging empirical findings show that stressful experiences become deeply embedded in the child's neurobiology, with an astonishing range of long-term effects on cognition, emotion, and behavior. In contrast with the prevailing view that such effects are the maladaptive outcomes of 'toxic' stress, adaptive models regard them as manifestations of evolved developmental plasticity. In this paper, I offer a brief introduction to adaptive models of early stress and human behavioral development, with emphasis on recent theoretical contributions and emerging concepts in the field. I begin by contrasting dysregulation models of early stress with their adaptive counterparts; I then introduce life history theory as a unifying framework, and review recent work on predictive adaptive responses (PARs) in human life history development. In particular, I discuss the distinction between forecasting the future state of the environment (external prediction) and forecasting the future state of the organism (internal prediction). Next, I present the adaptive calibration model, an integrative model of individual differences in stress responsivity based on life history concepts. I conclude by examining how maternal-fetal conflict may shape the physiology of prenatal stress and its adaptive and maladaptive effects on postnatal development. In total, I aim to show how theoretical work from evolutionary biology is reshaping the way we think about the role of stress in human development, and provide researchers with an up-to-date conceptual map of this fascinating and rapidly evolving field.

Identification of cis-suppression of human disease mutations by comparative genomics

PubMed Central

Jordan, Daniel M.; Frangakis, Stephan G.; Golzio, Christelle; Cassa, Christopher A.; Kurtzberg, Joanne; Davis, Erica E.; Sunyaev, Shamil R.; Katsanis, Nicholas

2015-01-01

Patterns of amino acid conservation have served as a tool for understanding protein evolution1. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients2. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes3,4 revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity5,6. PMID:26123021

Novelty and Inductive Generalization in Human Reinforcement Learning.

PubMed

Gershman, Samuel J; Niv, Yael

2015-07-01

In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. Copyright © 2015 Cognitive Science Society, Inc.

Novelty and Inductive Generalization in Human Reinforcement Learning

PubMed Central

Gershman, Samuel J.; Niv, Yael

2015-01-01

In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: what is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: how can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of reinforcement learning in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional reinforcement learning algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. PMID:25808176

Establishment of the Northeast Coastal Watershed Geospatial Data Network (NECWGDN)

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

Hannigan, Robyn

The goals of NECWGDN were to establish integrated geospatial databases that interfaced with existing open-source (water.html) environmental data server technologies (e.g., HydroDesktop) and included ecological and human data to enable evaluation, prediction, and adaptation in coastal environments to climate- and human-induced threats to the coastal marine resources within the Gulf of Maine. We have completed the development and testing of a "test bed" architecture that is compatible with HydroDesktop and have identified key metadata structures that will enable seamless integration and delivery of environmental, ecological, and human data as well as models to predict threats to end-users. Uniquely this databasemore » integrates point as well as model data and so offers capacities to end-users that are unique among databases. Future efforts will focus on the development of integrated environmental-human dimension models that can serve, in near real time, visualizations of threats to coastal resources and habitats.« less

Dynamics Of Human Motion The Case Study of an Examination Hall

NASA Astrophysics Data System (ADS)

Ogunjo, Samuel; Ajayi, Oluwaseyi; Fuwape, Ibiyinka; Dansu, Emmanuel

Human behaviour is difficult to characterize and generalize due to ITS complex nature. Advances in mathematical models have enabled human systems such as love interaction, alcohol abuse, admission problem to be described using models. This study investigates one of such problems, the dynamics of human motion in an examination hall with limited computer systems such that students write their examination in batches. The examination is characterized by time (t) allocated to each students and difficulty level (dl) associated with the examination. A stochastic model based on the difficulty level of the examination was developed for the prediction of student's motion around the examination hall. A good agreement was obtained between theoretical predictions and numerical simulation. The result obtained will help in better planning of examination session to maximize available resources. Furthermore, results obtained in the research can be extended to other areas such as banking hall, customer service points where available resources will be shared amongst many users.

Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model.

PubMed

Jürgens, Tim; Brand, Thomas

2009-11-01

This study compares the phoneme recognition performance in speech-shaped noise of a microscopic model for speech recognition with the performance of normal-hearing listeners. "Microscopic" is defined in terms of this model twofold. First, the speech recognition rate is predicted on a phoneme-by-phoneme basis. Second, microscopic modeling means that the signal waveforms to be recognized are processed by mimicking elementary parts of human's auditory processing. The model is based on an approach by Holube and Kollmeier [J. Acoust. Soc. Am. 100, 1703-1716 (1996)] and consists of a psychoacoustically and physiologically motivated preprocessing and a simple dynamic-time-warp speech recognizer. The model is evaluated while presenting nonsense speech in a closed-set paradigm. Averaged phoneme recognition rates, specific phoneme recognition rates, and phoneme confusions are analyzed. The influence of different perceptual distance measures and of the model's a-priori knowledge is investigated. The results show that human performance can be predicted by this model using an optimal detector, i.e., identical speech waveforms for both training of the recognizer and testing. The best model performance is yielded by distance measures which focus mainly on small perceptual distances and neglect outliers.

A neuromathematical model of human information processing and its application to science content acquisition

NASA Astrophysics Data System (ADS)

Anderson, O. Roger

The rate of information processing during science learning and the efficiency of the learner in mobilizing relevant information in long-term memory as an aid in transmitting newly acquired information to stable storage in long-term memory are fundamental aspects of science content acquisition. These cognitive processes, moreover, may be substantially related in tempo and quality of organization to the efficiency of higher thought processes such as divergent thinking and problem-solving ability that characterize scientific thought. As a contribution to our quantitative understanding of these fundamental information processes, a mathematical model of information acquisition is presented and empirically evaluated in comparison to evidence obtained from experimental studies of science content acquisition. Computer-based models are used to simulate variations in learning parameters and to generate the theoretical predictions to be empirically tested. The initial tests of the predictive accuracy of the model show close agreement between predicted and actual mean recall scores in short-term learning tasks. Implications of the model for human information acquisition and possible future research are discussed in the context of the unique theoretical framework of the model.

Knowledge and implicature: modeling language understanding as social cognition.

PubMed

Goodman, Noah D; Stuhlmüller, Andreas

2013-01-01

Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the rational speech-act theory: Listeners assume that speakers choose their utterances approximately optimally, and listeners interpret an utterance by using Bayesian inference to "invert" this model of the speaker. We apply this framework to model scalar implicature ("some" implies "not all," and "N" implies "not more than N"). This model predicts an interaction between the speaker's knowledge state and the listener's interpretation. We test these predictions in two experiments and find good fit between model predictions and human judgments. Copyright © 2013 Cognitive Science Society, Inc.

Human spaceflight and space adaptations: Computational simulation of gravitational unloading on the spine

NASA Astrophysics Data System (ADS)

Townsend, Molly T.; Sarigul-Klijn, Nesrin

2018-04-01

Living in reduced gravitational environments for a prolonged duration such, as a fly by mission to Mars or an extended stay at the international space station, affects the human body - in particular, the spine. As the spine adapts to spaceflight, morphological and physiological changes cause the mechanical integrity of the spinal column to be compromised, potentially endangering internal organs, nervous health, and human body mechanical function. Therefore, a high fidelity computational model and simulation of the whole human spine was created and validated for the purpose of investigating the mechanical integrity of the spine in crew members during exploratory space missions. A spaceflight exposed spine has been developed through the adaptation of a three-dimensional nonlinear finite element model with the updated Lagrangian formulation of a healthy ground-based human spine in vivo. Simulation of the porohyperelastic response of the intervertebral disc to mechanical unloading resulted in a model capable of accurately predicting spinal swelling/lengthening, spinal motion, and internal stress distribution. The curvature of this space adaptation exposed spine model was compared to a control terrestrial-based finite element model, indicating how the shape changed. Finally, the potential of injury sites to crew members are predicted for a typical 9 day mission.

A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots.

PubMed

Ma, Ye; Xie, Shengquan; Zhang, Yanxin

2016-03-01

A patient-specific electromyography (EMG)-driven neuromuscular model (PENm) is developed for the potential use of human-inspired gait rehabilitation robots. The PENm is modified based on the current EMG-driven models by decreasing the calculation time and ensuring good prediction accuracy. To ensure the calculation efficiency, the PENm is simplified into two EMG channels around one joint with minimal physiological parameters. In addition, a dynamic computation model is developed to achieve real-time calculation. To ensure the calculation accuracy, patient-specific muscle kinematics information, such as the musculotendon lengths and the muscle moment arms during the entire gait cycle, are employed based on the patient-specific musculoskeletal model. Moreover, an improved force-length-velocity relationship is implemented to generate accurate muscle forces. Gait analysis data including kinematics, ground reaction forces, and raw EMG signals from six adolescents at three different speeds were used to evaluate the PENm. The simulation results show that the PENm has the potential to predict accurate joint moment in real-time. The design of advanced human-robot interaction control strategies and human-inspired gait rehabilitation robots can benefit from the application of the human internal state provided by the PENm. Copyright © 2016 Elsevier Ltd. All rights reserved.

A computational feedforward model predicts categorization of masked emotional body language for longer, but not for shorter, latencies.

PubMed

Stienen, Bernard M C; Schindler, Konrad; de Gelder, Beatrice

2012-07-01

Given the presence of massive feedback loops in brain networks, it is difficult to disentangle the contribution of feedforward and feedback processing to the recognition of visual stimuli, in this case, of emotional body expressions. The aim of the work presented in this letter is to shed light on how well feedforward processing explains rapid categorization of this important class of stimuli. By means of parametric masking, it may be possible to control the contribution of feedback activity in human participants. A close comparison is presented between human recognition performance and the performance of a computational neural model that exclusively modeled feedforward processing and was engineered to fulfill the computational requirements of recognition. Results show that the longer the stimulus onset asynchrony (SOA), the closer the performance of the human participants was to the values predicted by the model, with an optimum at an SOA of 100 ms. At short SOA latencies, human performance deteriorated, but the categorization of the emotional expressions was still above baseline. The data suggest that, although theoretically, feedback arising from inferotemporal cortex is likely to be blocked when the SOA is 100 ms, human participants still seem to rely on more local visual feedback processing to equal the model's performance.

Human performance cognitive-behavioral modeling: a benefit for occupational safety.

PubMed

Gore, Brian F

2002-01-01

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Human performance cognitive-behavioral modeling: a benefit for occupational safety

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2002-01-01

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Evaluating the environmental fate of short-chain chlorinated paraffins (SCCPs) in the Nordic environment using a dynamic multimedia model.

PubMed

Krogseth, Ingjerd S; Breivik, Knut; Arnot, Jon A; Wania, Frank; Borgen, Anders R; Schlabach, Martin

2013-12-01

Short chain chlorinated paraffins (SCCPs) raise concerns due to their potential for persistence, bioaccumulation, long-range transport and adverse effects. An understanding of their environmental fate remains limited, partly due to the complexity of the mixture. The purpose of this study was to evaluate whether a mechanistic, integrated, dynamic environmental fate and bioaccumulation multimedia model (CoZMoMAN) can reconcile what is known about environmental emissions and human exposure of SCCPs in the Nordic environment. Realistic SCCP emission scenarios, resolved by formula group, were estimated and used to predict the composition and concentrations of SCCPs in the environment and the human food chain. Emissions at the upper end of the estimated range resulted in predicted total concentrations that were often within a factor of 6 of observations. Similar model performance for a complex group of organic contaminants as for the well-known polychlorinated biphenyls strengthens the confidence in the CoZMoMAN model and implies a relatively good mechanistic understanding of the environmental fate of SCCPs. However, the degree of chlorination predicted for SCCPs in sediments, fish, and humans was higher than observed and poorly established environmental half-lives and biotransformation rate constants contributed to the uncertainties in the predicted composition and ∑SCCP concentrations. Improving prediction of the SCCP composition will also require better constrained estimates of the composition of SCCP emissions. There is, however, also large uncertainty and lack of coherence in the existing observations, and better model-measurement agreement will require improved analytical methods and more strategic sampling. More measurements of SCCP levels and compositions in samples from background regions are particularly important.

Nuclear Fragmentation Processes Relevant for Human Space Radiation Protection

NASA Technical Reports Server (NTRS)

Lin, Zi-Wei

2007-01-01

Space radiation from cosmic ray particles is one of the main challenges for human space explorations such-as a moon base or a trip to Mars. Models have been developed in order to predict the radiation exposure to astronauts and to evaluate the effectiveness of different shielding materials, and a key ingredient in these models is the physics of nuclear fragmentations. We have developed a semi-analytical method to determine which partial cross sections of nuclear fragmentations most affect the radiation dose behind shielding materials due to exposure to galactic cosmic rays. The cross sections thus determined will require more theoretical and/or experimental studies in order for us to better predict, reduce and mitigate the radiation exposure in human space explorations.

Modeling ultrasound propagation through material of increasing geometrical complexity.

PubMed

Odabaee, Maryam; Odabaee, Mostafa; Pelekanos, Matthew; Leinenga, Gerhard; Götz, Jürgen

2018-06-01

Ultrasound is increasingly being recognized as a neuromodulatory and therapeutic tool, inducing a broad range of bio-effects in the tissue of experimental animals and humans. To achieve these effects in a predictable manner in the human brain, the thick cancellous skull presents a problem, causing attenuation. In order to overcome this challenge, as a first step, the acoustic properties of a set of simple bone-modeling resin samples that displayed an increasing geometrical complexity (increasing step sizes) were analyzed. Using two Non-Destructive Testing (NDT) transducers, we found that Wiener deconvolution predicted the Ultrasound Acoustic Response (UAR) and attenuation caused by the samples. However, whereas the UAR of samples with step sizes larger than the wavelength could be accurately estimated, the prediction was not accurate when the sample had a smaller step size. Furthermore, a Finite Element Analysis (FEA) performed in ANSYS determined that the scattering and refraction of sound waves was significantly higher in complex samples with smaller step sizes compared to simple samples with a larger step size. Together, this reveals an interaction of frequency and geometrical complexity in predicting the UAR and attenuation. These findings could in future be applied to poro-visco-elastic materials that better model the human skull. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations.

PubMed

Mamoshina, Polina; Kochetov, Kirill; Putin, Evgeny; Cortese, Franco; Aliper, Alexander; Lee, Won-Suk; Ahn, Sung-Min; Uhn, Lee; Skjodt, Neil; Kovalchuk, Olga; Scheibye-Knudsen, Morten; Zhavoronkov, Alex

2018-01-11

Accurate and physiologically meaningful biomarkers for human aging are key to assessing anti-aging therapies. Given ethnic differences in health, diet, lifestyle, behaviour, environmental exposures and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population-specific hematologic aging clocks. The performance of models was also evaluated on publicly-available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population-specific and combined hematological clocks and all-cause mortality. Overall, this study suggests a) the population-specificity of aging patterns and b) hematologic clocks predicts all-cause mortality. Proposed models added to the freely available Aging.AI system allowing improved ability to assess human aging. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America.

Application of response surface methodology to maximize the productivity of scalable automated human embryonic stem cell manufacture.

PubMed

Ratcliffe, Elizabeth; Hourd, Paul; Guijarro-Leach, Juan; Rayment, Erin; Williams, David J; Thomas, Robert J

2013-01-01

Commercial regenerative medicine will require large quantities of clinical-specification human cells. The cost and quality of manufacture is notoriously difficult to control due to highly complex processes with poorly defined tolerances. As a step to overcome this, we aimed to demonstrate the use of 'quality-by-design' tools to define the operating space for economic passage of a scalable human embryonic stem cell production method with minimal cell loss. Design of experiments response surface methodology was applied to generate empirical models to predict optimal operating conditions for a unit of manufacture of a previously developed automatable and scalable human embryonic stem cell production method. Two models were defined to predict cell yield and cell recovery rate postpassage, in terms of the predictor variables of media volume, cell seeding density, media exchange and length of passage. Predicted operating conditions for maximized productivity were successfully validated. Such 'quality-by-design' type approaches to process design and optimization will be essential to reduce the risk of product failure and patient harm, and to build regulatory confidence in cell therapy manufacturing processes.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Adapting the Water Erosion Prediction Project (WEPP) model for forest applications

Treesearch

Shuhui Dun; Joan Q. Wu; William J. Elliot; Peter R. Robichaud; Dennis C. Flanagan; James R. Frankenberger; Robert E. Brown; Arthur C. Xu

2009-01-01

There has been an increasing public concern over forest stream pollution by excessive sedimentation due to natural or human disturbances. Adequate erosion simulation tools are needed for sound management of forest resources. The Water Erosion Prediction Project (WEPP) watershed model has proved useful in forest applications where Hortonian flow is the major form of...

Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

PubMed Central

Sahaï, Aïsha; Pacherie, Elisabeth; Grynszpan, Ouriel; Berberian, Bruno

2017-01-01

Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents. PMID:29081744

Modeling pilot interaction with automated digital avionics systems: Guidance and control algorithms for contour and nap-of-the-Earth flight

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1990-01-01

A collection of technical papers are presented that cover modeling pilot interaction with automated digital avionics systems and guidance and control algorithms for contour and nap-of-the-earth flight. The titles of the papers presented are as follows: (1) Automation effects in a multiloop manual control system; (2) A qualitative model of human interaction with complex dynamic systems; (3) Generalized predictive control of dynamic systems; (4) An application of generalized predictive control to rotorcraft terrain-following flight; (5) Self-tuning generalized predictive control applied to terrain-following flight; and (6) Precise flight path control using a predictive algorithm.

Visual performance modeling in the human operator simulator

NASA Technical Reports Server (NTRS)

Strieb, M. I.

1979-01-01

A brief description of the history of the development of the human operator simulator (HOS) model is presented. Features of the HOS micromodels that impact on the obtainment of visual performance data are discussed along with preliminary details on a HOS pilot model designed to predict the results of visual performance workload data obtained through oculometer studies on pilots in real and simulated approaches and landings.

Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants.

PubMed

Goldstein-Piekarski, Andrea N; Korgaonkar, Mayuresh S; Green, Erin; Suppes, Trisha; Schatzberg, Alan F; Hastie, Trevor; Nemeroff, Charles B; Williams, Leanne M

2016-10-18

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

USGS Publications Warehouse

Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

2012-01-01

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Predictive performance models and multiple task performance

NASA Technical Reports Server (NTRS)

Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

1989-01-01

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

Multivariate Modelling of the Career Intent of Air Force Personnel.

DTIC Science & Technology

1980-09-01

index (HOPP) was used as a measure of current job satisfaction . As with the Vroom and Fishbein/Graen models, two separate validations were accom...34 Organizational Behavior and Human Performance , 23: 251-267, 1979. Lewis, Logan M. "Expectancy Theory as a Predictive Model of Career Intent, Job Satisfaction ...W. Albright. "Expectancy Theory Predictions of the Satisfaction , Effort, Performance , and Retention of Naval Aviation Officers," Organizational

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

Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning

PubMed Central

Mogi, Masaki; Iwanami, Jun; Min, Li-Juan; Bai, Hui-Yu; Shan, Bao-Shuai; Kukida, Masayoshi; Kan-no, Harumi; Ikeda, Shuntaro; Higaki, Jitsuo; Horiuchi, Masatsugu

2018-01-01

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model. PMID:29415035

Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning.

PubMed

Higaki, Akinori; Mogi, Masaki; Iwanami, Jun; Min, Li-Juan; Bai, Hui-Yu; Shan, Bao-Shuai; Kukida, Masayoshi; Kan-No, Harumi; Ikeda, Shuntaro; Higaki, Jitsuo; Horiuchi, Masatsugu

2018-01-01

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents' spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model.

[Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

PubMed

Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

2013-01-01

QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

PubMed Central

Park, Juyong

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility. PMID:29432440

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Human hepatocytes derived from pluripotent stem cells: a promising cell model for drug hepatotoxicity screening.

PubMed

Gómez-Lechón, María José; Tolosa, Laia

2016-09-01

Drug-induced liver injury (DILI) is a frequent cause of failure in both clinical and post-approval stages of drug development, and poses a key challenge to the pharmaceutical industry. Current animal models offer poor prediction of human DILI. Although several human cell-based models have been proposed for the detection of human DILI, human primary hepatocytes remain the gold standard for preclinical toxicological screening. However, their use is hindered by their limited availability, variability and phenotypic instability. In contrast, pluripotent stem cells, which include embryonic and induced pluripotent stem cells (iPSCs), proliferate extensively in vitro and can be differentiated into hepatocytes by the addition of soluble factors. This provides a stable source of hepatocytes for multiple applications, including early preclinical hepatotoxicity screening. In addition, iPSCs also have the potential to establish genotype-specific cells from different individuals, which would increase the predictivity of toxicity assays allowing more successful clinical trials. Therefore, the generation of human hepatocyte-like cells derived from pluripotent stem cells seems to be promising for overcoming limitations of hepatocyte preparations, and it is expected to have a substantial repercussion in preclinical hepatotoxicity risk assessment in early drug development stages.

Air pollution dispersion models for human exposure predictions in London.

PubMed

Beevers, Sean D; Kitwiroon, Nutthida; Williams, Martin L; Kelly, Frank J; Ross Anderson, H; Carslaw, David C

2013-01-01

The London household survey has shown that people travel and are exposed to air pollutants differently. This argues for human exposure to be based upon space-time-activity data and spatio-temporal air quality predictions. For the latter, we have demonstrated the role that dispersion models can play by using two complimentary models, KCLurban, which gives source apportionment information, and Community Multi-scale Air Quality Model (CMAQ)-urban, which predicts hourly air quality. The KCLurban model is in close agreement with observations of NO(X), NO(2) and particulate matter (PM)(10/2.5), having a small normalised mean bias (-6% to 4%) and a large Index of Agreement (0.71-0.88). The temporal trends of NO(X) from the CMAQ-urban model are also in reasonable agreement with observations. Spatially, NO(2) predictions show that within 10's of metres of major roads, concentrations can range from approximately 10-20 p.p.b. up to 70 p.p.b. and that for PM(10/2.5) central London roadside concentrations are approximately double the suburban background concentrations. Exposure to different PM sources is important and we predict that brake wear-related PM(10) concentrations are approximately eight times greater near major roads than at suburban background locations. Temporally, we have shown that average NO(X) concentrations close to roads can range by a factor of approximately six between the early morning minimum and morning rush hour maximum periods. These results present strong arguments for the hybrid exposure model under development at King's and, in future, for in-building models and a model for the London Underground.

Modeling Visual, Vestibular and Oculomotor Interactions in Self-Motion Estimation

NASA Technical Reports Server (NTRS)

Perrone, John

1997-01-01

A computational model of human self-motion perception has been developed in collaboration with Dr. Leland S. Stone at NASA Ames Research Center. The research included in the grant proposal sought to extend the utility of this model so that it could be used for explaining and predicting human performance in a greater variety of aerospace applications. This extension has been achieved along with physiological validation of the basic operation of the model.

Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

EPA Science Inventory

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, ...

Development of biomechanical models for human factors evaluations

NASA Technical Reports Server (NTRS)

Woolford, Barbara; Pandya, Abhilash; Maida, James

1991-01-01

Previewing human capabilities in a computer-aided engineering mode has assisted greatly in planning well-designed systems without the cost and time involved in mockups and engineering models. To date, the computer models have focused on such variables as field of view, accessibility and fit, and reach envelopes. Program outputs have matured from simple static pictures to animations viewable from any eyepoint. However, while kinematics models are available, there are few biomechanical models available for estimating strength and motion patterns. Those, such as Crew Chief, that are available are based on strength measurements taken in specific positions. Johnson Space Center is pursuing a biomechanical model which will use strength data collected on single joints at two or three velocities to attempt to predict compound motions of several joint simultaneously and the resulting force at the end effector. Two lines of research are coming together to produce this result. One is an attempt to use optimal control theory to predict joint motion in complex motions, and another is the development of graphical representation of human capabilities. The progress to date in this research is described.

The improbable transmission of Trypanosoma cruzi to human: the missing link in the dynamics and control of Chagas disease.

PubMed

Nouvellet, Pierre; Dumonteil, Eric; Gourbière, Sébastien

2013-11-01

Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite its crucial role in the ecology and evolution of the pathogen and the disease. The objective of this study was to quantify the probability of T. cruzi vectorial transmission to humans, and to use such an estimate to predict human prevalence from entomological data. We developed several models of T. cruzi transmission to estimate the probability of transmission from vector to host. Using datasets from the literature, we estimated the probability of transmission per contact with an infected triatomine to be 5.8 × 10(-4) (95%CI: [2.6 ; 11.0] × 10(-4)). This estimate was consistent across triatomine species, robust to variations in other parameters, and corresponded to 900-4,000 contacts per case. Our models subsequently allowed predicting human prevalence from vector abundance and infection rate in 7/10 independent datasets covering various triatomine species and epidemiological situations. This low probability of T. cruzi transmission reflected well the complex and unlikely mechanism of transmission via insect feces, and allowed predicting human prevalence from basic entomological data. Although a proof of principle study would now be valuable to validate our models' predictive ability in an even broader range of entomological and ecological settings, our quantitative estimate could allow switching the evaluation of disease risk and vector control program from purely entomological indexes to parasitological measures, as commonly done for other major vector borne diseases. This might lead to different quantitative perspectives as these indexes are well known not to be proportional one to another.

The Improbable Transmission of Trypanosoma cruzi to Human: The Missing Link in the Dynamics and Control of Chagas Disease

PubMed Central

Nouvellet, Pierre; Dumonteil, Eric; Gourbière, Sébastien

2013-01-01

Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite its crucial role in the ecology and evolution of the pathogen and the disease. The objective of this study was to quantify the probability of T. cruzi vectorial transmission to humans, and to use such an estimate to predict human prevalence from entomological data. We developed several models of T. cruzi transmission to estimate the probability of transmission from vector to host. Using datasets from the literature, we estimated the probability of transmission per contact with an infected triatomine to be 5.8×10−4 (95%CI: [2.6 ; 11.0]×10−4). This estimate was consistent across triatomine species, robust to variations in other parameters, and corresponded to 900–4,000 contacts per case. Our models subsequently allowed predicting human prevalence from vector abundance and infection rate in 7/10 independent datasets covering various triatomine species and epidemiological situations. This low probability of T. cruzi transmission reflected well the complex and unlikely mechanism of transmission via insect feces, and allowed predicting human prevalence from basic entomological data. Although a proof of principle study would now be valuable to validate our models' predictive ability in an even broader range of entomological and ecological settings, our quantitative estimate could allow switching the evaluation of disease risk and vector control program from purely entomological indexes to parasitological measures, as commonly done for other major vector borne diseases. This might lead to different quantitative perspectives as these indexes are well known not to be proportional one to another. PMID:24244766

Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

PubMed Central

Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

2010-01-01

Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

A Circuit Model of Real Time Human Body Hydration.

PubMed

Asogwa, Clement Ogugua; Teshome, Assefa K; Collins, Stephen F; Lai, Daniel T H

2016-06-01

Changes in human body hydration leading to excess fluid losses or overload affects the body fluid's ability to provide the necessary support for healthy living. We propose a time-dependent circuit model of real-time human body hydration, which models the human body tissue as a signal transmission medium. The circuit model predicts the attenuation of a propagating electrical signal. Hydration rates are modeled by a time constant τ, which characterizes the individual specific metabolic function of the body part measured. We define a surrogate human body anthropometric parameter θ by the muscle-fat ratio and comparing it with the body mass index (BMI), we find theoretically, the rate of hydration varying from 1.73 dB/min, for high θ and low τ to 0.05 dB/min for low θ and high τ. We compare these theoretical values with empirical measurements and show that real-time changes in human body hydration can be observed by measuring signal attenuation. We took empirical measurements using a vector network analyzer and obtained different hydration rates for various BMI, ranging from 0.6 dB/min for 22.7 [Formula: see text] down to 0.04 dB/min for 41.2 [Formula: see text]. We conclude that the galvanic coupling circuit model can predict changes in the volume of the body fluid, which are essential in diagnosing and monitoring treatment of body fluid disorder. Individuals with high BMI would have higher time-dependent biological characteristic, lower metabolic rate, and lower rate of hydration.

Model of Tooth Morphogenesis Predicts Carabelli Cusp Expression, Size, and Symmetry in Humans

PubMed Central

Hunter, John P.; Guatelli-Steinberg, Debbie; Weston, Theresia C.; Durner, Ryan; Betsinger, Tracy K.

2010-01-01

Background The patterning cascade model of tooth morphogenesis accounts for shape development through the interaction of a small number of genes. In the model, gene expression both directs development and is controlled by the shape of developing teeth. Enamel knots (zones of nonproliferating epithelium) mark the future sites of cusps. In order to form, a new enamel knot must escape the inhibitory fields surrounding other enamel knots before crown components become spatially fixed as morphogenesis ceases. Because cusp location on a fully formed tooth reflects enamel knot placement and tooth size is limited by the cessation of morphogenesis, the model predicts that cusp expression varies with intercusp spacing relative to tooth size. Although previous studies in humans have supported the model's implications, here we directly test the model's predictions for the expression, size, and symmetry of Carabelli cusp, a variation present in many human populations. Methodology/Principal Findings In a dental cast sample of upper first molars (M1s) (187 rights, 189 lefts, and 185 antimeric pairs), we measured tooth area and intercusp distances with a Hirox digital microscope. We assessed Carabelli expression quantitatively as an area in a subsample and qualitatively using two typological schemes in the full sample. As predicted, low relative intercusp distance is associated with Carabelli expression in both right and left samples using either qualitative or quantitative measures. Furthermore, asymmetry in Carabelli area is associated with asymmetry in relative intercusp spacing. Conclusions/Significance These findings support the model's predictions for Carabelli cusp expression both across and within individuals. By comparing right-left pairs of the same individual, our data show that small variations in developmental timing or spacing of enamel knots can influence cusp pattern independently of genotype. Our findings suggest that during evolution new cusps may first appear as a result of small changes in the spacing of enamel knots relative to crown size. PMID:20689576

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

PubMed

Majid, Abdul; Ali, Safdar; Iqbal, Mubashar; Kausar, Nabeela

2014-03-01

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics.

PubMed

Haredasht, S Amirpour; Taylor, C J; Maes, P; Verstraeten, W W; Clement, J; Barrios, M; Lagrou, K; Van Ranst, M; Coppin, P; Berckmans, D; Aerts, J-M

2013-11-01

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks. © 2012 Blackwell Verlag GmbH.

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

Developmental and Reproductive Toxicity (DART) testing is important for assessing the potential consequences of drug and chemical exposure on human health and well-being. Complexity of pregnancy and the reproductive cycle makes DART testing challenging and costly for traditional (animal-based) methods. A compendium of in vitro data from ToxCast/Tox21 high-throughput screening (HTS) programs is available for predictive toxicology. ‘Predictive DART’ will require an integrative strategy that mobilizes HTS data into in silico models that capture the relevant embryology. This lecture addresses progress on EPA's 'virtual embryo'. The question of how tissues and organs are shaped during development is crucial for understanding (and predicting) human birth defects. While ToxCast HTS data may predict developmental toxicity with reasonable accuracy, mechanistic models are still necessary to capture the relevant biology. Subtle microscopic changes induced chemically may amplify to an adverse outcome but coarse changes may override lesion propagation in any complex adaptive system. Modeling system dynamics in a developing tissue is a multiscale problem that challenges our ability to predict toxicity from in vitro profiling data (ToxCast/Tox21). (DISCLAIMER: The views expressed in this presentation are those of the presenter and do not necessarily reflect the views or policies of the US EPA). This was an invited seminar presentation to the National Institute for Public H

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.