The Interplay of Maternal Sensitivity and Toddler Engagement of Mother in Predicting Self-Regulation

ERIC Educational Resources Information Center

Ispa, Jean M.; Su-Russell, Chang; Palermo, Francisco; Carlo, Gustavo

2017-01-01

Using data from the Early Head Start Research and Evaluation Project, a cross-lag mediation model was tested to examine longitudinal relations among low-income mothers' sensitivity; toddlers' engagement of their mothers; and toddler's self-regulation at ages 1, 2, and 3 years (N = 2,958). Age 1 maternal sensitivity predicted self-regulation at…

QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

PubMed

Kovarich, Simona; Papa, Ester; Gramatica, Paola

2011-06-15

The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

Monitoring Cosmic Radiation Risk: Comparisons between Observations and Predictive Codes for Naval Aviation

DTIC Science & Technology

2009-01-01

proton PARMA PHITS -based Analytical Radiation Model in the Atmosphere PCAIRE Predictive Code for Aircrew Radiation Exposure PHITS Particle and...radiation transport code utilized is called PARMA ( PHITS based Analytical Radiation Model in the Atmosphere) [36]. The particle fluxes calculated from the...same dose equivalent coefficient regulations from the ICRP-60 regulations. As a result, the transport codes utilized by EXPACS ( PHITS ) and CARI-6

Monitoring Cosmic Radiation Risk: Comparisons Between Observations and Predictive Codes for Naval Aviation

DTIC Science & Technology

2009-07-05

proton PARMA PHITS -based Analytical Radiation Model in the Atmosphere PCAIRE Predictive Code for Aircrew Radiation Exposure PHITS Particle and Heavy...transport code utilized is called PARMA ( PHITS based Analytical Radiation Model in the Atmosphere) [36]. The particle fluxes calculated from the input...dose equivalent coefficient regulations from the ICRP-60 regulations. As a result, the transport codes utilized by EXPACS ( PHITS ) and CARI-6 (PARMA

PROSTATE REGULATION: MODELING ENDOGENOUS ...

EPA Pesticide Factsheets

Prostate function is an important indicator of androgen status in toxicological studies making predictive modeling of the relevant pharmacokinetics and pharmacodynamics desirable. Prostate function is an important indicator of androgen status in toxicological studies making predictive modeling of the relevant pharmacokinetics and pharmacodynamics desirable.

Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Eure, Kenneth W.

1998-01-01

Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.

Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration

DOE PAGES

McManamay, Ryan A.; Frimpong, Emmanuel A.

2015-01-01

Lotic fish have developed life history strategies adapted to the natural variation in stream flow regimes. The natural timing, duration, and magnitude of flow events has contributed to the diversity, production, and composition of fish assemblages over time. Studies evaluating the role of hydrology in structuring fish assemblages have been more common at the local or regional scale with very few studies conducted at the continental scale. Furthermore, quantitative linkages between natural hydrologic patterns and fish assemblages are rarely used to make predictions of ecological consequences of hydrologic alterations. We ask two questions: (1) what is the relative role ofmore » hydrology in structuring fish assemblages at large scales? and (2) can relationships between fish assemblages and natural hydrology be utilized to predict fish assemblage responses to hydrologic disturbance? We developed models to relate fish life histories and reproductive strategies to landscape and hydrologic variables separately and then combined. Models were then used to predict the ecological consequences of altered hydrology due to dam regulation. Although hydrology plays a considerable role in structuring fish assemblages, the performance of models using only hydrologic variables was lower than that of models constructed using landscape variables. Isolating the relative importance of hydrology in structuring fish assemblages at the continental scale is difficult since hydrology is interrelated to many landscape factors. By applying models to dam-regulated hydrologic data, we observed some consistent predicted responses in fish life history strategies and modes of reproduction. In agreement with existing literature, equilibrium strategists are predicted to increase following dam regulation, whereas opportunistic and periodic species are predicted to decrease. In addition, dam regulation favors the selection of reproductive strategies with extended spawning seasons and preference for stable conditions.« less

Hydrologic filtering of fish life history strategies across the United States: implications for stream flow alteration

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

McManamay, Ryan A.; Frimpong, Emmanuel A.

Lotic fish have developed life history strategies adapted to the natural variation in stream flow regimes. The natural timing, duration, and magnitude of flow events has contributed to the diversity, production, and composition of fish assemblages over time. Studies evaluating the role of hydrology in structuring fish assemblages have been more common at the local or regional scale with very few studies conducted at the continental scale. Furthermore, quantitative linkages between natural hydrologic patterns and fish assemblages are rarely used to make predictions of ecological consequences of hydrologic alterations. We ask two questions: (1) what is the relative role ofmore » hydrology in structuring fish assemblages at large scales? and (2) can relationships between fish assemblages and natural hydrology be utilized to predict fish assemblage responses to hydrologic disturbance? We developed models to relate fish life histories and reproductive strategies to landscape and hydrologic variables separately and then combined. Models were then used to predict the ecological consequences of altered hydrology due to dam regulation. Although hydrology plays a considerable role in structuring fish assemblages, the performance of models using only hydrologic variables was lower than that of models constructed using landscape variables. Isolating the relative importance of hydrology in structuring fish assemblages at the continental scale is difficult since hydrology is interrelated to many landscape factors. By applying models to dam-regulated hydrologic data, we observed some consistent predicted responses in fish life history strategies and modes of reproduction. In agreement with existing literature, equilibrium strategists are predicted to increase following dam regulation, whereas opportunistic and periodic species are predicted to decrease. In addition, dam regulation favors the selection of reproductive strategies with extended spawning seasons and preference for stable conditions.« less

An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein.

PubMed

Wang, Junbai; Wu, Qianqian; Hu, Xiaohua Tony; Tian, Tianhai

2016-11-01

Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is a very important but challenging task. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this challenge, a new integrated method is proposed by combining a top-down approach and a bottom-up approach. First, the top-down approach uses probabilistic graphical models to predict the network structure of DNA repair pathway that is regulated by the p53 protein. Two networks are predicted, namely a network of eight genes with eight inferred interactions and an extended network of 21 genes with 17 interactions. Then, the bottom-up approach using differential equation models is developed to study the detailed genetic regulations based on either a fully connected regulatory network or a gene network obtained by the top-down approach. Model simulation error, parameter identifiability and robustness property are used as criteria to select the optimal network. Simulation results together with permutation tests of input gene network structures indicate that the prediction accuracy and robustness property of the two predicted networks using the top-down approach are better than those of the corresponding fully connected networks. In particular, the proposed approach reduces computational cost significantly for inferring model parameters. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulation. Copyright © 2016 Elsevier Inc. All rights reserved.

An Integrative View of School Functioning: Transactions between Self-Regulation, School Engagement, and Teacher-Child Relationship Quality

PubMed Central

Portilla, Ximena A.; Ballard, Parissa J.; Adler, Nancy E.; Boyce, W. Thomas; Obradović, Jelena

2014-01-01

This study investigates the dynamic interplay between teacher-child relationship quality and children’s behaviors across kindergarten and first grade to predict academic competence in first grade. Using a sample of 338 ethnically diverse 5-year-old children, nested path analytic models were conducted to examine bidirectional pathways between children’s behaviors and teacher-child relationship quality. Low self-regulation in kindergarten fall, as indexed by inattention and impulsive behaviors, predicted more conflict with teachers in kindergarten spring and this effect persisted into first grade. Conflict and low self-regulation jointly predicted decreases in school engagement which in turn predicted first grade academic competence. Findings illustrate the importance of considering transactions between self-regulation, teacher-child relationship quality, and school engagement in predicting academic competence. PMID:24916608

Model-based redesign of global transcription regulation

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso

2009-01-01

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology. PMID:19188257

Systematic review of computational methods for identifying miRNA-mediated RNA-RNA crosstalk.

PubMed

Li, Yongsheng; Jin, Xiyun; Wang, Zishan; Li, Lili; Chen, Hong; Lin, Xiaoyu; Yi, Song; Zhang, Yunpeng; Xu, Juan

2017-10-25

Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA-target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA-target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictive Modelling for Fisheries Management in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Beal, Jacob; Bennett, Sara

A group of Colombian indigenous communities and Amacayacu National Park are cooperating to make regulations for sustainable use of their shared natural resources, especially the fish populations. To aid this effort, we are modeling the interactions among these communities and their ecosystem with the objective of predicting the stability of regulations, identifying potential failure modes, and guiding investment of scarce resources. The goal is to improve the probability of actually achieving fair, sustainable and community-managed subsistence fishing in the region.

The interplay of maternal sensitivity and toddler engagement of mother in predicting self-regulation.

PubMed

Ispa, Jean M; Su-Russell, Chang; Palermo, Francisco; Carlo, Gustavo

2017-03-01

Using data from the Early Head Start Research and Evaluation Project, a cross-lag mediation model was tested to examine longitudinal relations among low-income mothers' sensitivity; toddlers' engagement of their mothers; and toddler's self-regulation at ages 1, 2, and 3 years (N = 2,958). Age 1 maternal sensitivity predicted self-regulation at ages 2 and 3 years, and age 2 engagement of mother mediated the relation between age 1 maternal sensitivity and age 3 self-regulation. Lagged relations from toddler self-regulation at ages 1 and 2 years to later maternal sensitivity were not significant, suggesting stronger influence from mother to toddler than vice versa. Model fit was similar regardless of child gender and depth of family poverty. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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…

CORECLUST: identification of the conserved CRM grammar together with prediction of gene regulation.

PubMed

Nikulova, Anna A; Favorov, Alexander V; Sutormin, Roman A; Makeev, Vsevolod J; Mironov, Andrey A

2012-07-01

Identification of transcriptional regulatory regions and tracing their internal organization are important for understanding the eukaryotic cell machinery. Cis-regulatory modules (CRMs) of higher eukaryotes are believed to possess a regulatory 'grammar', or preferred arrangement of binding sites, that is crucial for proper regulation and thus tends to be evolutionarily conserved. Here, we present a method CORECLUST (COnservative REgulatory CLUster STructure) that predicts CRMs based on a set of positional weight matrices. Given regulatory regions of orthologous and/or co-regulated genes, CORECLUST constructs a CRM model by revealing the conserved rules that describe the relative location of binding sites. The constructed model may be consequently used for the genome-wide prediction of similar CRMs, and thus detection of co-regulated genes, and for the investigation of the regulatory grammar of the system. Compared with related methods, CORECLUST shows better performance at identification of CRMs conferring muscle-specific gene expression in vertebrates and early-developmental CRMs in Drosophila.

Future orientation in the self-system: possible selves, self-regulation, and behavior.

PubMed

Hoyle, Rick H; Sherrill, Michelle R

2006-12-01

Possible selves are representations of the self in the future. Early theoretical accounts of the construct suggested that possible selves directly influence motivation and behavior. We propose an alternative view of possible selves as a component in self-regulatory processes through which motivation and behavior are influenced. We demonstrate the advantages of this conceptualization in two studies that test predictions generated from theoretical models of self-regulation in which the possible selves construct could be embedded. In one study, we show how viewing possible selves as a source of behavioral standards in a control-process model of self-regulation yields support for a set of predictions about the influence of possible selves on current behavior. In the other study, we examine possible selves in the context of an interpersonal model of self-regulation, showing strong evidence of concern for relational value in freely generated hoped-for and feared selves. These findings suggest that the role of possible selves in motivation and behavior can be profitably studied in models that fully specify the process of self-regulation and that those models can be enriched by a consideration of future-oriented self-representations. We offer additional recommendations for strengthening research on possible selves and self-regulation.

Self-regulation of Exercise Behavior in the TIGER Study

PubMed Central

Dishman, Rod K.; Jackson, Andrew S.; Bray, Molly S.

2014-01-01

Objective To test experiential and behavioral processes of change as mediators of the prediction of exercise behavior by two self-regulation traits, self-efficacy and self-motivation, while controlling for exercise enjoyment. Methods Structural equation modeling was applied to questionnaire responses obtained from a diverse sample of participants. Objective measures defined adherence (928 of 1279 participants attended 80% or more of sessions) and compliance (867 of 1145 participants exercised 30 minutes or more each session at their prescribed heart rate). Results Prediction of attendance by self-efficacy (inversely) and self-motivation was direct and also indirect, mediated through positive relations with the typical use of behavioral change processes. Enjoyment and self-efficacy (inversely) predicted compliance with the exercise prescription. Conclusions The results support the usefulness of self-regulatory behavioral processes of the Transtheoretical Model for predicting exercise adherence, but not compliance, extending the supportive evidence for self-regulation beyond self-reports of physical activity used in prior observational studies. PMID:24311018

A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis

PubMed Central

Ni, Ying; Aghamirzaie, Delasa; Elmarakeby, Haitham; Collakova, Eva; Li, Song; Grene, Ruth; Heath, Lenwood S.

2016-01-01

Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and the cis elements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git. PMID:28066488

Application of the ELOHA Framework to Regulated Rivers in the Upper Tennessee River Basin: A Case Study

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

McManamay, Ryan A; Orth, Dr. Donald J; Dolloff, Dr. Charles A

2013-01-01

In order for habitat restoration in regulated rivers to be effective at large scales, broadly applicable frameworks are needed that provide measurable objectives and contexts for management. The Ecological Limits of Hydrologic Alteration (ELOHA) framework was created as a template to assess hydrologic alterations, develop relationships between altered streamflow and ecology, and establish environmental flow standards. We tested the utility of ELOHA in informing flow restoration applications for fish and riparian communities in regulated rivers in the Upper Tennessee River Basin (UTRB). We followed the steps of ELOHA to generate flow alteration-ecological response relationships and then determined whether those relationshipsmore » could predict fish and riparian responses to flow restoration in the Cheoah River, a regulated system within the UTRB. Although ELOHA provided a robust template to construct hydrologic information and predict hydrology for ungaged locations, our results do not support the assertion that over-generalized univariate relationships between flow and ecology can produce results sufficient to guide management in regulated rivers. After constructing multivariate models, we successfully developed predictive relationships between flow alterations and fish/riparian responses. In accordance with model predictions, riparian encroachment displayed consistent decreases with increases in flow magnitude in the Cheoah River; however, fish richness did not increase as predicted four years post- restoration. Our results suggest that altered temperature and substrate and the current disturbance regime may have reduced opportunities for fish species colonization. Our case study highlights the need for interdisciplinary science in defining environmental flows for regulated rivers and the need for adaptive management approaches once flows are restored.« less

Transport phenomena governing nicotine emissions from electronic cigarettes: model formulation and experimental investigation

PubMed Central

Talih, Soha; Balhas, Zainab; Salman, Rola; El-Hage, Rachel; Karaoghlanian, Nareg; El-Hellani, Ahmad; Baassiri, Mohamad; Jaroudi, Ezzat; Eissenberg, Thomas; Saliba, Najat; Shihadeh, Alan

2017-01-01

Electronic cigarettes (ECIGs) electrically heat and aerosolize a liquid containing propylene glycol (PG), vegetable glycerin (VG), flavorants, water, and nicotine. ECIG effects and proposed methods to regulate them are controversial. One regulatory focal point involves nicotine emissions. We describe a mathematical model that predicts ECIG nicotine emissions. The model computes the vaporization rate of individual species by numerically solving the unsteady species and energy conservation equations. To validate model predictions, yields of nicotine, total particulate matter, PG, and VG were measured while manipulating puff topography, electrical power, and liquid composition across 100 conditions. Nicotine flux, the rate at which nicotine is emitted per unit time, was the primary outcome. Across conditions, the measured and computed nicotine flux were highly correlated (r = 0.85, p<.0001). As predicted, device power, nicotine concentration, PG/VG ratio, and puff duration influenced nicotine flux (p<.05), while water content and puff velocity did not. Additional empirical investigation revealed that PG/VG liquids act as ideal solutions, that liquid vaporization accounts for more than 95% of ECIG aerosol mass emissions, and that as device power increases the aerosol composition shifts towards the less volatile components of the parent liquid. To the extent that ECIG regulations focus on nicotine emissions, mathematical models like this one can be used to predict ECIG nicotine emissions and to test the effects of proposed regulation of factors that influence nicotine flux. PMID:28706340

A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility.

PubMed

Devenyi, Ryan A; Ortega, Francis A; Groenendaal, Willemijn; Krogh-Madsen, Trine; Christini, David J; Sobie, Eric A

2017-04-01

Arrhythmias result from disruptions to cardiac electrical activity, although the factors that control cellular action potentials are incompletely understood. We combined mathematical modelling with experiments in heart cells from guinea pigs to determine how cellular electrical activity is regulated. A mismatch between modelling predictions and the experimental results allowed us to construct an improved, more predictive mathematical model. The balance between two particular potassium currents dictates how heart cells respond to perturbations and their susceptibility to arrhythmias. Imbalances of ionic currents can destabilize the cardiac action potential and potentially trigger lethal cardiac arrhythmias. In the present study, we combined mathematical modelling with information-rich dynamic clamp experiments to determine the regulation of action potential morphology in guinea pig ventricular myocytes. Parameter sensitivity analysis was used to predict how changes in ionic currents alter action potential duration, and these were tested experimentally using dynamic clamp, a technique that allows for multiple perturbations to be tested in each cell. Surprisingly, we found that a leading mathematical model, developed with traditional approaches, systematically underestimated experimental responses to dynamic clamp perturbations. We then re-parameterized the model using a genetic algorithm, which allowed us to estimate ionic current levels in each of the cells studied. This unbiased model adjustment consistently predicted an increase in the rapid delayed rectifier K + current and a drastic decrease in the slow delayed rectifier K + current, and this prediction was validated experimentally. Subsequent simulations with the adjusted model generated the clinically relevant prediction that the slow delayed rectifier is better able to stabilize the action potential and suppress pro-arrhythmic events than the rapid delayed rectifier. In summary, iterative coupling of simulations and experiments enabled novel insight into how the balance between cardiac K + currents influences ventricular arrhythmia susceptibility. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

PubMed Central

Todd, Robert G.; van der Zee, Lucas

2016-01-01

Abstract The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network. PMID:27993914

Autonomous Motivation Predicts 7-Day Physical Activity in Hong Kong Students.

PubMed

Ha, Amy S; Ng, Johan Y Y

2015-07-01

Autonomous motivation predicts positive health behaviors such as physical activity. However, few studies have examined the relation between motivational regulations and objectively measured physical activity and sedentary behaviors. Thus, we investigated whether different motivational regulations (autonomous motivation, controlled motivation, and amotivation) predicted 7-day physical activity, sedentary behaviors, and health-related quality of life (HRQoL) of students. A total of 115 students (mean age = 11.6 years, 55.7% female) self-reported their motivational regulations and health-related quality of life. Physical activity and sedentary behaviors were measured using accelerometers for seven days. Using multilevel modeling, we found that autonomous motivation predicted higher levels of moderate-to-vigorous physical activity, less sedentary behaviors, and better HRQoL. Controlled motivation and amotivation each only negatively predicted one facet of HRQoL. Results suggested that autonomous motivation could be an important predictor of physical activity behaviors in Hong Kong students. Promotion of this form of motivational regulation may also increase HRQoL. © 2015 The International Association of Applied Psychology.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.

PubMed

Toropova, Alla P; Toropov, Andrey A

2017-06-05

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by n validation =29, r 2 validation =0.8596, RMSE validation =0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.

Can air temperature be used to project influences of climate change on stream temperature?

USGS Publications Warehouse

Arismendi, Ivan; Safeeq, Mohammad; Dunham, Jason B.; Johnson, Sherri L.

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11–44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature–stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 °C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time.

Parent Emotion Socialization Practices and Child Self-regulation as Predictors of Child Anxiety: The Mediating Role of Cardiac Variability.

PubMed

Williams, Sarah R; Woodruff-Borden, Janet

2015-08-01

The importance of the parent-child relationship in emotional development is well supported. The parental role of facilitating a child's self-regulation may provide a more focused approach for examining the role of parenting in child anxiety. The current study hypothesized that parent emotion socialization practices would predict a child's abilities in self-regulation. Given that physiological arousal has been implicated in emotional development, this was hypothesized to mediate the relationship between parental emotion socialization and child emotion regulation to predict child anxiety. Eighty-five parent and child dyads participated in the study. Parents reporting higher degrees of unsupportive emotion socialization were more likely to have children with fewer abilities in emotion regulation. Cardiac responsiveness mediated the relationship between unsupportive emotion socialization and child emotion regulation. The model of cardiac responsiveness mediating the relationship between unsupportive emotion socialization and child emotion regulation failed to reach statistical significance in predicting child anxiety symptoms.

Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance

NASA Technical Reports Server (NTRS)

Van Dongen, Hans P A.; Dinges, David F.

2003-01-01

The two-process model of sleep regulation has been applied successfully to describe, predict, and understand sleep-wake regulation in a variety of experimental protocols such as sleep deprivation and forced desynchrony. A non-linear interaction between the homeostatic and circadian processes was reported when the model was applied to describe alertness and performance data obtained during forced desynchrony. This non-linear interaction could also be due to intrinsic non-linearity in the metrics used to measure alertness and performance, however. Distinguishing these possibilities would be of theoretical interest, but could also have important implications for the design and interpretation of experiments placing sleep at different circadian phases or varying the duration of sleep and/or wakefulness. Although to date no resolution to this controversy has been found, here we show that the issue can be addressed with existing data sets. The interaction between the homeostatic and circadian processes of sleep-wake regulation was investigated using neurobehavioural performance data from a laboratory experiment involving total sleep deprivation. The results provided evidence of an actual non-linear interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance.

Enzyme clustering accelerates processing of intermediates through metabolic channeling

PubMed Central

Castellana, Michele; Wilson, Maxwell Z.; Xu, Yifan; Joshi, Preeti; Cristea, Ileana M.; Rabinowitz, Joshua D.; Gitai, Zemer; Wingreen, Ned S.

2015-01-01

We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation. PMID:25262299

Theoretical kinetic studies of models for binding myosin subfragment-1 to regulated actin: Hill model versus Geeves model.

PubMed Central

Chen , Y; Yan, B; Chalovich, J M; Brenner, B

2001-01-01

It was previously shown that a one-dimensional Ising model could successfully simulate the equilibrium binding of myosin S1 to regulated actin filaments (T. L. Hill, E. Eisenberg and L. Greene, Proc. Natl. Acad. Sci. U.S.A. 77:3186-3190, 1980). However, the time course of myosin S1 binding to regulated actin was thought to be incompatible with this model, and a three-state model was subsequently developed (D. F. McKillop and M. A. Geeves, Biophys. J. 65:693-701, 1993). A quantitative analysis of the predicted time course of myosin S1 binding to regulated actin, however, was never done for either model. Here we present the procedure for the theoretical evaluation of the time course of myosin S1 binding for both models and then show that 1) the Hill model can predict the "lag" in the binding of myosin S1 to regulated actin that is observed in the absence of Ca++ when S1 is in excess of actin, and 2) both models generate very similar families of binding curves when [S1]/[actin] is varied. This result shows that, just based on the equilibrium and pre-steady-state kinetic binding data alone, it is not possible to differentiate between the two models. Thus, the model of Hill et al. cannot be ruled out on the basis of existing pre-steady-state and equilibrium binding data. Physical mechanisms underlying the generation of the lag in the Hill model are discussed. PMID:11325734

The Effects of Psychotherapy Treatment on Outcome in Bulimia Nervosa: Examining Indirect Effects through Emotion Regulation, Self-Directed Behavior, and Self-Discrepancy within the Mediation Model

PubMed Central

Peterson, Carol B.; Berg, Kelly C.; Crosby, Ross D.; Lavender, Jason M.; Accurso, Erin C.; Ciao, Anna C.; Smith, Tracey L.; Klein, Marjorie; Mitchell, James E.; Crow, Scott J.; Wonderlich, Stephen A.

2017-01-01

Objective The purpose of this investigation was to examine the indirect effects of Integrative Cognitive-Affective Therapy (ICAT-BN) and Cognitive-Behavioral Therapy-Enhanced (CBT-E) on bulimia nervosa (BN) treatment outcome through three hypothesized maintenance variables: emotion regulation, self-directed behavior, and self-discrepancy. Method Eighty adults with BN were randomized to 21 sessions of ICAT-BN or CBT-E. A regression-based bootstrapping approach was used to test the indirect effects of treatment on outcome at end of treatment through emotion regulation and self-directed behavior measured at mid-treatment, as well as the indirect effects of treatment at follow-up through emotion regulation, self-directed behavior, and self-discrepancy measured at end of treatment. Results No significant differences in outcome between treatment conditions were observed, and no significant direct or indirect effects were found. Examination of the individual paths within the indirect effects models revealed comparable treatment effects. Across treatments, improvements in emotion regulation and self-directed behavior between baseline and mid-treatment predicted improvements in global eating disorder scores but not binge eating and purging frequency at end of treatment. Baseline to end of treatment improvements in emotion regulation and self-directed behavior also predicted improvements in global eating disorder scores at follow-up. Baseline to end of treatment improvements in emotion regulation predicted improvements in binge eating and baseline to end of treatment increases in positive self-directed behavior predicted improvements in purging at follow-up. Discussion These findings suggest that emotion regulation and self-directed behavior are important treatment targets and that ICAT-BN and CBT-E are comparable in modifying these psychological processes among individuals with BN. PMID:28117906

The effects of psychotherapy treatment on outcome in bulimia nervosa: Examining indirect effects through emotion regulation, self-directed behavior, and self-discrepancy within the mediation model.

PubMed

Peterson, Carol B; Berg, Kelly C; Crosby, Ross D; Lavender, Jason M; Accurso, Erin C; Ciao, Anna C; Smith, Tracey L; Klein, Marjorie; Mitchell, James E; Crow, Scott J; Wonderlich, Stephen A

2017-06-01

The purpose of this investigation was to examine the indirect effects of Integrative Cognitive-Affective Therapy (ICAT-BN) and Cognitive-Behavioral Therapy-Enhanced (CBT-E) on bulimia nervosa (BN) treatment outcome through three hypothesized maintenance variables: emotion regulation, self-directed behavior, and self-discrepancy. Eighty adults with BN were randomized to 21 sessions of ICAT-BN or CBT-E. A regression-based bootstrapping approach was used to test the indirect effects of treatment on outcome at end of treatment through emotion regulation and self-directed behavior measured at mid-treatment, as well as the indirect effects of treatment at follow-up through emotion regulation, self-directed behavior, and self-discrepancy measured at end of treatment. No significant differences in outcome between treatment conditions were observed, and no significant direct or indirect effects were found. Examination of the individual paths within the indirect effects models revealed comparable treatment effects. Across treatments, improvements in emotion regulation and self-directed behavior between baseline and mid-treatment predicted improvements in global eating disorder scores but not binge eating and purging frequency at end of treatment. Baseline to end of treatment improvements in emotion regulation and self-directed behavior also predicted improvements in global eating disorder scores at follow-up. Baseline to end of treatment improvements in emotion regulation predicted improvements in binge eating and baseline to end of treatment increases in positive self-directed behavior predicted improvements in purging at follow-up. These findings suggest that emotion regulation and self-directed behavior are important treatment targets and that ICAT-BN and CBT-E are comparable in modifying these psychological processes among individuals with BN. © 2017 Wiley Periodicals, Inc.

A Complete Procedure for Predicting and Improving the Performance of HAWT's

NASA Astrophysics Data System (ADS)

Al-Abadi, Ali; Ertunç, Özgür; Sittig, Florian; Delgado, Antonio

2014-06-01

A complete procedure for predicting and improving the performance of the horizontal axis wind turbine (HAWT) has been developed. The first process is predicting the power extracted by the turbine and the derived rotor torque, which should be identical to that of the drive unit. The BEM method and a developed post-stall treatment for resolving stall-regulated HAWT is incorporated in the prediction. For that, a modified stall-regulated prediction model, which can predict the HAWT performance over the operating range of oncoming wind velocity, is derived from existing models. The model involves radius and chord, which has made it more general in applications for predicting the performance of different scales and rotor shapes of HAWTs. The second process is modifying the rotor shape by an optimization process, which can be applied to any existing HAWT, to improve its performance. A gradient- based optimization is used for adjusting the chord and twist angle distribution of the rotor blade to increase the extraction of the power while keeping the drive torque constant, thus the same drive unit can be kept. The final process is testing the modified turbine to predict its enhanced performance. The procedure is applied to NREL phase-VI 10kW as a baseline turbine. The study has proven the applicability of the developed model in predicting the performance of the baseline as well as the optimized turbine. In addition, the optimization method has shown that the power coefficient can be increased while keeping same design rotational speed.

Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models

ERIC Educational Resources Information Center

Sabourin, Jennifer L.; Mott, Bradford W.; Lester, James C.

2012-01-01

Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing interest. Unfortunately, monitoring these behaviors in real-time has proven…

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

PubMed Central

Erickson, David W; Schink, Severin J.; Patsalo, Vadim; Williamson, James R.; Gerland, Ulrich; Hwa, Terence

2018-01-01

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms1–3 and they remain a topic of active investigation4–11. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations12–17, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models15,18–20 from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions. PMID:29072300

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.

Theory of Self- vs. Externally-Regulated LearningTM: Fundamentals, Evidence, and Applicability.

PubMed

de la Fuente-Arias, Jesús

2017-01-01

The Theory of Self- vs. Externally-Regulated Learning TM has integrated the variables of SRL theory, the DEDEPRO model, and the 3P model. This new Theory has proposed: (a) in general, the importance of the cyclical model of individual self-regulation (SR) and of external regulation stemming from the context (ER), as two different and complementary variables, both in combination and in interaction; (b) specifically, in the teaching-learning context, the relevance of different types of combinations between levels of self-regulation (SR) and of external regulation (ER) in the prediction of self-regulated learning (SRL), and of cognitive-emotional achievement. This review analyzes the assumptions, conceptual elements, empirical evidence, benefits and limitations of SRL vs. ERL Theory . Finally, professional fields of application and future lines of research are suggested.

The polyadenylation code: a unified model for the regulation of mRNA alternative polyadenylation*

PubMed Central

Davis, Ryan; Shi, Yongsheng

2014-01-01

The majority of eukaryotic genes produce multiple mRNA isoforms with distinct 3′ ends through a process called mRNA alternative polyadenylation (APA). Recent studies have demonstrated that APA is dynamically regulated during development and in response to environmental stimuli. A number of mechanisms have been described for APA regulation. In this review, we attempt to integrate all the known mechanisms into a unified model. This model not only explains most of previous results, but also provides testable predictions that will improve our understanding of the mechanistic details of APA regulation. Finally, we briefly discuss the known and putative functions of APA regulation. PMID:24793760

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.

Shewregdb: Database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1

PubMed Central

Syed, Mustafa H; Karpinets, Tatiana V; Leuze, Michael R; Kora, Guruprasad H; Romine, Margaret R; Uberbacher, Edward C

2009-01-01

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression, and, a visualization environment that enables integration of these data types. The regulatory information in the database includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators. Availability http://shewanella-knowledgebase.org:8080/Shewanella/gbrowserLanding.jsp PMID:20198195

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

DOE PAGES

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika; ...

2016-01-19

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

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

Larsen, Peter E.; Sreedasyam, Avinash; Trivedi, Geetika

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensormore » systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.« less

Understanding entrepreneurial intent in late adolescence: the role of intentional self-regulation and innovation.

PubMed

Geldhof, G John; Weiner, Michelle; Agans, Jennifer P; Mueller, Megan K; Lerner, Richard M

2014-01-01

Entrepreneurship represents a form of adaptive developmental regulation through which both entrepreneurs and their ecologies benefit. We describe entrepreneurship from the perspective of relational developmental systems theory, and examine the joint role of personal attributes, contextual attributes, and characteristics of person-context relationships in predicting entrepreneurial intent in a sample 3,461 college students enrolled in colleges and universities in the United States (60 % female; 61 % European American). Specifically, we tested whether personal characteristics (i.e., gender, intentional self-regulation skills, innovation orientation) and contextual factors (i.e., entrepreneurial parents) predicted college students' intentions to pursue an entrepreneurial career. Our findings suggest that self-regulation, innovation orientation, and having entrepreneurial role models (i.e., parents) predict entrepreneurial intent. Limitations and future directions for the study of youth entrepreneurship are discussed.

Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

PubMed

Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

2017-04-01

The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Comparison of the theoretical and real-world evolutionary potential of a genetic circuit

NASA Astrophysics Data System (ADS)

Razo-Mejia, M.; Boedicker, J. Q.; Jones, D.; DeLuna, A.; Kinney, J. B.; Phillips, R.

2014-04-01

With the development of next-generation sequencing technologies, many large scale experimental efforts aim to map genotypic variability among individuals. This natural variability in populations fuels many fundamental biological processes, ranging from evolutionary adaptation and speciation to the spread of genetic diseases and drug resistance. An interesting and important component of this variability is present within the regulatory regions of genes. As these regions evolve, accumulated mutations lead to modulation of gene expression, which may have consequences for the phenotype. A simple model system where the link between genetic variability, gene regulation and function can be studied in detail is missing. In this article we develop a model to explore how the sequence of the wild-type lac promoter dictates the fold-change in gene expression. The model combines single-base pair resolution maps of transcription factor and RNA polymerase binding energies with a comprehensive thermodynamic model of gene regulation. The model was validated by predicting and then measuring the variability of lac operon regulation in a collection of natural isolates. We then implement the model to analyze the sensitivity of the promoter sequence to the regulatory output, and predict the potential for regulation to evolve due to point mutations in the promoter region.

Biomedical systems analysis program

NASA Technical Reports Server (NTRS)

1979-01-01

Biomedical monitoring programs which were developed to provide a system analysis context for a unified hypothesis for adaptation to space flight are presented and discussed. A real-time system of data analysis and decision making to assure the greatest possible crew safety and mission success is described. Information about man's abilities, limitations, and characteristic reactions to weightless space flight was analyzed and simulation models were developed. The predictive capabilities of simulation models for fluid-electrolyte regulation, erythropoiesis regulation, and calcium regulation are discussed.

A Computational Model Predicting Disruption of Blood Vessel Development

EPA Science Inventory

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a varie...

Resilience of honeybee colonies via common stomach: A model of self-regulation of foraging

PubMed Central

Schmickl, Thomas

2017-01-01

We propose a new regulation mechanism based on the idea of the “common stomach” to explain several aspects of the resilience and homeostatic regulation of honeybee colonies. This mechanism exploits shared pools of substances (pollen, nectar, workers, brood) that modulate recruitment, abandonment and allocation patterns at the colony-level and enable bees to perform several survival strategies to cope with difficult circumstances: Lack of proteins leads to reduced feeding of young brood, to early capping of old brood and to regaining of already spent proteins through brood cannibalism. We modeled this system by linear interaction terms and mass-action law. To test the predictive power of the model of this regulatory mechanism we compared our model predictions to experimental data of several studies. These comparisons show that the proposed regulation mechanism can explain a variety of colony level behaviors. Detailed analysis of the model revealed that these mechanisms could explain the resilience, stability and self-regulation observed in honeybee colonies. We found that manipulation of material flow and applying sudden perturbations to colony stocks are quickly compensated by a resulting counter-acting shift in task selection. Selective analysis of feedback loops allowed us to discriminate the importance of different feedback loops in self-regulation of honeybee colonies. We stress that a network of simple proximate mechanisms can explain significant colony-level abilities that can also be seen as ultimate reasoning of the evolutionary trajectory of honeybees. PMID:29161278

The relations between interpersonal self-support traits and emotion regulation strategies: a longitudinal study.

PubMed

Xia, Ling-Xiang; Gao, Xin; Wang, Qian; Hollon, Steven D

2014-08-01

Although several cross-sectional surveys have shown that certain traits such as extraversion and neuroticism are related to emotion regulation, few studies have explored the nature of this relationship. The present study tried to explore the longitudinal relation between traits and emotion regulation strategies. The Interpersonal Self-Support Scale for Middle School Students (ISSS-MSS) and the Emotion Regulation Questionnaire (ERQ) were administrated to 374 middle school students two times across a 6-month interval. A path analysis via structural equation modeling of the five interpersonal self-support traits and the two emotion regulation strategies was tested. The results showed that interpersonal independence predicted expressive suppression and cognitive reappraisal, and that interpersonal initiative also predicted reappraisal, while reappraisal predicted interpersonal flexibility and interpersonal openness 6 month later. These results support the hypotheses that some personality traits influence certain emotion regulation strategies, while other traits may be influenced by specific emotion regulation strategies. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Comparing motor-vehicle crash risk of EU and US vehicles.

PubMed

Flannagan, Carol A C; Bálint, András; Klinich, Kathleen D; Sander, Ulrich; Manary, Miriam A; Cuny, Sophie; McCarthy, Michael; Phan, Vuthy; Wallbank, Caroline; Green, Paul E; Sui, Bo; Forsman, Åsa; Fagerlind, Helen

2018-08-01

This study examined the hypotheses that passenger vehicles meeting European Union (EU) safety standards have similar crashworthiness to United States (US) -regulated vehicles in the US driving environment, and vice versa. The first step involved identifying appropriate databases of US and EU crashes that include in-depth crash information, such as estimation of crash severity using Delta-V and injury outcome based on medical records. The next step was to harmonize variable definitions and sampling criteria so that the EU data could be combined and compared to the US data using the same or equivalent parameters. Logistic regression models of the risk of a Maximum injury according to the Abbreviated Injury Scale of 3 or greater, or fatality (MAIS3+F) in EU-regulated and US-regulated vehicles were constructed. The injury risk predictions of the EU model and the US model were each applied to both the US and EU standard crash populations. Frontal, near-side, and far-side crashes were analyzed together (termed "front/side crashes") and a separate model was developed for rollover crashes. For the front/side model applied to the US standard population, the mean estimated risk for the US-vehicle model is 0.035 (sd = 0.012), and the mean estimated risk for the EU-vehicle model is 0.023 (sd = 0.016). When applied to the EU front/side population, the US model predicted a 0.065 risk (sd = 0.027), and the EU model predicted a 0.052 risk (sd = 0.025). For the rollover model applied to the US standard population, the US model predicted a risk of 0.071 (sd = 0.024), and the EU model predicted 0.128 risk (sd = 0.057). When applied to the EU rollover standard population, the US model predicted a 0.067 risk (sd = 0.024), and the EU model predicted 0.103 risk (sd = 0.040). The results based on these methods indicate that EU vehicles most likely have a lower risk of MAIS3+F injury in front/side impacts, while US vehicles most likely have a lower risk of MAIS3+F injury in llroovers. These results should be interpreted with an understanding of the uncertainty of the estimates, the study limitations, and our recommendations for further study detailed in the report. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Intraocular Pressure, Blood Pressure, and Retinal Blood Flow Autoregulation: A Mathematical Model to Clarify Their Relationship and Clinical Relevance

PubMed Central

Guidoboni, Giovanna; Harris, Alon; Cassani, Simone; Arciero, Julia; Siesky, Brent; Amireskandari, Annahita; Tobe, Leslie; Egan, Patrick; Januleviciene, Ingrida; Park, Joshua

2014-01-01

Purpose. This study investigates the relationship between intraocular pressure (IOP) and retinal hemodynamics and predicts how arterial blood pressure (BP) and blood flow autoregulation (AR) influence this relationship. Methods. A mathematical model is developed to simulate blood flow in the central retinal vessels and retinal microvasculature as current flowing through a network of resistances and capacitances. Variable resistances describe active and passive diameter changes due to AR and IOP. The model is validated by using clinically measured values of retinal blood flow and velocity. The model simulations for six theoretical patients with high, normal, and low BP (HBP-, NBP-, LBP-) and functional or absent AR (-wAR, -woAR) are compared with clinical data. Results. The model predicts that NBPwAR and HBPwAR patients can regulate retinal blood flow (RBF) as IOP varies between 15 and 23 mm Hg and between 23 and 29 mm Hg, respectively, whereas LBPwAR patients do not adequately regulate blood flow if IOP is 15 mm Hg or higher. Hemodynamic alterations would be noticeable only if IOP changes occur outside of the regulating range, which, most importantly, depend on BP. The model predictions are consistent with clinical data for IOP reduction via surgery and medications and for cases of induced IOP elevation. Conclusions. The theoretical model results suggest that the ability of IOP to induce noticeable changes in retinal hemodynamics depends on the levels of BP and AR of the individual. These predictions might help to explain the inconsistencies found in the clinical literature concerning the relationship between IOP and retinal hemodynamics. PMID:24876284

Intraocular pressure, blood pressure, and retinal blood flow autoregulation: a mathematical model to clarify their relationship and clinical relevance.

PubMed

Guidoboni, Giovanna; Harris, Alon; Cassani, Simone; Arciero, Julia; Siesky, Brent; Amireskandari, Annahita; Tobe, Leslie; Egan, Patrick; Januleviciene, Ingrida; Park, Joshua

2014-05-29

This study investigates the relationship between intraocular pressure (IOP) and retinal hemodynamics and predicts how arterial blood pressure (BP) and blood flow autoregulation (AR) influence this relationship. A mathematical model is developed to simulate blood flow in the central retinal vessels and retinal microvasculature as current flowing through a network of resistances and capacitances. Variable resistances describe active and passive diameter changes due to AR and IOP. The model is validated by using clinically measured values of retinal blood flow and velocity. The model simulations for six theoretical patients with high, normal, and low BP (HBP-, NBP-, LBP-) and functional or absent AR (-wAR, -woAR) are compared with clinical data. The model predicts that NBPwAR and HBPwAR patients can regulate retinal blood flow (RBF) as IOP varies between 15 and 23 mm Hg and between 23 and 29 mm Hg, respectively, whereas LBPwAR patients do not adequately regulate blood flow if IOP is 15 mm Hg or higher. Hemodynamic alterations would be noticeable only if IOP changes occur outside of the regulating range, which, most importantly, depend on BP. The model predictions are consistent with clinical data for IOP reduction via surgery and medications and for cases of induced IOP elevation. The theoretical model results suggest that the ability of IOP to induce noticeable changes in retinal hemodynamics depends on the levels of BP and AR of the individual. These predictions might help to explain the inconsistencies found in the clinical literature concerning the relationship between IOP and retinal hemodynamics. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

A quantitative validated model reveals two phases of transcriptional regulation for the gap gene giant in Drosophila.

PubMed

Hoermann, Astrid; Cicin-Sain, Damjan; Jaeger, Johannes

2016-03-15

Understanding eukaryotic transcriptional regulation and its role in development and pattern formation is one of the big challenges in biology today. Most attempts at tackling this problem either focus on the molecular details of transcription factor binding, or aim at genome-wide prediction of expression patterns from sequence through bioinformatics and mathematical modelling. Here we bridge the gap between these two complementary approaches by providing an integrative model of cis-regulatory elements governing the expression of the gap gene giant (gt) in the blastoderm embryo of Drosophila melanogaster. We use a reverse-engineering method, where mathematical models are fit to quantitative spatio-temporal reporter gene expression data to infer the regulatory mechanisms underlying gt expression in its anterior and posterior domains. These models are validated through prediction of gene expression in mutant backgrounds. A detailed analysis of our data and models reveals that gt is regulated by domain-specific CREs at early stages, while a late element drives expression in both the anterior and the posterior domains. Initial gt expression depends exclusively on inputs from maternal factors. Later, gap gene cross-repression and gt auto-activation become increasingly important. We show that auto-regulation creates a positive feedback, which mediates the transition from early to late stages of regulation. We confirm the existence and role of gt auto-activation through targeted mutagenesis of Gt transcription factor binding sites. In summary, our analysis provides a comprehensive picture of spatio-temporal gene regulation by different interacting enhancer elements for an important developmental regulator. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Theory of Self- vs. Externally-Regulated LearningTM: Fundamentals, Evidence, and Applicability

PubMed Central

de la Fuente-Arias, Jesús

2017-01-01

The Theory of Self- vs. Externally-Regulated LearningTM has integrated the variables of SRL theory, the DEDEPRO model, and the 3P model. This new Theory has proposed: (a) in general, the importance of the cyclical model of individual self-regulation (SR) and of external regulation stemming from the context (ER), as two different and complementary variables, both in combination and in interaction; (b) specifically, in the teaching-learning context, the relevance of different types of combinations between levels of self-regulation (SR) and of external regulation (ER) in the prediction of self-regulated learning (SRL), and of cognitive-emotional achievement. This review analyzes the assumptions, conceptual elements, empirical evidence, benefits and limitations of SRL vs. ERL Theory. Finally, professional fields of application and future lines of research are suggested. PMID:29033872

Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms

PubMed Central

Bilsland, Alan E.; Stevenson, Katrina; Liu, Yu; Hoare, Stacey; Cairney, Claire J.; Roffey, Jon; Keith, W. Nicol

2014-01-01

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability. PMID:24550717

Information theory and the neuropeptidergic regulation of seasonal reproduction in mammals and birds

PubMed Central

Stevenson, Tyler J.; Ball, Gregory F.

2011-01-01

Seasonal breeding in the temperate zone is a dramatic example of a naturally occurring change in physiology and behaviour. Cues that predict periods of environmental amelioration favourable for breeding must be processed by the brain so that the appropriate responses in reproductive physiology can be implemented. The neural integration of several environmental cues converges on discrete hypothalamic neurons in order to regulate reproductive physiology. Gonadotrophin-releasing hormone-1 (GnRH1) and Kisspeptin (Kiss1) neurons in avian and mammalian species, respectively, show marked variation in expression that is positively associated with breeding state. We applied the constancy/contingency model of predictability to investigate how GnRH1 and Kiss1 integrate different environmental cues to regulate reproduction. We show that variation in GnRH1 from a highly seasonal avian species exhibits a predictive change that is primarily based on contingency information. Opportunistic species have low measures of predictability and exhibit a greater contribution of constancy information that is sex-dependent. In hamsters, Kiss1 exhibited a predictive change in expression that was predominantly contingency information and is anatomically localized. The model applied here provides a framework for studies geared towards determining the impact of variation in climate patterns to reproductive success in vertebrate species. PMID:21208957

Developmental model of static allometry in holometabolous insects.

PubMed

Shingleton, Alexander W; Mirth, Christen K; Bates, Peter W

2008-08-22

The regulation of static allometry is a fundamental developmental process, yet little is understood of the mechanisms that ensure organs scale correctly across a range of body sizes. Recent studies have revealed the physiological and genetic mechanisms that control nutritional variation in the final body and organ size in holometabolous insects. The implications these mechanisms have for the regulation of static allometry is, however, unknown. Here, we formulate a mathematical description of the nutritional control of body and organ size in Drosophila melanogaster and use it to explore how the developmental regulators of size influence static allometry. The model suggests that the slope of nutritional static allometries, the 'allometric coefficient', is controlled by the relative sensitivity of an organ's growth rate to changes in nutrition, and the relative duration of development when nutrition affects an organ's final size. The model also predicts that, in order to maintain correct scaling, sensitivity to changes in nutrition varies among organs, and within organs through time. We present experimental data that support these predictions. By revealing how specific physiological and genetic regulators of size influence allometry, the model serves to identify developmental processes upon which evolution may act to alter scaling relationships.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Combining Early Coagulation and Inflammatory Status Improves Prediction of Mortality in Burned and Nonburned Trauma Patients

DTIC Science & Technology

2008-02-01

clinician to distinguish between the effects of treatment and the effects of disease. Several different prediction models for multiple or- gan failure...treat- ment protocols and allow a clinician to distinguish the effect of treatment from effect of disease. In this study, our model predicted in...TNF produces a decrease in protein C activation by down regulating the expression of endothelial cell protein C receptor and thrombomodulin, both of

Development of Multi-Layered Floating Floor for Cabin Noise Reduction

NASA Astrophysics Data System (ADS)

Song, Jee-Hun; Hong, Suk-Yoon; Kwon, Hyun-Wung

2017-12-01

Recently, regulations pertaining to the noise and vibration environment of ship cabins have been strengthened. In this paper, a numerical model is developed for multi-layered floating floor to predict the structure-borne noise in ship cabins. The theoretical model consists of multi-panel structures lined with high-density mineral wool. The predicted results for structure-borne noise when multi-layered floating floor is used are compared to the measure-ments made of a mock-up. A comparison of the predicted results and the experimental one shows that the developed model could be an effective tool for predicting structure-borne noise in ship cabins.

Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

PubMed

Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

2013-10-01

Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

Evidence of Microbial Regulation of Biogeochemical Cycles from a Study on Methane Flux and Land Use Change

PubMed Central

Nazaries, Loïc; Pan, Yao; Bodrossy, Levente; Baggs, Elizabeth M.; Millard, Peter; Murrell, J. Colin

2013-01-01

Microbes play an essential role in ecosystem functions, including carrying out biogeochemical cycles, but are currently considered a black box in predictive models and all global biodiversity debates. This is due to (i) perceived temporal and spatial variations in microbial communities and (ii) lack of ecological theory explaining how microbes regulate ecosystem functions. Providing evidence of the microbial regulation of biogeochemical cycles is key for predicting ecosystem functions, including greenhouse gas fluxes, under current and future climate scenarios. Using functional measures, stable-isotope probing, and molecular methods, we show that microbial (community diversity and function) response to land use change is stable over time. We investigated the change in net methane flux and associated microbial communities due to afforestation of bog, grassland, and moorland. Afforestation resulted in the stable and consistent enhancement in sink of atmospheric methane at all sites. This change in function was linked to a niche-specific separation of microbial communities (methanotrophs). The results suggest that ecological theories developed for macroecology may explain the microbial regulation of the methane cycle. Our findings provide support for the explicit consideration of microbial data in ecosystem/climate models to improve predictions of biogeochemical cycles. PMID:23624469

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

An ensemble model of QSAR tools for regulatory risk assessment.

PubMed

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

An ensemble model of QSAR tools for regulatory risk assessment

DOE PAGES

Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

2016-09-22

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.« less

Regulating sadness and fear from outside and within: mothers' emotion socialization and adolescents' parasympathetic regulation predict the development of internalizing difficulties.

PubMed

Hastings, Paul D; Klimes-Dougan, Bonnie; Kendziora, Kimberly T; Brand, Ann; Zahn-Waxler, Carolyn

2014-11-01

Multilevel models of developmental psychopathology implicate both characteristics of the individual and their rearing environment in the etiology of internalizing problems and disorders. Maladaptive regulation of fear and sadness, the core of anxiety and depression, arises from the conjoint influences of ineffective parasympathetic regulation of emotion and ineffective emotion socialization experiences. In 171 youths (84 female, M = 13.69 years, SD = 1.84), we measured changes of respiratory sinus arrhythmia (RSA) in response to sadness- and fear-inducing film clips and maternal supportive and punitive responses to youths' internalizing emotions. Youths and mothers reported on youths' internalizing problems and anxiety and depression symptoms concurrently and 2 years later at Time 2. Maternal supportive emotion socialization predicted fewer, and punitive socialization predicted more, mother-reported internalizing problems at Time 2 only for youths who showed RSA suppression to fear-inducing films. More RSA suppression to sadness-inducing films predicted more youth-reported internalizing problems at Time 2 in girls only. In addition, less supportive emotion socialization predicted more youth-reported depression symptoms at Time 2 only for girls who showed more RSA suppression to sadness. RSA suppression to sadness versus fear might reflect different patterns of atypical parasympathetic regulation of emotional arousal, both of which increase the risk for internalizing difficulties in youths, and especially girls, who lack maternal support for regulating emotions.

A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen

PubMed Central

Yoon, Sung Ho; Turkarslan, Serdar; Reiss, David J.; Pan, Min; Burn, June A.; Costa, Kyle C.; Lie, Thomas J.; Slagel, Joseph; Moritz, Robert L.; Hackett, Murray; Leigh, John A.; Baliga, Nitin S.

2013-01-01

Methanogens catalyze the critical methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter. Here, we present the first predictive model of global gene regulation of methanogenesis in a hydrogenotrophic methanogen, Methanococcus maripaludis. We generated a comprehensive list of genes (protein-coding and noncoding) for M. maripaludis through integrated analysis of the transcriptome structure and a newly constructed Peptide Atlas. The environment and gene-regulatory influence network (EGRIN) model of the strain was constructed from a compendium of transcriptome data that was collected over 58 different steady-state and time-course experiments that were performed in chemostats or batch cultures under a spectrum of environmental perturbations that modulated methanogenesis. Analyses of the EGRIN model have revealed novel components of methanogenesis that included at least three additional protein-coding genes of previously unknown function as well as one noncoding RNA. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to intercoordinate key steps of methanogenesis with different processes such as motility, ATP biosynthesis, and carbon assimilation. Through a combination of genetic and environmental perturbation experiments we have validated the EGRIN-predicted role of two novel transcription factors in the regulation of phosphate-dependent repression of formate dehydrogenase—a key enzyme in the methanogenesis pathway. The EGRIN model demonstrates regulatory affiliations within methanogenesis as well as between methanogenesis and other cellular functions. PMID:24089473

Emotion dysregulation and peer drinking norms uniquely predict alcohol-related problems via motives.

PubMed

Simons, Raluca M; Hahn, Austin M; Simons, Jeffrey S; Murase, Hanako

2017-08-01

This study examined the relationships between emotion dysregulation, peer drinking norms, drinking motives, and alcohol-related outcomes among 435 college students. We examined the mediating roles of drinking motives when predicting alcohol consumption and related problems from the subscales of the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer, 2004) via negative and positive reinforcement models. First, we hypothesized that individuals who lack in emotion regulation strategies or have difficulties in accepting negative emotions are more likely to drink to cope. Additionally, we hypothesized that individuals who act impulsively or become distracted when upset as well as those with higher peer drinking norms are more likely to drink for social and enhancement motives. The results of the path model indicated that limited access to emotion regulation strategies significantly predicted alcohol-related problems via both depression and anxiety coping motives, but did not predict alcohol consumption. Nonacceptance of emotional responses was not significantly associated with coping motives. Impulsivity had a significant direct relationship with alcohol problems. Difficulty in engaging in goal-directed behaviors predicted both enhancement and social motives, but only enhancement motives in turn predicted consumption. Norms indirectly predicted problems via enhancement motives and consumption. The results indicated that using alcohol to reduce negative or to increase positive emotions increases alcohol consumption and alcohol-related problems. Overall, results advance our understanding of the mechanisms of increased alcohol use and problems among college students. Copyright © 2017 Elsevier B.V. All rights reserved.

Parenting and Preschool Self-Regulation as Predictors of Social Emotional Competence in 1st Grade

PubMed Central

Russell, Beth S.; Lee, Jungeun Olivia; Spieker, Susan; Oxford, Monica L.

2016-01-01

The current longitudinal study used data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD) to examine a model of development that emphasizes early caregiving environments as predictors of social emotional competence (including classroom competence). This path analysis model included features of parenting, emotion regulation, preschool language skills, and attention to predict child outcomes in 1st grade. Early caregiving environments were directly predictive of peer relationship satisfaction, oppositional behavior, social skills, and classroom competence over and above significant mediated effects through preschool self regulation (language, inattention, and anger/frustration). These results suggest that the characteristics of supportive and stimulating caregiving shift in valence over time, such that qualities of the infant-child relationship that are significant in predicting early childhood outcomes are not the same as the caregiving qualities that move to the foreground in predicting primary school outcomes. Implications for school-readiness programming are discussed, including interventions in the early caregiving system to encourage sensitive and supportive parent child interactions to bolster school readiness via the development of social-emotional competence. PMID:27616805

Distal and proximal predictors of snacking at work: A daily-survey study.

PubMed

Sonnentag, Sabine; Pundt, Alexander; Venz, Laura

2017-02-01

This study aimed at examining predictors of healthy and unhealthy snacking at work. As proximal predictors we looked at food-choice motives (health motive, affect-regulation motive); as distal predictors we included organizational eating climate, emotional eating, and self-control demands at work. We collected daily survey data from 247 employees, over a period of 2 workweeks. Multilevel structural equation modeling showed that organizational eating climate predicted health as food-choice motive, whereas emotional eating and self-control demands predicted affect regulation as food-choice motive. The health motive, in turn, predicted consuming more fruits and more cereal bars and less sweet snacks; the affect-regulation motive predicted consuming more sweet snacks. Findings highlight the importance of a health-promoting eating climate within the organization and point to the potential harm of high self-control demands at work. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predictive Models of Nanotoxicity: Relationship of Physicochemical Properties to Particle Movement Through Biological Barriers

EPA Science Inventory

Understanding the linkage between the physicochemical (PC) properties of nanoparticles (NP) and their activation of biological systems is poorly understood, yet fundamental to predicting nanotoxicity, idenitifying mode of actions and developing appropriate and effective regul...

Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches

PubMed Central

Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

2016-01-01

ABSTRACT Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations. PMID:26932506

Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches.

PubMed

Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

2016-01-01

Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations.

Learning a Markov Logic network for supervised gene regulatory network inference.

PubMed

Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.

Mechano-regulation of mesenchymal stem cell differentiation and collagen organisation during skeletal tissue repair.

PubMed

Nagel, Thomas; Kelly, Daniel J

2010-06-01

A number of mechano-regulation theories have been proposed that relate the differentiation pathway of mesenchymal stem cells (MSCs) to their local biomechanical environment. During spontaneous repair processes in skeletal tissues, the organisation of the extracellular matrix is a key determinant of its mechanical fitness. In this paper, we extend the mechano-regulation theory proposed by Prendergast et al. (J Biomech 30(6):539-548, 1997) to include the role of the mechanical environment on the collagen architecture in regenerating soft tissues. A large strain anisotropic poroelastic material model is used in a simulation of tissue differentiation in a fracture subject to cyclic bending (Cullinane et al. in J Orthop Res 20(3):579-586, 2002). The model predicts non-union with cartilage and fibrous tissue formation in the defect. Predicted collagen fibre angles, as determined by the principal decomposition of strain- and stress-type tensors, are similar to the architecture seen in native articular cartilage and neoarthroses induced by bending of mid-femoral defects in rats. Both stress and strain-based remodelling stimuli successfully predicted the general patterns of collagen fibre organisation observed in vivo. This provides further evidence that collagen organisation during tissue differentiation is determined by the mechanical environment. It is envisioned that such predictive models can play a key role in optimising MSC-based skeletal repair therapies where recapitulation of the normal tissue architecture is critical to successful repair.

Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites.

PubMed

Wang, Guohua; Wang, Fang; Huang, Qian; Li, Yu; Liu, Yunlong; Wang, Yadong

2015-01-01

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5-20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

Enforcement of continuous compliance with air quality regulations

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

Harrington, C.W.

1985-01-01

The compliance of stationary air-pollution sources with air quality regulations is examined. Contrary to the predictions of economic models of enforcement, sources are generally in continuous compliance with the regulations. An alternative voluntary compliance model of enforcement is proposed, in which regulated sources are penalized not for violations, but for failing to return to compliance when a violation is discovered. It is argued that continuous compliance is a bargain struck between the source and the regulatory agency, in which the agency agrees to avoid use of penalties in return for the source's good faith attempts to maintain compliance.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium.

PubMed

Rajeev, Lara; Luning, Eric G; Dehal, Paramvir S; Price, Morgan N; Arkin, Adam P; Mukhopadhyay, Aindrila

2011-10-12

Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

NASA Technical Reports Server (NTRS)

Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M.

1989-01-01

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some sample results are compared to data obtained from testing hardware inverters.

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

NASA Technical Reports Server (NTRS)

Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M.

1989-01-01

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some examples are compared to data obtained from testing hardware inverters.

External-environmental and internal-health early life predictors of adolescent development.

PubMed

Hartman, Sarah; Li, Zhi; Nettle, Daniel; Belsky, Jay

2017-12-01

A wealth of evidence documents associations between various aspects of the rearing environment and later development. Two evolutionary-inspired models advance explanations for why and how such early experiences shape later functioning: (a) the external-prediction model, which highlights the role of the early environment (e.g., parenting) in regulating children's development, and (b) the internal-prediction model, which emphasizes internal state (i.e., health) as the critical regulator. Thus, by using data from the NICHD Study of Early Child Care and Youth Development, the current project draws from both models by investigating whether the effect of the early environment on later adolescent functioning is subject to an indirect effect by internal-health variables. Results showed a significant indirect effect of internal health on the relation between the early environment and adolescent behavior. Specifically, early environmental adversity during the first 5 years of life predicted lower quality health during childhood, which then led to problematic adolescent functioning and earlier age of menarche for girls. In addition, for girls, early adversity predicted lower quality health that forecasted earlier age of menarche leading to increased adolescent risk taking. The discussion highlights the importance of integrating both internal and external models to further understand the developmental processes that effect adolescent behavior.

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

NASA Astrophysics Data System (ADS)

Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano

2015-11-01

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

PubMed

Vivek-Ananth, R P; Samal, Areejit

2016-09-01

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Rapid improvements in emotion regulation predict intensive treatment outcome for patients with bulimia nervosa and purging disorder.

PubMed

MacDonald, Danielle E; Trottier, Kathryn; Olmsted, Marion P

2017-10-01

Rapid and substantial behavior change (RSBC) early in cognitive behavior therapy (CBT) for eating disorders is the strongest known predictor of treatment outcome. Rapid change in other clinically relevant variables may also be important. This study examined whether rapid change in emotion regulation predicted treatment outcomes, beyond the effects of RSBC. Participants were diagnosed with bulimia nervosa or purging disorder (N = 104) and completed ≥6 weeks of CBT-based intensive treatment. Hierarchical regression models were used to test whether rapid change in emotion regulation variables predicted posttreatment outcomes, defined in three ways: (a) binge/purge abstinence; (b) cognitive eating disorder psychopathology; and (c) depression symptoms. Baseline psychopathology and emotion regulation difficulties and RSBC were controlled for. After controlling for baseline variables and RSBC, rapid improvement in access to emotion regulation strategies made significant unique contributions to the prediction of posttreatment binge/purge abstinence, cognitive psychopathology of eating disorders, and depression symptoms. Individuals with eating disorders who rapidly improve their belief that they can effectively modulate negative emotions are more likely to achieve a variety of good treatment outcomes. This supports the formal inclusion of emotion regulation skills early in CBT, and encouraging patient beliefs that these strategies are helpful. © 2017 Wiley Periodicals, Inc.

In Silico Analysis of the Regulation of the Photosynthetic Electron Transport Chain in C3 Plants1[OPEN

PubMed Central

Kramer, David M.

2018-01-01

We present a new simulation model of the reactions in the photosynthetic electron transport chain of C3 species. We show that including recent insights about the regulation of the thylakoid proton motive force, ATP/NADPH balancing mechanisms (cyclic and noncyclic alternative electron transport), and regulation of Rubisco activity leads to emergent behaviors that may affect the operation and regulation of photosynthesis under different dynamic environmental conditions. The model was parameterized with experimental results in the literature, with a focus on Arabidopsis (Arabidopsis thaliana). A dataset was constructed from multiple sources, including measurements of steady-state and dynamic gas exchange, chlorophyll fluorescence, and absorbance spectroscopy under different light intensities and CO2, to test predictions of the model under different experimental conditions. Simulations suggested that there are strong interactions between cyclic and noncyclic alternative electron transport and that an excess capacity for alternative electron transport is required to ensure adequate redox state and lumen pH. Furthermore, the model predicted that, under specific conditions, reduction of ferredoxin by plastoquinol is possible after a rapid increase in light intensity. Further analysis also revealed that the relationship between ATP synthesis and proton motive force was highly regulated by the concentrations of ATP, ADP, and inorganic phosphate, and this facilitated an increase in nonphotochemical quenching and proton motive force under conditions where metabolism was limiting, such as low CO2, high light intensity, or combined high CO2 and high light intensity. The model may be used as an in silico platform for future research on the regulation of photosynthetic electron transport. PMID:28924017

Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data

USGS Publications Warehouse

Veltman, K.; Huijbregts, M.A.J.; Vijver, M.G.; Peijnenburg, W.J.G.M.; Hobbelen, P.H.F.; Koolhaas, J.E.; van Gestel, C.A.M.; van Vliet, P.C.J.; Jan, Hendriks A.

2007-01-01

The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope < 1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper. ?? 2006 Elsevier Ltd. All rights reserved.

MECHANISTIC-BASED DISINFECTION AND DISINFECTION BYPRODUCT MODELS

EPA Science Inventory

We propose developing a mechanistic-based numerical model for chlorine decay and regulated DBP (THM and HAA) formation derived from (free) chlorination; the model framework will allow future modifications for other DBPs and chloramination. Predicted chlorine residual and DBP r...

Coordinated roles of motivation and perception in the regulation of intergroup responses: frontal cortical asymmetry effects on the P2 event-related potential and behavior.

PubMed

Amodio, David M

2010-11-01

Self-regulation is believed to involve changes in motivation and perception that function to promote goal-driven behavior. However, little is known about the way these processes interact during the on-line engagement of self-regulation. The present study examined the coordination of motivation, perception, and action control in White American participants as they regulated responses on a racial stereotyping task. Electroencephalographic indices of approach motivation (left frontal cortical asymmetry) and perceptual attention to Black versus White faces (the P2 event-related potential) were assessed during task performance. Action control was modeled from task behavior using the process-dissociation procedure. A pattern of moderated mediation emerged, such that stronger left frontal activity predicted larger P2 responses to race, which in turn predicted better action control, especially for participants holding positive racial attitudes. Results supported the hypothesis that motivation tunes perception to facilitate goal-directed action. Implications for theoretical models of intergroup response regulation, the P2 component, and the relation between motivation and perception are discussed.

A reciprocal effects model of the temporal ordering of basic psychological needs and motivation.

PubMed

Martinent, Guillaume; Guillet-Descas, Emma; Moiret, Sophie

2015-04-01

Using self-determination theory as the framework, we examined the temporal ordering between satisfaction and thwarting of basic psychological needs and motivation. We accomplished this goal by using a two-wave 7-month partial least squares path modeling approach (PLS-PM) among a sample of 94 adolescent athletes (Mage = 15.96) in an intensive training setting. The PLS-PM results showed significant paths leading: (a) from T1 satisfaction of basic psychological need for competence to T2 identified regulation, (b) from T1 external regulation to T2 thwarting and satisfaction of basic psychological need for competence, and (c) from T1 amotivation to T2 satisfaction of basic psychological need for relatedness. Overall, our results suggest that the relationship between basic psychological need and motivation varied depending on the type of basic need and motivation assessed. Basic psychological need for competence predicted identified regulation over time whereas amotivation and external regulation predicted basic psychological need for relatedness or competence over time.

Impulsivity, self-regulation,and pathological video gaming among youth: testing a mediation model.

PubMed

Liau, Albert K; Neo, Eng Chuan; Gentile, Douglas A; Choo, Hyekyung; Sim, Timothy; Li, Dongdong; Khoo, Angeline

2015-03-01

Given the potential negative mental health consequences of pathological video gaming, understanding its etiology may lead to useful treatment developments. The purpose of the study was to examine the influence of impulsive and regulatory processes on pathological video gaming. Study 1 involved 2154 students from 6 primary and 4 secondary schools in Singapore. Study 2 involved 191 students from 2 secondary schools. The results of study 1 and study 2 supported the hypothesis that self-regulation is a mediator between impulsivity and pathological video gaming. Specifically, higher levels of impulsivity was related to lower levels of self-regulation, which in turn was related to higher levels of pathological video gaming. The use of impulsivity and self-regulation in predicting pathological video gaming supports the dual-system model of incorporating both impulsive and reflective systems in the prediction of self-control outcomes. The study highlights the development of self-regulatory resources as a possible avenue for future prevention and treatment research. © 2011 APJPH.

Validation Evidence of the Motivation for Teaching Scale in Secondary Education.

PubMed

Abós, Ángel; Sevil, Javier; Martín-Albo, José; Aibar, Alberto; García-González, Luis

2018-04-10

Grounded in self-determination theory, the aim of this study was to develop a scale with adequate psychometric properties to assess motivation for teaching and to explain some outcomes of secondary education teachers at work. The sample comprised 584 secondary education teachers. Analyses supported the five-factor model (intrinsic motivation, identified regulation, introjected regulation, external regulation and amotivation) and indicated the presence of a continuum of self-determination. Evidence of reliability was provided by Cronbach's alpha, composite reliability and average variance extracted. Multigroup confirmatory factor analyses supported the partial invariance (configural and metric) of the scale in different sub-samples, in terms of gender and type of school. Concurrent validity was analyzed by a structural equation modeling that explained 71% of the work dedication variance and 69% of the boredom at work variance. Work dedication was positively predicted by intrinsic motivation (ß = .56, p < .001) and external regulation (ß = .29, p < .001) and negatively predicted by introjected regulation (ß = -.22, p < .001) and amotivation (ß = -.49, p < .001). Boredom at work was negatively predicted by intrinsic motivation (ß = -.28, p < .005) and positively predicted by amotivation (ß = .68, p < .001). The Motivation for Teaching Scale in Secondary Education (Spanish acronym EME-ES, Escala de Motivación por la Enseñanza en Educación Secundaria) is discussed as a valid and reliable instrument. This is the first specific scale in the work context of secondary teachers that has integrated the five-factor structure together with their dedication and boredom at work.

Molecular dynamics simulations of the Bcl-2 protein to predict the structure of its unordered flexible loop domain.

PubMed

Raghav, Pawan Kumar; Verma, Yogesh Kumar; Gangenahalli, Gurudutta U

2012-05-01

B-cell lymphoma (Bcl-2) protein is an anti-apoptotic member of the Bcl-2 family. It is functionally demarcated into four Bcl-2 homology (BH) domains: BH1, BH2, BH3, BH4, one flexible loop domain (FLD), a transmembrane domain (TM), and an X domain. Bcl-2's BH domains have clearly been elucidated from a structural perspective, whereas the conformation of FLD has not yet been predicted, despite its important role in regulating apoptosis through its interactions with JNK-1, PKC, PP2A phosphatase, caspase 3, MAP kinase, ubiquitin, PS1, and FKBP38. Many important residues that regulate Bcl-2 anti-apoptotic activity are present in this domain, for example Asp34, Thr56, Thr69, Ser70, Thr74, and Ser87. The structural elucidation of the FLD would likely help in attempts to accurately predict the effect of mutating these residues on the overall structure of the protein and the interactions of other proteins in this domain. Therefore, we have generated an increased quality model of the Bcl-2 protein including the FLD through modeling. Further, molecular dynamics (MD) simulations were used for FLD optimization, to predict the flexibility, and to determine the stability of the folded FLD. In addition, essential dynamics (ED) was used to predict the collective motions and the essential subspace relevant to Bcl-2 protein function. The predicted average structure and ensemble of MD-simulated structures were submitted to the Protein Model Database (PMDB), and the Bcl-2 structures obtained exhibited enhanced quality. This study should help to elucidate the structural basis for Bcl-2 anti-apoptotic activity regulation through its binding to other proteins via the FLD.

HOW GALACTIC ENVIRONMENT REGULATES STAR FORMATION

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

Meidt, Sharon E.

2016-02-10

In a new simple model I reconcile two contradictory views on the factors that determine the rate at which molecular clouds form stars—internal structure versus external, environmental influences—providing a unified picture for the regulation of star formation in galaxies. In the presence of external pressure, the pressure gradient set up within a self-gravitating turbulent (isothermal) cloud leads to a non-uniform density distribution. Thus the local environment of a cloud influences its internal structure. In the simple equilibrium model, the fraction of gas at high density in the cloud interior is determined simply by the cloud surface density, which is itselfmore » inherited from the pressure in the immediate surroundings. This idea is tested using measurements of the properties of local clouds, which are found to show remarkable agreement with the simple equilibrium model. The model also naturally predicts the star formation relation observed on cloud scales and at the same time provides a mapping between this relation and the closer-to-linear molecular star formation relation measured on larger scales in galaxies. The key is that pressure regulates not only the molecular content of the ISM but also the cloud surface density. I provide a straightforward prescription for the pressure regulation of star formation that can be directly implemented in numerical models. Predictions for the dense gas fraction and star formation efficiency measured on large-scales within galaxies are also presented, establishing the basis for a new picture of star formation regulated by galactic environment.« less

Modeling regulation of cardiac KATP and L-type Ca2+ currents by ATP, ADP, and Mg2+.

PubMed

Michailova, Anushka; Saucerman, Jeffrey; Belik, Mary Ellen; McCulloch, Andrew D

2005-03-01

Changes in cytosolic free Mg(2+) and adenosine nucleotide phosphates affect cardiac excitability and contractility. To investigate how modulation by Mg(2+), ATP, and ADP of K(ATP) and L-type Ca(2+) channels influences excitation-contraction coupling, we incorporated equations for intracellular ATP and MgADP regulation of the K(ATP) current and MgATP regulation of the L-type Ca(2+) current in an ionic-metabolic model of the canine ventricular myocyte. The new model: 1), quantitatively reproduces a dose-response relationship for the effects of changes in ATP on K(ATP) current, 2), simulates effects of ADP in modulating ATP sensitivity of K(ATP) channel, 3), predicts activation of Ca(2+) current during rapid increase in MgATP, and 4), demonstrates that decreased ATP/ADP ratio with normal total Mg(2+) or increased free Mg(2+) with normal ATP and ADP activate K(ATP) current, shorten action potential, and alter ionic currents and intracellular Ca(2+) signals. The model predictions are in agreement with experimental data measured under normal and a variety of pathological conditions.

Modeling regulation of cardiac KATP and L-type Ca2+ currents by ATP, ADP, and Mg2+

NASA Technical Reports Server (NTRS)

Michailova, Anushka; Saucerman, Jeffrey; Belik, Mary Ellen; McCulloch, Andrew D.

2005-01-01

Changes in cytosolic free Mg(2+) and adenosine nucleotide phosphates affect cardiac excitability and contractility. To investigate how modulation by Mg(2+), ATP, and ADP of K(ATP) and L-type Ca(2+) channels influences excitation-contraction coupling, we incorporated equations for intracellular ATP and MgADP regulation of the K(ATP) current and MgATP regulation of the L-type Ca(2+) current in an ionic-metabolic model of the canine ventricular myocyte. The new model: 1), quantitatively reproduces a dose-response relationship for the effects of changes in ATP on K(ATP) current, 2), simulates effects of ADP in modulating ATP sensitivity of K(ATP) channel, 3), predicts activation of Ca(2+) current during rapid increase in MgATP, and 4), demonstrates that decreased ATP/ADP ratio with normal total Mg(2+) or increased free Mg(2+) with normal ATP and ADP activate K(ATP) current, shorten action potential, and alter ionic currents and intracellular Ca(2+) signals. The model predictions are in agreement with experimental data measured under normal and a variety of pathological conditions.

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PubMed Central

Kundaje, Anshul; Xin, Xiantong; Lan, Changgui; Lianoglou, Steve; Zhou, Mei; Zhang, Li; Leslie, Christina

2008-01-01

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included. PMID:19008939

Input filter compensation for switching regulators

NASA Technical Reports Server (NTRS)

Lee, F. C.

1984-01-01

Problems caused by input filter interaction and conventional input filter design techniques are discussed. The concept of feedforward control is modeled with an input filter and a buck regulator. Experimental measurement and comparison to the analytical predictions is carried out. Transient response and the use of a feedforward loop to stabilize the regulator system is described. Other possible applications for feedforward control are included.

Older adults' exercise behavior: roles of selected constructs of social-cognitive theory.

PubMed

Umstattd, M Renée; Hallam, Jeffrey

2007-04-01

Exercise is consistently related to physical and psychological health benefits in older adults. Bandura's social-cognitive theory (SCT) is one theoretical perspective on understanding and predicting exercise behavior. Thus, the authors examined whether three SCT variables-self-efficacy, self-regulation, and outcome-expectancy value-predicted older adults' (N = 98) exercise behavior. Bivariate analyses revealed that regular exercise was associated with being male, White, and married; having higher income, education, and self-efficacy; using self-regulation skills; and having favorable outcome-expectancy values (p < .05). In a simultaneous multivariate model, however, self-regulation (p = .0097) was the only variable independently associated with regular exercise. Thus, exercise interventions targeting older adults should include components aimed at increasing the use of self-regulation strategies.

A Longitudinal Study for the Empirical Validation of an Etiopathogenetic Model of Internet Addiction in Adolescence Based on Early Emotion Regulation

PubMed Central

Cimino, Silvia

2018-01-01

Several etiopathogenetic models have been conceptualized for the onset of Internet Addiction (IA). However, no study had evaluated the possible predictive effect of early emotion regulation strategies on the development of IA in adolescence. In a sample of N = 142 adolescents with Internet Addiction, this twelve-year longitudinal study aimed at verifying whether and how emotion regulation strategies (self-focused versus other-focused) at two years of age were predictive of school-age children's internalizing/externalizing symptoms, which in turn fostered Internet Addiction (compulsive use of the Web versus distressed use) in adolescence. Our results confirmed our hypotheses demonstrating that early emotion regulation has an impact on the emotional-behavioral functioning in middle childhood (8 years of age), which in turn has an influence on the onset of IA in adolescence. Moreover, our results showed a strong, direct statistical link between the characteristics of emotion regulation strategies in infancy and IA in adolescence. These results indicate that a common root of unbalanced emotion regulation could lead to two different manifestations of Internet Addiction in youths and could be useful in the assessment and treatment of adolescents with IA.

Mechanical regulation of fibroblast migration and collagen remodelling in healing myocardial infarcts

PubMed Central

Rouillard, Andrew D; Holmes, Jeffrey W

2012-01-01

Effective management of healing and remodelling after myocardial infarction is an important problem in modern cardiology practice. We have recently shown that the level of infarct anisotropy is a critical determinant of heart function following a large anterior infarction, which suggests that therapeutic gains may be realized by controlling infarct anisotropy. However, factors regulating infarct anisotropy are not well understood. Mechanical, structural and chemical guidance cues have all been shown to regulate alignment of fibroblasts and collagen in vitro, and prior studies have proposed that each of these cues could regulate anisotropy of infarct scar tissue, but understanding of fibroblast behaviour in the complex environment of a healing infarct is lacking. We developed an agent-based model of infarct healing that accounted for the combined influence of these cues on fibroblast alignment, collagen deposition and collagen remodelling. We pooled published experimental data from several sources in order to determine parameter values, then used the model to test the importance of each cue for predicting collagen alignment measurements from a set of recent cryoinfarction experiments. We found that although chemokine gradients and pre-existing matrix structures had important effects on collagen organization, a response of fibroblasts to mechanical cues was critical for correctly predicting collagen alignment in infarct scar. Many proposed therapies for myocardial infarction, such as injection of cells or polymers, alter the mechanics of the infarct region. Our modelling results suggest that such therapies could change the anisotropy of the healing infarct, which could have important functional consequences. This model is therefore a potentially important tool for predicting how such interventions change healing outcomes. PMID:22495588

Relationship between executive function, attachment style, and psychotic like experiences in typically developing youth.

PubMed

Blair, Melanie A; Nitzburg, George; DeRosse, Pamela; Karlsgodt, Katherine H

2018-03-03

Psychotic like experiences (PLE's) are common in the general population, particularly during adolescence, which has generated interest in how PLE's emerge, and the extent to which they reflect either risk for, or resilience to, psychosis. The "attachment-developmental-cognitive" (ADC) model is one effort to model the effect of risk factors on PLEs. The ADC model proposes attachment insecurity as an early environmental insult that can contribute to altered neurodevelopment, increasing the likelihood of PLE's and psychosis. In particular, early-life attachment disruptions may negatively impact numerous aspects of executive function (EF), including behavioral inhibition and emotion regulation. Yet despite the relationship of disrupted attachment to EF impairments, no studies have examined how these factors may combine to contribute to PLE's in adolescents. Here, we examined the relative contributions of daily-life EF and attachment difficulties (avoidance and anxiety) to PLEs in typically developing youth (N=52; ages 10-21). We found that EF deficits and high attachment insecurity both accounted for a significant proportion of the variance in PLE's, and interacted to predict PLE manifestation. Specifically, positive PLEs were predicted by greater trouble monitoring behavioral impact, less difficulty completing tasks, greater difficulty regulating emotional reactions, greater difficulty controlling impulses and higher attachment anxiety. Negative PLEs were predicted by greater difficulty in alternating attention, transitioning across situations, and regulating emotional reactions as well as higher attachment anxiety. These results are consistent with the ADC model, providing evidence that early-life attachment disruptions may impact behavioral regulation and emotional control, which together may contribute to PLEs. Copyright © 2018. Published by Elsevier B.V.

Maternal emotion socialization differentially predicts third-grade children's emotion regulation and lability.

PubMed

Rogers, Megan L; Halberstadt, Amy G; Castro, Vanessa L; MacCormack, Jennifer K; Garrett-Peters, Patricia

2016-03-01

Numerous parental emotion socialization factors have been implicated as direct and indirect contributors to the development of children's emotional competence. To date, however, no study has combined parents' emotion-related beliefs, behaviors, and regulation strategies in one model to assess their cumulative-as well as unique-contributions to children's emotion regulation. We considered the 2 components that have recently been distinguished: emotion regulation and emotional lability. We predicted that mothers' beliefs about the value of and contempt for children's emotions, mothers' supportive and nonsupportive reactions to their children's emotions, as well as mothers' use of cognitive reappraisal and suppression of their own emotions would each contribute unique variance to their children's emotion regulation and lability, as assessed by children's teachers. The study sample consisted of an ethnically and socioeconomically diverse group of 165 mothers and their third-grade children. Different patterns emerged for regulation and lability: Controlling for family income, child gender, and ethnicity, only mothers' lack of suppression as a regulatory strategy predicted greater emotion regulation in children, whereas mothers' valuing of children's emotions, mothers' lack of contempt for children's emotions, mothers' use of cognitive reappraisal to reinterpret events, and mothers' lack of emotional suppression predicted less lability in children. These findings support the divergence of emotion regulation and lability as constructs and indicate that, during middle childhood, children's lability may be substantially and uniquely affected by multiple forms of parental socialization. (c) 2016 APA, all rights reserved).

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

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

Larkin, Andrew; Department of Statistics, Oregon State University; Superfund Research Center, Oregon State University

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdanimore » logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions for combining PAH mixtures in agreement with microarrays ► Predictions highly dependent on aryl hydrocarbon receptor repressor expression.« less

A Model of Young Children's Social Cognition: Linkages Between Latent Structures and Discrete Processing

ERIC Educational Resources Information Center

Meece, Darrell

1999-01-01

This study proposes a model of associations between young children's social cognition and their social behavior with peers. In this model, two latent structures -children's representations of peer relationships and emotion regulation -- predict children's competent, prosocial, withdrawn, and aggressive behavior. Moreover, the model proposes that…

The role of emotional dysregulation in concurrent eating disorders and substance use disorders.

PubMed

Spence, Sarah; Courbasson, Christine

2012-12-01

This study explored the role of emotional dysregulation in 178 participants with concurrent EDs and SUDs. We ran two path analyses: Model 1 predicted negative mood regulation from alexithymia, and Model 2 predicted emotional eating from negative mood regulation. For Model 1, difficulty identifying and describing feelings was related to poor coping expectancies, while externally-oriented thinking was related to greater coping expectancies. For Model 2, poor coping expectancies in general were related to emotional eating, while greater coping expectancies in relation to behavior (i.e., the belief that some behavior or action can alleviate one's negative affect) also resulted in increased emotional eating. This finding suggests that there may be differences in the purpose of emotional eating; some people may believe that emotional eating can be used as an effective coping strategy to deal with negative affect. Copyright © 2012 Elsevier Ltd. All rights reserved.

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium

PubMed Central

2011-01-01

Background Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. Results We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. Conclusions The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms. PMID:21992415

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

Comparing models for IMF variation across cosmological time in Milky Way-like galaxies

NASA Astrophysics Data System (ADS)

Guszejnov, Dávid; Hopkins, Philip F.; Ma, Xiangcheng

2017-12-01

One of the key observations regarding the stellar initial mass function (IMF) is its near-universality in the Milky Way (MW), which provides a powerful way to constrain different star formation models that predict the IMF. However, those models are almost universally 'cloud-scale' or smaller - they take as input or simulate single molecular clouds (GMCs), clumps or cores, and predict the resulting IMF as a function of the cloud properties. Without a model for the progenitor properties of all clouds that formed the stars at different locations in the MW (including ancient stellar populations formed in high redshift, likely gas-rich dwarf progenitor galaxies that looked little like the Galaxy today), the predictions cannot be fully explored nor safely applied to 'live' cosmological calculations of the IMF in different galaxies at different cosmological times. We therefore combine a suite of high-resolution cosmological simulations (from the Feedback In Realistic Environments project), which form MW-like galaxies with reasonable star formation properties and explicitly resolve massive GMCs, with various proposed cloud-scale IMF models. We apply the models independently to every star particle formed in the simulations to synthesize the predicted IMF in the present-day galaxy. We explore models where the IMF depends on Jeans mass, sonic or 'turbulent Bonnor-Ebert' mass, fragmentation with a polytropic equation of state, or where it is self-regulated by protostellar feedback. We show that all of these models, except the feedback-regulated ones, predict far more variation (∼0.6-1 dex 1σ scatter in the IMF turnover mass) in the simulations than is observed in the MW.

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

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

PubMed

Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

2016-11-29

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

Learning a Markov Logic network for supervised gene regulatory network inference

PubMed Central

2013-01-01

Background Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. Conclusions The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge. PMID:24028533

An equilibrium-point model for fast, single-joint movement: I. Emergence of strategy-dependent EMG patterns.

PubMed

Latash, M L; Gottlieb, G L

1991-09-01

We describe a model for the regulation of fast, single-joint movements, based on the equilibrium-point hypothesis. Limb movement follows constant rate shifts of independently regulated neuromuscular variables. The independently regulated variables are tentatively identified as thresholds of a length sensitive reflex for each of the participating muscles. We use the model to predict EMG patterns associated with changes in the conditions of movement execution, specifically, changes in movement times, velocities, amplitudes, and moments of limb inertia. The approach provides a theoretical neural framework for the dual-strategy hypothesis, which considers certain movements to be results of one of two basic, speed-sensitive or speed-insensitive strategies. This model is advanced as an alternative to pattern-imposing models based on explicit regulation of timing and amplitudes of signals that are explicitly manifest in the EMG patterns.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges

PubMed Central

Amarasingham, Ruben; Audet, Anne-Marie J.; Bates, David W.; Glenn Cohen, I.; Entwistle, Martin; Escobar, G. J.; Liu, Vincent; Etheredge, Lynn; Lo, Bernard; Ohno-Machado, Lucila; Ram, Sudha; Saria, Suchi; Schilling, Lisa M.; Shahi, Anand; Stewart, Walter F.; Steyerberg, Ewout W.; Xie, Bin

2016-01-01

Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. Objectives: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. Methods: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. Findings: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and Ethics) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models. PMID:27141516

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

PubMed

Amarasingham, Ruben; Audet, Anne-Marie J; Bates, David W; Glenn Cohen, I; Entwistle, Martin; Escobar, G J; Liu, Vincent; Etheredge, Lynn; Lo, Bernard; Ohno-Machado, Lucila; Ram, Sudha; Saria, Suchi; Schilling, Lisa M; Shahi, Anand; Stewart, Walter F; Steyerberg, Ewout W; Xie, Bin

2016-01-01

The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and ETHICS) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility. Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.

Integrated network modelling for identifying microbial mechanisms of particulate organic carbon accumulation in coastal marine systems

NASA Astrophysics Data System (ADS)

McDonald, Karlie; Turk, Valentina; Mozetič, Patricija; Tinta, Tinkara; Malfatti, Francesca; Hannah, David; Krause, Stefan

2016-04-01

Accumulation of particulate organic carbon (POC) has the potential to change the structure and function of marine ecosystems. High abidance of POC can develop into aggregates, known as marine snow or mucus aggregates that can impair essential marine ecosystem functioning and services. Currently marine POC formation, accumulation and sedimentation processes are being explored as potential pathways to remove CO2 from the atmosphere by CO2 sequestration via fixation into biomass by phytoplankton. However, the current ability of scientists, environmental managers and regulators to analyse and predict high POC concentrations is restricted by the limited understanding of the dynamic nature of the microbial mechanisms regulating POC accumulation events in marine environments. We present a proof of concept study that applies a novel Bayesian Networks (BN) approach to integrate relevant biological and physical-chemical variables across spatial and temporal scales in order to identify the interactions of the main contributing microbial mechanisms regulating POC accumulation in the northern Adriatic Sea. Where previous models have characterised only the POC formed, the BN approach provides a probabilistic framework for predicting the occurrence of POC accumulation by linking biotic factors with prevailing environmental conditions. In this paper the BN was used to test three scenarios (diatom, nanoflagellate, and dinoflagellate blooms). The scenarios predicted diatom blooms to produce high chlorophyll a at the water surface while nanoflagellate blooms were predicted to occur at lower depths (> 6m) in the water column and produce lower chlorophyll a concentrations. A sensitivity analysis identified the variables with the greatest influence on POC accumulation being the enzymes protease and alkaline phosphatase, which highlights the importance of microbial community interactions. The developed proof of concept BN model allows for the first time to quantify the impacts of biological, chemical and physical parameters influencing microbial community interactions mechanisms that regulate POC accumulation in marine environments. The dynamic modular nature of the developed BN will allow successive updating and improvement of the model structure as new data are emerging, thus, providing a powerful interactive framework for the investigation, prediction and mitigation of future POC accumulation events.

Minimizing the total harmonic distortion for a 3 kW, 20 kHz ac to dc converter using SPICE

NASA Technical Reports Server (NTRS)

Lollar, Louis F.; Kapustka, Robert E.

1988-01-01

This paper describes the SPICE model of a transformer-rectified-filter (TRF) circuit and the Micro-CAP (Microcomputer Circuit Analysis Program) model and their application. The models were used to develop an actual circuit with reduced input current THD. The SPICE analysis consistently predicted the THD improvements in actual circuits as various designs were attempted. In an effort to predict and verify load regulation, the incorporation of saturable inductor models significantly improved the fidelity of the TRF circuit output voltage.

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.

Predicting short-term positive affect in individuals with social anxiety disorder: The role of selected personality traits and emotion regulation strategies.

PubMed

Weisman, Jaclyn S; Rodebaugh, Thomas L; Lim, Michelle H; Fernandez, Katya C

2015-08-01

Recently, research has provided support for a moderate, inverse relationship between social anxiety and dispositional positive affect. However, the dynamics of this relationship remain poorly understood. The present study evaluates whether certain personality traits and emotion regulation variables predict short-term positive affect for individuals with social anxiety disorder and healthy controls. Positive affect as measured by two self-report instruments was assessed before and after two tasks in which the participant conversed with either a friend or a romantic partner. Tests of models examining the hypothesized prospective predictors revealed that the paths did not differ significantly across diagnostic group and both groups showed the hypothesized patterns of endorsement for the emotion regulation variables. Further, a variable reflecting difficulty redirecting oneself when distressed prospectively predicted one measure of positive affect. Additional research is needed to explore further the role of emotion regulation strategies on positive emotions for individuals higher in social anxiety. Copyright © 2015 Elsevier Ltd. All rights reserved.

In Silico Analysis of the Regulation of the Photosynthetic Electron Transport Chain in C3 Plants.

PubMed

Morales, Alejandro; Yin, Xinyou; Harbinson, Jeremy; Driever, Steven M; Molenaar, Jaap; Kramer, David M; Struik, Paul C

2018-02-01

We present a new simulation model of the reactions in the photosynthetic electron transport chain of C3 species. We show that including recent insights about the regulation of the thylakoid proton motive force, ATP/NADPH balancing mechanisms (cyclic and noncyclic alternative electron transport), and regulation of Rubisco activity leads to emergent behaviors that may affect the operation and regulation of photosynthesis under different dynamic environmental conditions. The model was parameterized with experimental results in the literature, with a focus on Arabidopsis ( Arabidopsis thaliana ). A dataset was constructed from multiple sources, including measurements of steady-state and dynamic gas exchange, chlorophyll fluorescence, and absorbance spectroscopy under different light intensities and CO 2 , to test predictions of the model under different experimental conditions. Simulations suggested that there are strong interactions between cyclic and noncyclic alternative electron transport and that an excess capacity for alternative electron transport is required to ensure adequate redox state and lumen pH. Furthermore, the model predicted that, under specific conditions, reduction of ferredoxin by plastoquinol is possible after a rapid increase in light intensity. Further analysis also revealed that the relationship between ATP synthesis and proton motive force was highly regulated by the concentrations of ATP, ADP, and inorganic phosphate, and this facilitated an increase in nonphotochemical quenching and proton motive force under conditions where metabolism was limiting, such as low CO 2 , high light intensity, or combined high CO 2 and high light intensity. The model may be used as an in silico platform for future research on the regulation of photosynthetic electron transport. © 2018 American Society of Plant Biologists. All Rights Reserved.

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

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

Winkler, David A., E-mail: dave.winkler@csiro.au

2016-05-15

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

The development of adolescent self-regulation: reviewing the role of parent, peer, friend, and romantic relationships.

PubMed

Farley, Julee P; Kim-Spoon, Jungmeen

2014-06-01

Self-regulation plays an important role in adolescent development, predicting success in multiple domains including school and social relationships. While researchers have paid increasing attention to the influence of parents on the development of adolescent self-regulation, we know little about the influence of peers and friends and even less about the influence of romantic partners on adolescent development of self-regulation. Extant studies examined a unidirectional model of self-regulation development rather than a bidirectional model of self-regulation development. Given that relationships and self-regulation develop in tandem, a model of bidirectional development between relationship context and adolescent self-regulation may be relevant. This review summarizes extant literature and proposes that in order to understand how adolescent behavioral and emotional self-regulation develops in the context of social relationships one must consider that each relationship builds upon previous relationships and that self-regulation and relationship context develop bidirectionally. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Flux balance analysis of ammonia assimilation network in E. coli predicts preferred regulation point.

PubMed

Wang, Lu; Lai, Luhua; Ouyang, Qi; Tang, Chao

2011-01-25

Nitrogen assimilation is a critical biological process for the synthesis of biomolecules in Escherichia coli. The central ammonium assimilation network in E. coli converts carbon skeleton α-ketoglutarate and ammonium into glutamate and glutamine, which further serve as nitrogen donors for nitrogen metabolism in the cell. This reaction network involves three enzymes: glutamate dehydrogenase (GDH), glutamine synthetase (GS) and glutamate synthase (GOGAT). In minimal media, E. coli tries to maintain an optimal growth rate by regulating the activity of the enzymes to match the availability of the external ammonia. The molecular mechanism and the strategy of the regulation in this network have been the research topics for many investigators. In this paper, we develop a flux balance model for the nitrogen metabolism, taking into account of the cellular composition and biosynthetic requirements for nitrogen. The model agrees well with known experimental results. Specifically, it reproduces all the (15)N isotope labeling experiments in the wild type and the two mutant (ΔGDH and ΔGOGAT) strains of E. coli. Furthermore, the predicted catalytic activities of GDH, GS and GOGAT in different ammonium concentrations and growth rates for the wild type, ΔGDH and ΔGOGAT strains agree well with the enzyme concentrations obtained from western blots. Based on this flux balance model, we show that GS is the preferred regulation point among the three enzymes in the nitrogen assimilation network. Our analysis reveals the pattern of regulation in this central and highly regulated network, thus providing insights into the regulation strategy adopted by the bacteria. Our model and methods may also be useful in future investigations in this and other networks.

The role of oxygen as a regulator of stem cell fate during fracture repair in TSP2-null mice.

PubMed

Burke, Darren; Dishowitz, Michael; Sweetwyne, Mariya; Miedel, Emily; Hankenson, Kurt D; Kelly, Daniel J

2013-10-01

It is often difficult to decouple the relative importance of different factors in regulating MSC differentiation. Genetically modified mice provide model systems whereby some variables can be manipulated while others are kept constant. Fracture repair in thrombospondin-2 (TSP2)-null mice is characterized by reduced endochondral ossification and enhanced intramembranous bone formation. The proposed mechanism for this shift in MSC fate is that increased vascular density and hence oxygen availability in TSP2-null mice regulates differentiation. However, TSP2 is multifunctional and regulates other aspects of the regenerative cascade, such as MSC proliferation. The objective of this study is to use a previously developed computational model of tissue differentiation, in which substrate stiffness and oxygen tension regulate stem cell differentiation, to simulate potential mechanisms which may drive alterations in MSC fate in TSP2-null mice. Four models (increased cell proliferation, increased numbers of MSCs in the marrow decreased cellular oxygen consumption, and an initially stiffer callus) were not predictive of experimental observations in TSP2-null mice. In contrast, increasing the rate of angiogenic progression led to a prediction of greater intramembranous ossification, diminished endochondral ossification, and a reduced region of hypoxia in the fracture callus similar to that quantified experimentally by the immunohistochemical detection of pimonidazole adducts that develop with hypoxia. This study therefore provides further support for the hypothesis that oxygen availability during early fracture healing is a key regulator of MSC bipotential differentiation, and furthermore, it highlights the advantages of integrating computational models with genetically modified mouse studies for further elucidating mechanisms regulating stem cell fate. Copyright © 2013 Orthopaedic Research Society.

The Role of Means Efficacy When Predicting Creative Performance

ERIC Educational Resources Information Center

Simmons, Aneika L.; Payne, Stephanie C.; Pariyothorn, Matthew M.

2014-01-01

According to the "Internal-External Efficacy model", self-efficacy is an insufficient explanation for self-regulated behavior because it ignores the influence of external resources. Applying this theory of motivation to the prediction of creative performance, the extent to which means efficacy or the belief in the utility of external…

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

EPA Science Inventory

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

CALCULATION OF PHYSICOCHEMICAL PROPERTIES FOR ENVIRONMENTAL MODELING

EPA Science Inventory

Recent trends in environmental regulatory strategies dictate that EPA will rely heavily on predictive modeling to carry out the increasingly complex array of exposure and risk assessments necessary to develop scientifically defensible regulations. In response to this need, resea...

Stochastic Petri Net Modeling of Hypoxia Pathway Predicts a Novel Incoherent Feed-Forward Loop Controlling SDF-1 Expression in Acute Kidney Injury.

PubMed

Heidary, Zarifeh; Ghaisari, Jafar; Moein, Shiva; Naderi, Mahmood; Gheisari, Yousof

2016-01-01

Homing of stem cells to the sites of injury is crucial for tissue regeneration. Stromal derived factor 1 (SDF-1) is among the most important chemokines recruiting these cells. Unexpectedly, our previous experimental data on mouse models of acute kidney injury showed that SDF-1 has a declining trend following ischemic kidney insult. To describe this unforeseen observation, a stochastic Petri net model of SDF-1 regulation in the hypoxia pathway was constructed based on main related components extracted from literature. Using this strategy, predictions regarding the underlying mechanisms of SDF-1 kinetics are generated and a novel incoherent feed forward loop regulating SDF-1 expression is proposed. The computational approach suggested here can be exploited to propose novel therapies for debilitating disorders such as kidney injury.

Interaction of Reward Seeking and Self-Regulation in the Prediction of Risk Taking: A Cross-National Test of the Dual Systems Model

ERIC Educational Resources Information Center

Duell, Natasha; Steinberg, Laurence; Chein, Jason; Al-Hassan, Suha M.; Bacchini, Dario; Lei, Chang; Chaudhary, Nandita; Di Giunta, Laura; Dodge, Kenneth A.; Fanti, Kostas A.; Lansford, Jennifer E.; Malone, Patrick S.; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T.; Sorbring, Emma; Tapanya, Sombat; Uribe Tirado, Liliana Maria; Alampay, Liane Peña

2016-01-01

In the present analysis, we test the dual systems model of adolescent risk taking in a cross-national sample of over 5,200 individuals aged 10 through 30 (M = 17.05 years, SD = 5.91) from 11 countries. We examine whether reward seeking and self-regulation make independent, additive, or interactive contributions to risk taking, and ask whether…

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

Szymańska, Paulina; Martin, Katie R.; MacKeigan, Jeffrey P.

We constructed a mechanistic, computational model for regulation of (macro)autophagy and protein synthesis (at the level of translation). The model was formulated to study the system-level consequences of interactions among the following proteins: two key components of MTOR complex 1 (MTORC1), namely the protein kinase MTOR (mechanistic target of rapamycin) and the scaffold protein RPTOR; the autophagy-initiating protein kinase ULK1; and the multimeric energy-sensing AMP-activated protein kinase (AMPK). Inputs of the model include intrinsic AMPK kinase activity, which is taken as an adjustable surrogate parameter for cellular energy level or AMP:ATP ratio, and rapamycin dose, which controls MTORC1 activity. Outputsmore » of the model include the phosphorylation level of the translational repressor EIF4EBP1, a substrate of MTORC1, and the phosphorylation level of AMBRA1 (activating molecule in BECN1-regulated autophagy), a substrate of ULK1 critical for autophagosome formation. The model incorporates reciprocal regulation of mTORC1 and ULK1 by AMPK, mutual inhibition of MTORC1 and ULK1, and ULK1-mediated negative feedback regulation of AMPK. Through analysis of the model, we find that these processes may be responsible, depending on conditions, for graded responses to stress inputs, for bistable switching between autophagy and protein synthesis, or relaxation oscillations, comprising alternating periods of autophagy and protein synthesis. A sensitivity analysis indicates that the prediction of oscillatory behavior is robust to changes of the parameter values of the model. The model provides testable predictions about the behavior of the AMPK-MTORC1-ULK1 network, which plays a central role in maintaining cellular energy and nutrient homeostasis.« less

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.

PubMed

MacGilvray, Matthew E; Shishkova, Evgenia; Chasman, Deborah; Place, Michael; Gitter, Anthony; Coon, Joshua J; Gasch, Audrey P

2018-05-01

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Catastrophizing, rumination, and reappraisal prospectively predict adolescent PTSD symptom onset following a terrorist attack

PubMed Central

Jenness, Jessica L.; Jager-Hyman, Shari; Heleniak, Charlotte; Beck, Aaron T.; Sheridan, Margaret A.; McLaughlin, Katie A.

2016-01-01

Background Disruptions in emotion regulation are a transdiagnostic risk factor for psychopathology. However, scant research has examined whether emotion regulation strategies are related to the onset of posttraumatic stress disorder (PTSD) symptoms among youths exposed to trauma. We investigated whether pretrauma emotion regulation strategies prospectively predicted PTSD symptom onset after the 2013 Boston Marathon terrorist attack among adolescents and whether these associations were moderated by the degree of exposure to media coverage of the attack. Methods A sample of 78 Boston-area adolescents (mean age =16.72 years, 65% female) who previously participated in studies assessing emotion regulation and psychopathology were recruited following the terrorist attack. Within 4 weeks of the attack, we assessed self-reported PTSD symptoms and attack-related media exposure via an online survey. We examined the association of pretrauma emotion regulation strategies with PTSD symptom onset after adjustment for pretrauma internalizing symptoms and violence exposure. Results Greater pretrauma engagement in rumination predicted onset of PTSD symptoms following the attack. Adolescents who engaged in catastrophizing also had greater PTSD symptoms postattack, but only when exposed to high levels of media coverage of the attacks; the same pattern was observed for adolescents who engaged in low levels of cognitive reappraisal. Conclusions Engagement in specific emotion regulation strategies prior to a traumatic event predicts the onset of PTSD symptoms among youths exposed to trauma, extending transdiagnostic models of emotion regulation to encompass trauma-related psychopathology in children and adolescents. PMID:27557454

Modelling climate change effects on Atlantic salmon: Implications for mitigation in regulated rivers.

PubMed

Sundt-Hansen, L E; Hedger, R D; Ugedal, O; Diserud, O H; Finstad, A G; Sauterleute, J F; Tøfte, L; Alfredsen, K; Forseth, T

2018-08-01

Climate change is expected to alter future temperature and discharge regimes of rivers. These regimes have a strong influence on the life history of most aquatic river species, and are key variables controlling the growth and survival of Atlantic salmon. This study explores how the future abundance of Atlantic salmon may be influenced by climate-induced changes in water temperature and discharge in a regulated river, and investigates how negative impacts in the future can be mitigated by applying different regulated discharge regimes during critical periods for salmon survival. A spatially explicit individual-based model was used to predict juvenile Atlantic salmon population abundance in a regulated river under a range of future water temperature and discharge scenarios (derived from climate data predicted by the Hadley Centre's Global Climate Model (GCM) HadAm3H and the Max Plank Institute's GCM ECHAM4), which were then compared with populations predicted under control scenarios representing past conditions. Parr abundance decreased in all future scenarios compared to the control scenarios due to reduced wetted areas (with the effect depending on climate scenario, GCM, and GCM spatial domain). To examine the potential for mitigation of climate change-induced reductions in wetted area, simulations were run with specific minimum discharge regimes. An increase in abundance of both parr and smolt occurred with an increase in the limit of minimum permitted discharge for three of the four GCM/GCM spatial domains examined. This study shows that, in regulated rivers with upstream storage capacity, negative effects of climate change on Atlantic salmon populations can potentially be mitigated by release of water from reservoirs during critical periods for juvenile salmon. Copyright © 2018. Published by Elsevier B.V.

PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling

PubMed Central

Yang, Jason H.; Polanowska-Grabowska, Renata K.; Smith, Jeffrey S.; Shields, Charles W.; Saucerman, Jeffrey J.

2014-01-01

β-adrenergic signaling is spatiotemporally heterogeneous in the cardiac myocyte, conferring exquisite control to sympathetic stimulation. Such heterogeneity drives the formation of protein kinase A (PKA) signaling microdomains, which regulate Ca2+ handling and contractility. Here, we test the hypothesis that the nucleus independently comprises a PKA signaling microdomain regulating myocyte hypertrophy. Spatially-targeted FRET reporters for PKA activity identified slower PKA activation and lower isoproterenol sensitivity in the nucleus (t50 = 10.60±0.68 min; EC50 = 89.00 nmol/L) than in the cytosol (t50 = 3.71±0.25 min; EC50 = 1.22 nmol/L). These differences were not explained by cAMP or AKAP-based compartmentation. A computational model of cytosolic and nuclear PKA activity was developed and predicted that differences in nuclear PKA dynamics and magnitude are regulated by slow PKA catalytic subunit diffusion, while differences in isoproterenol sensitivity are regulated by nuclear expression of protein kinase inhibitor (PKI). These were validated by FRET and immunofluorescence. The model also predicted differential phosphorylation of PKA substrates regulating cell contractility and hypertrophy. Ca2+ and cell hypertrophy measurements validated these predictions and identified higher isoproterenol sensitivity for contractile enhancements (EC50 = 1.84 nmol/L) over cell hypertrophy (EC50 = 85.88 nmol/L). Over-expression of spatially targeted PKA catalytic subunit to the cytosol or nucleus enhanced contractile and hypertrophic responses, respectively. We conclude that restricted PKA catalytic subunit diffusion is an important PKA compartmentation mechanism and the nucleus comprises a novel PKA signaling microdomain, insulating hypertrophic from contractile β-adrenergic signaling responses. PMID:24225179

Tuning stochastic matrix models with hydrologic data to predict the population dynamics of a riverine fish.

PubMed

Sakaris, Peter C; Irwin, Elise R

2010-03-01

We developed stochastic matrix models to evaluate the effects of hydrologic alteration and variable mortality on the population dynamics of a lotic fish in a regulated river system. Models were applied to a representative lotic fish species, the flathead catfish (Pylodictis olivaris), for which two populations were examined: a native population from a regulated reach of the Coosa River (Alabama, USA) and an introduced population from an unregulated section of the Ocmulgee River (Georgia, USA). Size-classified matrix models were constructed for both populations, and residuals from catch-curve regressions were used as indices of year class strength (i.e., recruitment). A multiple regression model indicated that recruitment of flathead catfish in the Coosa River was positively related to the frequency of spring pulses between 283 and 566 m3/s. For the Ocmulgee River population, multiple regression models indicated that year class strength was negatively related to mean March discharge and positively related to June low flow. When the Coosa population was modeled to experience five consecutive years of favorable hydrologic conditions during a 50-year projection period, it exhibited a substantial spike in size and increased at an overall 0.2% annual rate. When modeled to experience five years of unfavorable hydrologic conditions, the Coosa population initially exhibited a decrease in size but later stabilized and increased at a 0.4% annual rate following the decline. When the Ocmulgee River population was modeled to experience five years of favorable conditions, it exhibited a substantial spike in size and increased at an overall 0.4% annual rate. After the Ocmulgee population experienced five years of unfavorable conditions, a sharp decline in population size was predicted. However, the population quickly recovered, with population size increasing at a 0.3% annual rate following the decline. In general, stochastic population growth in the Ocmulgee River was more erratic and variable than population growth in the Coosa River. We encourage ecologists to develop similar models for other lotic species, particularly in regulated river systems. Successful management of fish populations in regulated systems requires that we are able to predict how hydrology affects recruitment and will ultimately influence the population dynamics of fishes.

Stochastic Model of Supercoiling-Dependent Transcription

NASA Astrophysics Data System (ADS)

Brackley, C. A.; Johnson, J.; Bentivoglio, A.; Corless, S.; Gilbert, N.; Gonnella, G.; Marenduzzo, D.

2016-07-01

We propose a stochastic model for gene transcription coupled to DNA supercoiling, where we incorporate the experimental observation that polymerases create supercoiling as they unwind the DNA helix and that these enzymes bind more favorably to regions where the genome is unwound. Within this model, we show that when the transcriptionally induced flux of supercoiling increases, there is a sharp crossover from a regime where torsional stresses relax quickly and gene transcription is random, to one where gene expression is highly correlated and tightly regulated by supercoiling. In the latter regime, the model displays transcriptional bursts, waves of supercoiling, and up regulation of divergent or bidirectional genes. It also predicts that topological enzymes which relax twist and writhe should provide a pathway to down regulate transcription.

Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model.

PubMed

Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P

2007-05-01

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

Elevated temperature alters carbon cycling in a model microbial community

NASA Astrophysics Data System (ADS)

Mosier, A.; Li, Z.; Thomas, B. C.; Hettich, R. L.; Pan, C.; Banfield, J. F.

2013-12-01

Earth's climate is regulated by biogeochemical carbon exchanges between the land, oceans and atmosphere that are chiefly driven by microorganisms. Microbial communities are therefore indispensible to the study of carbon cycling and its impacts on the global climate system. In spite of the critical role of microbial communities in carbon cycling processes, microbial activity is currently minimally represented or altogether absent from most Earth System Models. Method development and hypothesis-driven experimentation on tractable model ecosystems of reduced complexity, as presented here, are essential for building molecularly resolved, benchmarked carbon-climate models. Here, we use chemoautotropic acid mine drainage biofilms as a model community to determine how elevated temperature, a key parameter of global climate change, regulates the flow of carbon through microbial-based ecosystems. This study represents the first community proteomics analysis using tandem mass tags (TMT), which enable accurate, precise, and reproducible quantification of proteins. We compare protein expression levels of biofilms growing over a narrow temperature range expected to occur with predicted climate changes. We show that elevated temperature leads to up-regulation of proteins involved in amino acid metabolism and protein modification, and down-regulation of proteins involved in growth and reproduction. Closely related bacterial genotypes differ in their response to temperature: Elevated temperature represses carbon fixation by two Leptospirillum genotypes, whereas carbon fixation is significantly up-regulated at higher temperature by a third closely related genotypic group. Leptospirillum group III bacteria are more susceptible to viral stress at elevated temperature, which may lead to greater carbon turnover in the microbial food web through the release of viral lysate. Overall, this proteogenomics approach revealed the effects of climate change on carbon cycling pathways and other microbial activities. When scaled to more complex ecosystems and integrated into Earth System Models, this approach could significantly improve predictions of global carbon-climate feedbacks. Experiments such as these are a critical first step designed at understanding climate change impacts in order to better predict ecosystem adaptations, assess the viability of mitigation strategies, and inform relevant policy decisions.

An Improved Dynamic Model for the Respiratory Response to Exercise

PubMed Central

Serna, Leidy Y.; Mañanas, Miguel A.; Hernández, Alher M.; Rabinovich, Roberto A.

2018-01-01

Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model). Secondly, to propose changes in the models' structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency. PMID:29467674

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

DOE PAGES

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

2016-06-06

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies.

PubMed

DeRolph, Christopher R; Schramm, Michael P; Bevelhimer, Mark S

2016-10-01

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits.

PubMed

Sperry, John S; Wang, Yujie; Wolfe, Brett T; Mackay, D Scott; Anderegg, William R L; McDowell, Nate G; Pockman, William T

2016-11-01

Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (P canopy ) from soil water potential (P soil ) and vapor pressure deficit (D). Modeled responses to D and P soil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and P canopy across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

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

Bevelhimer, Mark S.; DeRolph, Christopher R.; Schramm, Michael P.

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multidisciplinary explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, wemore » were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements have been a result of a range of factors, from biological and hydrological to political and cultural. Furthermore, project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.« less

Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa[W

PubMed Central

Wang, Jack P.; Naik, Punith P.; Chen, Hsi-Chuan; Shi, Rui; Lin, Chien-Yuan; Liu, Jie; Shuford, Christopher M.; Li, Quanzi; Sun, Ying-Hsuan; Tunlaya-Anukit, Sermsawat; Williams, Cranos M.; Muddiman, David C.; Ducoste, Joel J.; Sederoff, Ronald R.; Chiang, Vincent L.

2014-01-01

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants. PMID:24619611

Mathematical modeling of white adipocyte exocytosis predicts adiponectin secretion and quantifies the rates of vesicle exo- and endocytosis.

PubMed

Brännmark, Cecilia; Lövfors, William; Komai, Ali M; Axelsson, Tom; El Hachmane, Mickaël F; Musovic, Saliha; Paul, Alexandra; Nyman, Elin; Olofsson, Charlotta S

2017-12-08

Adiponectin is a hormone secreted from white adipocytes and takes part in the regulation of several metabolic processes. Although the pathophysiological importance of adiponectin has been thoroughly investigated, the mechanisms controlling its release are only partly understood. We have recently shown that adiponectin is secreted via regulated exocytosis of adiponectin-containing vesicles, that adiponectin exocytosis is stimulated by cAMP-dependent mechanisms, and that Ca 2+ and ATP augment the cAMP-triggered secretion. However, much remains to be discovered regarding the molecular and cellular regulation of adiponectin release. Here, we have used mathematical modeling to extract detailed information contained within our previously obtained high-resolution patch-clamp time-resolved capacitance recordings to produce the first model of adiponectin exocytosis/secretion that combines all mechanistic knowledge deduced from electrophysiological experimental series. This model demonstrates that our previous understanding of the role of intracellular ATP in the control of adiponectin exocytosis needs to be revised to include an additional ATP-dependent step. Validation of the model by introduction of data of secreted adiponectin yielded a very close resemblance between the simulations and experimental results. Moreover, we could show that Ca 2+ -dependent adiponectin endocytosis contributes to the measured capacitance signal, and we were able to predict the contribution of endocytosis to the measured exocytotic rate under different experimental conditions. In conclusion, using mathematical modeling of published and newly generated data, we have obtained estimates of adiponectin exo- and endocytosis rates, and we have predicted adiponectin secretion. We believe that our model should have multiple applications in the study of metabolic processes and hormonal control thereof. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Modelling fatigue and the use of fatigue models in work settings.

PubMed

Dawson, Drew; Ian Noy, Y; Härmä, Mikko; Akerstedt, Torbjorn; Belenky, Gregory

2011-03-01

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the 'defenses-in-depth' approach to fatigue risk management. Copyright © 2010 Elsevier Ltd. All rights reserved.

Consumer-mediated recycling and cascading trophic interactions.

PubMed

Leroux, Shawn J; Loreau, Michel

2010-07-01

Cascading trophic interactions mediated by consumers are complex phenomena, which encompass many direct and indirect effects. Nonetheless, most experiments and theory on the topic focus uniquely on the indirect, positive effects of predators on producers via regulation of herbivores. Empirical research in aquatic ecosystems, however, demonstrate that the indirect, positive effects of consumer-mediated recycling on primary producer stocks may be larger than the effects of herbivore regulation, particularly when predators have access to alternative prey. We derive an ecosystem model with both recipient- and donor-controlled trophic relationships to test the conditions of four hypotheses generated from recent empirical work on the role of consumer-mediated recycling in cascading trophic interactions. Our model predicts that predator regulation of herbivores will have larger, positive effects on producers than consumer-mediated recycling in most cases but that consumer-mediated recycling does generally have a positive effect on producer stocks. We demonstrate that herbivore recycling will have larger effects on producer biomass than predator recycling when turnover rates and recycling efficiencies are high and predators prefer local prey. In addition, predictions suggest that consumer-mediated recycling has the largest effects on primary producers when predators prefer allochthonous prey and predator attack rates are high. Finally, our model predicts that consumer-mediated recycling effects may not be largest when external nutrient loading is low. Our model predictions highlight predator and prey feeding relationships, turnover rates, and external nutrient loading rates as key determinants of the strength of cascading trophic interactions. We show that existing hypotheses from specific empirical systems do not occur under all conditions, which further exacerbates the need to consider a broad suite of mechanisms when investigating trophic cascades.

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

PubMed Central

Schmitz, Ulf; Lai, Xin; Winter, Felix; Wolkenhauer, Olaf; Vera, Julio; Gupta, Shailendra K.

2014-01-01

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/. PMID:24875477

To Achieve or Not To Achieve: A Self-Regulation Perspective on Adolescents' Academic Decision Making.

ERIC Educational Resources Information Center

Miller, David C.; Byrnes, James P.

2001-01-01

This study investigated the utility of the self-regulation model of decision making for explaining and predicting adolescents' academic decision making. Measures included an assessment of decision-making skill; academic goals; select scales of Learning and Study Strategies Inventory; and teacher ratings of achievement behavior. Adolescents'…

Potential Impact of Clean Air Act Regulations on Nitrogen Fate and Transport in the Neuse River Basin: a Modeling Investigation Using CMAQ and SWAT

EPA Science Inventory

There has been extensive analysis of Clean Air Act Amendment (CAAA) regulation impacts to changes in atmospheric nitrogen deposition; however, few studies have focused on watershed nitrogen transfer particularly regarding long-term predictions. In this study, we investigated impa...

Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells

PubMed Central

de Luis Balaguer, Maria Angels; Fisher, Adam P.; Clark, Natalie M.; Fernandez-Espinosa, Maria Guadalupe; Möller, Barbara K.; Weijers, Dolf; Williams, Cranos; Lorenzo, Oscar; Sozzani, Rosangela

2017-01-01

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells. PMID:28827319

Axon growth regulation by a bistable molecular switch.

PubMed

Padmanabhan, Pranesh; Goodhill, Geoffrey J

2018-04-25

For the brain to function properly, its neurons must make the right connections during neural development. A key aspect of this process is the tight regulation of axon growth as axons navigate towards their targets. Neuronal growth cones at the tips of developing axons switch between growth and paused states during axonal pathfinding, and this switching behaviour determines the heterogeneous axon growth rates observed during brain development. The mechanisms controlling this switching behaviour, however, remain largely unknown. Here, using mathematical modelling, we predict that the molecular interaction network involved in axon growth can exhibit bistability, with one state representing a fast-growing growth cone state and the other a paused growth cone state. Owing to stochastic effects, even in an unchanging environment, model growth cones reversibly switch between growth and paused states. Our model further predicts that environmental signals could regulate axon growth rate by controlling the rates of switching between the two states. Our study presents a new conceptual understanding of growth cone switching behaviour, and suggests that axon guidance may be controlled by both cell-extrinsic factors and cell-intrinsic growth regulatory mechanisms. © 2018 The Author(s).

Models for financing the regulation of pharmaceutical promotion.

PubMed

Lexchin, Joel

2012-07-11

Pharmaceutical companies spend huge sums promoting their products whereas regulation of promotional activities is typically underfinanced. Any option for financing the monitoring and regulation of promotion should adhere to three basic principles: stability, predictability and lack of (perverse) ties between the level of financing and performance. This paper explores the strengths and weaknesses of six different models. All these six models considered here have positive and negative features and none may necessarily be ideal in any particular country. Different countries may choose to utilize a combination of two or more of these models in order to raise sufficient revenue. Financing of regulation of drug promotion should more than pay for itself through the prevention of unnecessary drug costs and the avoidance of adverse health effects due to inappropriate prescribing. However, it involves an initial outlay of money that is currently not being spent and many national governments, in both rich and poor countries, are unwilling to incur extra costs.

A mathematical model of the volume, pH, and ion content regulation in reticulocytes. Application to the pathophysiology of sickle cell dehydration.

PubMed Central

Lew, V L; Freeman, C J; Ortiz, O E; Bookchin, R M

1991-01-01

We developed a mathematical model of the reticulocyte, seeking to explain how a cell with similar volume but much higher ionic traffic than the mature red cell (RBC) regulates its volume, pH, and ion content in physiological and abnormal conditions. Analysis of the fluxbalance required by reticulocytes to conserve volume and composition predicted the existence of previously unsuspected Na(+)-dependent Cl- entry mechanisms. Unlike mature RBCs, reticulocytes did not tend to return to their original state after brief perturbations. The model predicted hysteresis and drift in cell pH, volume, and ion contents after transient alterations in membrane permeability or medium composition; irreversible cell dehydration could thus occur by brief K+ permeabilization, transient medium acidification, or the replacement of external Na+ with an impermeant cation. Both the hysteresis and drift after perturbations were shown to depend on the pHi dependence of the K:Cl cotransport, a major reticulocyte transporter. This behavior suggested a novel mechanism for the generation of irreversibly sickled cells directly from reticulocytes, rather than in a stepwise, progressive manner from discocytes. Experimental tests of the model's predictions and the hypothesis are described in the following paper. PMID:1985088

Application of a Fuzzy Neural Network Model in Predicting Polycyclic Aromatic Hydrocarbon- Mediated Perturbations of the Cyp1b1 Transcriptional Regulatory Network in Mouse Skin

PubMed Central

Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

2013-01-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave one out cross-validation. Predictions were within 1 log2 fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. PMID:23274566

Systems Modeling of Molecular Mechanisms Controlling Cytokine-driven CD4+ T Cell Differentiation and Phenotype Plasticity

PubMed Central

Carbo, Adria; Hontecillas, Raquel; Kronsteiner, Barbara; Viladomiu, Monica; Pedragosa, Mireia; Lu, Pinyi; Philipson, Casandra W.; Hoops, Stefan; Marathe, Madhav; Eubank, Stephen; Bisset, Keith; Wendelsdorf, Katherine; Jarrah, Abdul; Mei, Yongguo; Bassaganya-Riera, Josep

2013-01-01

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa. PMID:23592971

Molecular determinants of blood-brain barrier permeation.

PubMed

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

Molecular determinants of blood–brain barrier permeation

PubMed Central

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood–brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution. PMID:26305616

Modelling white-water rafting suitability in a hydropower regulated Alpine River.

PubMed

Carolli, Mauro; Zolezzi, Guido; Geneletti, Davide; Siviglia, Annunziato; Carolli, Fabiano; Cainelli, Oscar

2017-02-01

Cultural and recreational river ecosystem services and their relations with the flow regime are still poorly investigated. We develop a modelling-based approach to assess recreational flow requirements and the spatially distributed river suitability for white-water rafting, a typical service offered by mountain streams, with potential conflicts of interest with hydropower regulation. The approach is based on the principles of habitat suitability modelling using water depth as the main attribute, with preference curves defined through interviews with local rafting guides. The methodology allows to compute streamflow thresholds for conditions of suitability and optimality of a river reach in relation to rafting. Rafting suitability response to past, present and future flow management scenarios can be predicted on the basis of a hydrological model, which is incorporated in the methodology and is able to account for anthropic effects. Rafting suitability is expressed through a novel metric, the "Rafting hydro-suitability index" (RHSI) which quantifies the cumulative duration of suitable and optimal conditions for rafting. The approach is applied on the Noce River (NE Italy), an Alpine River regulated by hydropower production and affected by hydropeaking, which influences suitability at a sub-daily scale. A dedicated algorithm is developed within the hydrological model to resemble hydropeaking conditions with daily flow data. In the Noce River, peak flows associated with hydropeaking support rafting activities in late summer, highlighting the dual nature of hydropeaking in regulated rivers. Rafting suitability is slightly reduced under present, hydropower-regulated flow conditions compared to an idealized flow regime characterised by no water abstractions. Localized water abstractions for small, run-of-the-river hydropower plants are predicted to negatively affect rafting suitability. The proposed methodology can be extended to support decision making for flow management in hydropower regulated streams, as it has the potential to quantify the response of different ecosystem services to flow regulation. Copyright © 2016 Elsevier B.V. All rights reserved.

LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

Treesearch

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

2014-01-01

LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

Monitoring and Depth of Strategy Use in Computer-Based Learning Environments for Science and History

ERIC Educational Resources Information Center

Deekens, Victor M.; Greene, Jeffrey A.; Lobczowski, Nikki G.

2018-01-01

Background: Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning…

Attachment, emotion regulation and coping in Portuguese emerging adults: a test of a mediation hypothesis.

PubMed

Cabral, Joana; Matos, Paula M; Beyers, Wim; Soenens, Bart

2012-11-01

Although the quality of parent-adolescent emotional bonds has consistently been proposed as a major influence on young adult's psycho-emotional functioning, the precise means by which these bonds either facilitate or impede adaptive coping are not well-understood. In an effort to advance this inquiry, the present study examined interrelationships among measures of parental attachment, emotion regulation processes, and preferred coping strategies within a sample of 942 college freshmen. Structural Equation Modelling was used to test whether the link between attachment to parents and the use of particular coping strategies is mediated by differences in emotion regulation mechanisms. As hypothesized, differences in attachment to parents predicted differences in the use of emotion regulation mechanisms and coping strategies. More specifically, having a close emotional bond, feeling supported in autonomy processes and having (moderately) low levels of separation anxiety toward parents predict more constructive emotion regulation mechanisms and coping strategies. Additionally emotion regulation was found to (partly or totally) mediate the association between attachment and coping.

Evaluating mallard adaptive management models with time series

USGS Publications Warehouse

Conn, P.B.; Kendall, W.L.

2004-01-01

Wildlife practitioners concerned with midcontinent mallard (Anas platyrhynchos) management in the United States have instituted a system of adaptive harvest management (AHM) as an objective format for setting harvest regulations. Under the AHM paradigm, predictions from a set of models that reflect key uncertainties about processes underlying population dynamics are used in coordination with optimization software to determine an optimal set of harvest decisions. Managers use comparisons of the predictive abilities of these models to gauge the relative truth of different hypotheses about density-dependent recruitment and survival, with better-predicting models giving more weight to the determination of harvest regulations. We tested the effectiveness of this strategy by examining convergence rates of 'predictor' models when the true model for population dynamics was known a priori. We generated time series for cases when the a priori model was 1 of the predictor models as well as for several cases when the a priori model was not in the model set. We further examined the addition of different levels of uncertainty into the variance structure of predictor models, reflecting different levels of confidence about estimated parameters. We showed that in certain situations, the model-selection process favors a predictor model that incorporates the hypotheses of additive harvest mortality and weakly density-dependent recruitment, even when the model is not used to generate data. Higher levels of predictor model variance led to decreased rates of convergence to the model that generated the data, but model weight trajectories were in general more stable. We suggest that predictive models should incorporate all sources of uncertainty about estimated parameters, that the variance structure should be similar for all predictor models, and that models with different functional forms for population dynamics should be considered for inclusion in predictor model! sets. All of these suggestions should help lower the probability of erroneous learning in mallard ABM and adaptive management in general.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

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

System-level modeling of acetone-butanol-ethanol fermentation.

PubMed

Liao, Chen; Seo, Seung-Oh; Lu, Ting

2016-05-01

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A model of lysosomal pH regulation

PubMed Central

Ishida, Yoichi; Nayak, Smita

2013-01-01

Lysosomes must maintain an acidic luminal pH to activate hydrolytic enzymes and degrade internalized macromolecules. Acidification requires the vacuolar-type H+-ATPase to pump protons into the lumen and a counterion flux to neutralize the membrane potential created by proton accumulation. Early experiments suggested that the counterion was chloride, and more recently a pathway consistent with the ClC-7 Cl–/H+ antiporter was identified. However, reports that the steady-state luminal pH is unaffected in ClC-7 knockout mice raise questions regarding the identity of the carrier and the counterion. Here, we measure the current–voltage characteristics of a mammalian ClC-7 antiporter, and we use its transport properties, together with other key ion regulating elements, to construct a mathematical model of lysosomal pH regulation. We show that results of in vitro lysosome experiments can only be explained by the presence of ClC-7, and that ClC-7 promotes greater acidification than Cl–, K+, or Na+ channels. Our models predict strikingly different lysosomal K+ dynamics depending on the major counterion pathways. However, given the lack of experimental data concerning acidification in vivo, the model cannot definitively rule out any given mechanism, but the model does provide concrete predictions for additional experiments that would clarify the identity of the counterion and its carrier. PMID:23712550

Regulation of Ion Gradients across Myocardial Ischemic Border Zones: A Biophysical Modelling Analysis

PubMed Central

Niederer, Steven

2013-01-01

The myocardial ischemic border zone is associated with the initiation and sustenance of arrhythmias. The profile of ionic concentrations across the border zone play a significant role in determining cellular electrophysiology and conductivity, yet their spatial-temporal evolution and regulation are not well understood. To investigate the changes in ion concentrations that regulate cellular electrophysiology, a mathematical model of ion movement in the intra and extracellular space in the presence of ionic, potential and material property heterogeneities was developed. The model simulates the spatial and temporal evolution of concentrations of potassium, sodium, chloride, calcium, hydrogen and bicarbonate ions and carbon dioxide across an ischemic border zone. Ischemia was simulated by sodium-potassium pump inhibition, potassium channel activation and respiratory and metabolic acidosis. The model predicted significant disparities in the width of the border zone for each ionic species, with intracellular sodium and extracellular potassium having discordant gradients, facilitating multiple gradients in cellular properties across the border zone. Extracellular potassium was found to have the largest border zone and this was attributed to the voltage dependence of the potassium channels. The model also predicted the efflux of from the ischemic region due to electrogenic drift and diffusion within the intra and extracellular space, respectively, which contributed to depletion in the ischemic region. PMID:23577101

The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model

PubMed Central

De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M.; Sander, Paul; Cardelle-Elawar, María

2014-01-01

The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university. PMID:25964764

Modeling risk for SOD nationwide: what are the effects of model choice on risk prediction?

Treesearch

M. Kelly; D. Shaari; Q. Guo; D. Liu

2006-01-01

Phytophthora ramorum has the potential to infect many forest types found throughout the United States. Efforts to model the potential habitat for P. ramorum and sudden oak death (SOD) are important for disease regulation and management. Yet, spatial models using identical data can have differing results. In this paper we examine...

Parental childhood adversity, depressive symptoms, and parenting quality: Effects on toddler self-regulation in Child Welfare Services-involved families

PubMed Central

Spieker, Susan J.; Oxford, Monica L.; Fleming, Charles B.; Lohr, Mary Jane

2018-01-01

Parents who are child welfare services-involved (CWSI) often have a history of childhood adversity and depressive symptoms. Both affect parenting quality, which in turn influences child adaptive functioning. We tested a model of the relations between parental depression and child regulatory outcomes first proposed by Lyons-Ruth and colleagues (2002). We hypothesized that both parental depression and parenting quality mediate the effects of parental early adversity on offspring regulatory outcomes. Participants were 123 CWSI parents and their toddlers who were assessed three times over a period of six months. At T1, parents reported on their childhood adversity and current depressive symptoms. At T2, parents’ sensitivity to their child’s distress and non-distress cues were rated from a videotaped teaching task. At T3, observers rated children’s emotional regulation, orientation/engagement, and secure base behavior. The results of a path model partly supported hypotheses. Parent childhood adversity was associated with current depressive symptoms, which in turn was related to parent sensitivity to child distress but not non-distress. Sensitivity to distress also predicted secure base behavior. Depression directly predicted orientation/engagement, also predicted by sensitivity to non-distress. Sensitivity to distress predicted emotion regulation and orientation/engagement. Results are discussed in terms of intervention approaches for CWSI families. PMID:29266280

PARENTAL CHILDHOOD ADVERSITY, DEPRESSIVE SYMPTOMS, AND PARENTING QUALITY: EFFECTS ON TODDLER SELF-REGULATION IN CHILD WELFARE SERVICES INVOLVED FAMILIES.

PubMed

Spieker, Susan J; Oxford, Monica L; Fleming, Charles B; Lohr, Mary Jane

2018-01-01

Parents who are involved with child welfare services (CWSI) often have a history of childhood adversity and depressive symptoms. Both affect parenting quality, which in turn influences child adaptive functioning. We tested a model of the relations between parental depression and child regulatory outcomes first proposed by K. Lyons-Ruth, R. Wolfe, A. Lyubchik, and R. Steingard (2002). We hypothesized that both parental depression and parenting quality mediate the effects of parental early adversity on offspring regulatory outcomes. Participants were 123 CWSI parents and their toddlers assessed three times over a period of 6 months. At Time 1, parents reported on their childhood adversity and current depressive symptoms. At Time 2, parents' sensitivity to their child's distress and nondistress cues was rated from a videotaped teaching task. At Time 3, observers rated children's emotional regulation, orientation/engagement, and secure base behavior. The results of a path model partly supported the hypotheses. Parent childhood adversity was associated with current depressive symptoms, which in turn related to parent sensitivity to child distress, but not nondistress. Sensitivity to distress also predicted secure base behavior. Depression directly predicted orientation/engagement, also predicted by sensitivity to nondistress. Sensitivity to distress predicted emotion regulation and orientation/engagement. Results are discussed in terms of intervention approaches for CWSI families. © 2017 Michigan Association for Infant Mental Health.

Dynamic circadian modulation in a biomathematical model for the effects of sleep and sleep loss on waking neurobehavioral performance.

PubMed

McCauley, Peter; Kalachev, Leonid V; Mollicone, Daniel J; Banks, Siobhan; Dinges, David F; Van Dongen, Hans P A

2013-12-01

Recent experimental observations and theoretical advances have indicated that the homeostatic equilibrium for sleep/wake regulation--and thereby sensitivity to neurobehavioral impairment from sleep loss--is modulated by prior sleep/wake history. This phenomenon was predicted by a biomathematical model developed to explain changes in neurobehavioral performance across days in laboratory studies of total sleep deprivation and sustained sleep restriction. The present paper focuses on the dynamics of neurobehavioral performance within days in this biomathematical model of fatigue. Without increasing the number of model parameters, the model was updated by incorporating time-dependence in the amplitude of the circadian modulation of performance. The updated model was calibrated using a large dataset from three laboratory experiments on psychomotor vigilance test (PVT) performance, under conditions of sleep loss and circadian misalignment; and validated using another large dataset from three different laboratory experiments. The time-dependence of circadian amplitude resulted in improved goodness-of-fit in night shift schedules, nap sleep scenarios, and recovery from prior sleep loss. The updated model predicts that the homeostatic equilibrium for sleep/wake regulation--and thus sensitivity to sleep loss--depends not only on the duration but also on the circadian timing of prior sleep. This novel theoretical insight has important implications for predicting operator alertness during work schedules involving circadian misalignment such as night shift work.

Computational modelling of biomaterial surface interactions with blood platelets and osteoblastic cells for the prediction of contact osteogenesis.

PubMed

Amor, N; Geris, L; Vander Sloten, J; Van Oosterwyck, H

2011-02-01

Surface microroughness can induce contact osteogenesis (bone formation initiated at the implant surface) around oral implants, which may result from different mechanisms, such as blood platelet-biomaterial interactions and/or interaction with (pre-)osteoblast cells. We have developed a computational model of implant endosseous healing that takes into account these interactions. We hypothesized that the initial attachment and growth factor release from activated platelets is crucial in achieving contact osteogenesis. In order to investigate this, a computational model was applied to an animal experiment [7] that looked at the effect of surface microroughness on endosseous healing. Surface-specific model parameters were implemented based on in vitro data (Lincks et al. Biomaterials 1998;19:2219-32). The predicted spatio-temporal patterns of bone formation correlated with the histological data. It was found that contact osteogenesis could not be predicted if only the osteogenic response of cells was up-regulated by surface microroughness. This could only be achieved if platelet-biomaterial interactions were sufficiently up-regulated as well. These results confirmed our hypothesis and demonstrate the added value of the computational model to study the importance of surface-mediated events for peri-implant endosseous healing. Copyright © 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Computational analysis of an autophagy/translation switch based on mutual inhibition of MTORC1 and ULK1

DOE PAGES

Szymańska, Paulina; Martin, Katie R.; MacKeigan, Jeffrey P.; ...

2015-03-11

We constructed a mechanistic, computational model for regulation of (macro)autophagy and protein synthesis (at the level of translation). The model was formulated to study the system-level consequences of interactions among the following proteins: two key components of MTOR complex 1 (MTORC1), namely the protein kinase MTOR (mechanistic target of rapamycin) and the scaffold protein RPTOR; the autophagy-initiating protein kinase ULK1; and the multimeric energy-sensing AMP-activated protein kinase (AMPK). Inputs of the model include intrinsic AMPK kinase activity, which is taken as an adjustable surrogate parameter for cellular energy level or AMP:ATP ratio, and rapamycin dose, which controls MTORC1 activity. Outputsmore » of the model include the phosphorylation level of the translational repressor EIF4EBP1, a substrate of MTORC1, and the phosphorylation level of AMBRA1 (activating molecule in BECN1-regulated autophagy), a substrate of ULK1 critical for autophagosome formation. The model incorporates reciprocal regulation of mTORC1 and ULK1 by AMPK, mutual inhibition of MTORC1 and ULK1, and ULK1-mediated negative feedback regulation of AMPK. Through analysis of the model, we find that these processes may be responsible, depending on conditions, for graded responses to stress inputs, for bistable switching between autophagy and protein synthesis, or relaxation oscillations, comprising alternating periods of autophagy and protein synthesis. A sensitivity analysis indicates that the prediction of oscillatory behavior is robust to changes of the parameter values of the model. The model provides testable predictions about the behavior of the AMPK-MTORC1-ULK1 network, which plays a central role in maintaining cellular energy and nutrient homeostasis.« less

Growth in Adolescent Self-Regulation and Impact on Sexual Risk-Taking: A Curve-of-Factors Analysis.

PubMed

Crandall, AliceAnn; Magnusson, Brianna M; Novilla, M Lelinneth B

2018-04-01

Adolescent self-regulation is increasingly seen as an important predictor of sexual risk-taking behaviors, but little is understood about how changes in self-regulation affect later sexual risk-taking. Family financial stress may affect the development of self-regulation and later engagement in sexual risk-taking. We examined whether family financial stress influences self-regulation in early adolescence (age 13) and growth in self-regulation throughout adolescence (from age 13-17 years). We then assessed the effects of family financial stress, baseline self-regulation, and the development of self-regulation on adolescent sexual risk-taking behaviors at age 18 years. Using a curve-of-factors model, we examined these relationships in a 6-year longitudinal study of 470 adolescents (52% female) and their parents from a large northwestern city in the United States. Results indicated that family financial stress was negatively associated with baseline self-regulation but not with growth in self-regulation throughout adolescence. Both baseline self-regulation and growth in self-regulation were predictive of decreased likelihood of engaging in sexual risk-taking. Family financial stress was not predictive of later sexual risk-taking. Intervening to support the development of self-regulation in adolescence may be especially protective against later sexual risk-taking.

The emotional and academic consequences of parental conditional regard: comparing conditional positive regard, conditional negative regard, and autonomy support as parenting practices.

PubMed

Roth, Guy; Assor, Avi; Niemiec, Christopher P; Deci, Edward L; Ryan, Richard M

2009-07-01

The authors conducted 2 studies of 9th-grade Israeli adolescents (169 in Study 1, 156 in Study 2) to compare the parenting practices of conditional positive regard, conditional negative regard, and autonomy support using data from multiple reporters. Two socialization domains were studied: emotion control and academics. Results were consistent with the self-determination theory model of internalization, which posits that (a) conditional negative regard predicts feelings of resentment toward parents, which then predict dysregulation of negative emotions and academic disengagement; (b) conditional positive regard predicts feelings of internal compulsion, which then predict suppressive regulation of negative emotions and grade-focused academic engagement; and (c) autonomy support predicts sense of choice, which then predicts integrated regulation of negative emotions and interest-focused academic engagement. These findings suggest that even parents' use of conditional positive regard as a socialization practice has adverse emotional and academic consequences, relative to autonomy support.

The Adolescent's Competency for Interacting with Alcohol as a Determinant of Intake: The Role of Self-Regulation.

PubMed

de la Fuente, Jesús; Cubero, Inmaculada; Sánchez-Amate, Mari Carmen; Peralta, Francisco J; Garzón, Angélica; Fiz Pérez, Javier

2017-01-01

The competency for interacting with alcohol is a highly useful Educational Psychology model for preventing and for understanding the different behavioral levels of this interaction. Knowledge of facts, concepts and principles about alcohol use, self-regulated behavior, and attitudes toward alcohol are predictive of adequate interaction with alcohol. The objective of this study was to empirically evaluate this postulated relationship. A total of 328 Spanish adolescents participated, between the ages of 12 and 17. All were enrolled in 1st-4th year of compulsory secondary education, in the context of the ALADO Program for prevention of alcohol intake in adolescents. An ex post facto design was used, with inferential analyses and SEM analyses. Results show an interdependence relationship, with significant structural prediction between the behavioral levels defined and the level of alcohol intake, with principles, self-regulating control and attitudes carrying more weight. Analyses are presented, as are implications for psychoeducational intervention using preventive programs based on this competency model.

The Adolescent's Competency for Interacting with Alcohol as a Determinant of Intake: The Role of Self-Regulation

PubMed Central

de la Fuente, Jesús; Cubero, Inmaculada; Sánchez-Amate, Mari Carmen; Peralta, Francisco J.; Garzón, Angélica; Fiz Pérez, Javier

2017-01-01

The competency for interacting with alcohol is a highly useful Educational Psychology model for preventing and for understanding the different behavioral levels of this interaction. Knowledge of facts, concepts and principles about alcohol use, self-regulated behavior, and attitudes toward alcohol are predictive of adequate interaction with alcohol. The objective of this study was to empirically evaluate this postulated relationship. A total of 328 Spanish adolescents participated, between the ages of 12 and 17. All were enrolled in 1st–4th year of compulsory secondary education, in the context of the ALADO Program for prevention of alcohol intake in adolescents. An ex post facto design was used, with inferential analyses and SEM analyses. Results show an interdependence relationship, with significant structural prediction between the behavioral levels defined and the level of alcohol intake, with principles, self-regulating control and attitudes carrying more weight. Analyses are presented, as are implications for psychoeducational intervention using preventive programs based on this competency model. PMID:29123492

Roles of Raft-Anchored Adaptor Cbp/PAG1 in Spatial Regulation of c-Src Kinase

PubMed Central

Oneyama, Chitose; Suzuki, Takashi; Okada, Masato

2014-01-01

The tyrosine kinase c-Src is upregulated in numerous human cancers, implying a role for c-Src in cancer progression. Previously, we have shown that sequestration of activated c-Src into lipid rafts via a transmembrane adaptor, Cbp/PAG1, efficiently suppresses c-Src-induced cell transformation in Csk-deficient cells, suggesting that the transforming activity of c-Src is spatially regulated via Cbp in lipid rafts. To dissect the molecular mechanisms of the Cbp-mediated regulation of c-Src, a combined analysis was performed that included mathematical modeling and in vitro experiments in a c-Src- or Cbp-inducible system. c-Src activity was first determined as a function of c-Src or Cbp levels, using focal adhesion kinase (FAK) as a crucial c-Src substrate. Based on these experimental data, two mathematical models were constructed, the sequestration model and the ternary model. The computational analysis showed that both models supported our proposal that raft localization of Cbp is crucial for the suppression of c-Src function, but the ternary model, which includes a ternary complex consisting of Cbp, c-Src, and FAK, also predicted that c-Src function is dependent on the lipid-raft volume. Experimental analysis revealed that c-Src activity is elevated when lipid rafts are disrupted and the ternary complex forms in non-raft membranes, indicating that the ternary model accurately represents the system. Moreover, the ternary model predicted that, if Cbp enhances the interaction between c-Src and FAK, Cbp could promote c-Src function when lipid rafts are disrupted. These findings underscore the crucial role of lipid rafts in the Cbp-mediated negative regulation of c-Src-transforming activity, and explain the positive role of Cbp in c-Src regulation under particular conditions where lipid rafts are perturbed. PMID:24675741

Transcription Factor Binding Profiles Reveal Cyclic Expression of Human Protein-coding Genes and Non-coding RNAs

PubMed Central

Cheng, Chao; Ung, Matthew; Grant, Gavin D.; Whitfield, Michael L.

2013-01-01

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements. PMID:23874175

Inflammation, Self-Regulation, and Health: An Immunologic Model of Self-Regulatory Failure.

PubMed

Shields, Grant S; Moons, Wesley G; Slavich, George M

2017-07-01

Self-regulation is a fundamental human process that refers to multiple complex methods by which individuals pursue goals in the face of distractions. Whereas superior self-regulation predicts better academic achievement, relationship quality, financial and career success, and lifespan health, poor self-regulation increases a person's risk for negative outcomes in each of these domains and can ultimately presage early mortality. Given its centrality to understanding the human condition, a large body of research has examined cognitive, emotional, and behavioral aspects of self-regulation. In contrast, relatively little attention has been paid to specific biologic processes that may underlie self-regulation. We address this latter issue in the present review by examining the growing body of research showing that components of the immune system involved in inflammation can alter neural, cognitive, and motivational processes that lead to impaired self-regulation and poor health. Based on these findings, we propose an integrated, multilevel model that describes how inflammation may cause widespread biobehavioral alterations that promote self-regulatory failure. This immunologic model of self-regulatory failure has implications for understanding how biological and behavioral factors interact to influence self-regulation. The model also suggests new ways of reducing disease risk and enhancing human potential by targeting inflammatory processes that affect self-regulation.

Breakup and then makeup: a predictive model of how cilia self-regulate hardness for posture control.

PubMed

Bandyopadhyay, Promode R; Hansen, Joshua C

2013-01-01

Functioning as sensors and propulsors, cilia are evolutionarily conserved organelles having a highly organized internal structure. How a paramecium's cilium produces off-propulsion-plane curvature during its return stroke for symmetry breaking and drag reduction is not known. We explain these cilium deformations by developing a torsional pendulum model of beat frequency dependence on viscosity and an olivo-cerebellar model of self-regulation of posture control. The phase dependence of cilia torsion is determined, and a bio-physical model of hardness control with predictive features is offered. Crossbridge links between the central microtubule pair harden the cilium during the power stroke; this stroke's end is a critical phase during which ATP molecules soften the crossbridge-microtubule attachment at the cilium inflection point where torsion is at its maximum. A precipitous reduction in hardness ensues, signaling the start of ATP hydrolysis that re-hardens the cilium. The cilium attractor basin could be used as reference for perturbation sensing.

Breakup and then makeup: a predictive model of how cilia self-regulate hardness for posture control

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Promode R.; Hansen, Joshua C.

2013-06-01

Functioning as sensors and propulsors, cilia are evolutionarily conserved organelles having a highly organized internal structure. How a paramecium's cilium produces off-propulsion-plane curvature during its return stroke for symmetry breaking and drag reduction is not known. We explain these cilium deformations by developing a torsional pendulum model of beat frequency dependence on viscosity and an olivo-cerebellar model of self-regulation of posture control. The phase dependence of cilia torsion is determined, and a bio-physical model of hardness control with predictive features is offered. Crossbridge links between the central microtubule pair harden the cilium during the power stroke; this stroke's end is a critical phase during which ATP molecules soften the crossbridge-microtubule attachment at the cilium inflection point where torsion is at its maximum. A precipitous reduction in hardness ensues, signaling the start of ATP hydrolysis that re-hardens the cilium. The cilium attractor basin could be used as reference for perturbation sensing.

Integral control of plant gravitropism through the interplay of hormone signaling and gene regulation.

PubMed

Rodrigo, Guillermo; Jaramillo, Alfonso; Blázquez, Miguel A

2011-08-17

The interplay between hormone signaling and gene regulatory networks is instrumental in promoting the development of living organisms. In particular, plants have evolved mechanisms to sense gravity and orient themselves accordingly. Here, we present a mathematical model that reproduces plant gravitropic responses based on known molecular genetic interactions for auxin signaling coupled with a physical description of plant reorientation. The model allows one to analyze the spatiotemporal dynamics of the system, triggered by an auxin gradient that induces differential growth of the plant with respect to the gravity vector. Our model predicts two important features with strong biological implications: 1), robustness of the regulatory circuit as a consequence of integral control; and 2), a higher degree of plasticity generated by the molecular interplay between two classes of hormones. Our model also predicts the ability of gibberellins to modulate the tropic response and supports the integration of the hormonal role at the level of gene regulation. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Breakup and then makeup: a predictive model of how cilia self-regulate hardness for posture control

PubMed Central

Bandyopadhyay, Promode R.; Hansen, Joshua C.

2013-01-01

Functioning as sensors and propulsors, cilia are evolutionarily conserved organelles having a highly organized internal structure. How a paramecium's cilium produces off-propulsion-plane curvature during its return stroke for symmetry breaking and drag reduction is not known. We explain these cilium deformations by developing a torsional pendulum model of beat frequency dependence on viscosity and an olivo-cerebellar model of self-regulation of posture control. The phase dependence of cilia torsion is determined, and a bio-physical model of hardness control with predictive features is offered. Crossbridge links between the central microtubule pair harden the cilium during the power stroke; this stroke's end is a critical phase during which ATP molecules soften the crossbridge-microtubule attachment at the cilium inflection point where torsion is at its maximum. A precipitous reduction in hardness ensues, signaling the start of ATP hydrolysis that re-hardens the cilium. The cilium attractor basin could be used as reference for perturbation sensing. PMID:23739771

Interactive contributions of self-regulation deficits and social motivation to psychopathology: Unraveling divergent pathways to aggressive behavior and depressive symptoms

PubMed Central

RUDOLPH, KAREN D.; TROOP-GORDON, WENDY; LLEWELLYN, NICOLE

2015-01-01

Poor self-regulation has been implicated as a significant risk factor for the development of multiple forms of psychopathology. This research examined the proposition that self-regulation deficits differentially predict aggressive behavior and depressive symptoms, depending on children’s social approach versus avoidance motivation. A prospective, multiple-informant approach was used to test this hypothesis in 419 children (M age = 8.92, SD = 0.36). Parents rated children’s inhibitory control. Children completed measures of social approach–avoidance motivation and depressive symptoms. Teachers rated children’s aggressive behavior. As anticipated, poor inhibitory control predicted aggressive behavior in boys with high but not low approach motivation and low but not high avoidance motivation, whereas poor inhibitory control predicted depressive symptoms in girls with high but not low avoidance motivation. This research supports several complementary theoretical models of psychopathology and provides insight into the differential contributions of poor self-regulation to maladaptive developmental outcomes. The findings suggest the need for targeted intervention programs that consider heterogeneity among children with self-regulatory deficits. PMID:23627953

Predicting the Dynamics of Protein Abundance

PubMed Central

Mehdi, Ahmed M.; Patrick, Ralph; Bailey, Timothy L.; Bodén, Mikael

2014-01-01

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA–protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/. PMID:24532840

Predicting the dynamics of protein abundance.

PubMed

Mehdi, Ahmed M; Patrick, Ralph; Bailey, Timothy L; Bodén, Mikael

2014-05-01

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

Applications of alignment-free methods in epigenomics.

PubMed

Pinello, Luca; Lo Bosco, Giosuè; Yuan, Guo-Cheng

2014-05-01

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

Leadership styles, emotion regulation, and burnout.

PubMed

Arnold, Kara A; Connelly, Catherine E; Walsh, Megan M; Martin Ginis, Kathleen A

2015-10-01

This study investigated the potential impact of leadership style on leaders' emotional regulation strategies and burnout. Drawing on the full-range model of leadership and Conservation of Resources (COR) theory, we tested whether transformational, contingent reward, management by exception-active and -passive, or laissez-faire leadership exert direct effects on leaders' reported use of surface acting, deep acting, and genuine emotion. In turn, we hypothesized and tested the indirect effect of leadership on burnout through surface acting. Three waves of data from 205 leaders were analyzed using OLS regression. Transformational leadership predicted deep acting and genuine emotion. Contingent reward predicted both surface and deep acting. Management by exception-active and -passive predicted surface acting, and laissez faire predicted genuine emotion. The indirect effects of management by exception-active and -passive on burnout through surface acting were not significant. Indirect effects of transformational leadership and laissez-faire on burnout through genuine emotion, however, were significant. This study provides empirical evidence for the hypothesized relationships between leadership style, emotion regulation, and burnout, and provides the basis for future research and theory building on this topic. (c) 2015 APA, all rights reserved).

Template-based modeling and ab initio refinement of protein oligomer structures using GALAXY in CAPRI round 30.

PubMed

Lee, Hasup; Baek, Minkyung; Lee, Gyu Rie; Park, Sangwoo; Seok, Chaok

2017-03-01

Many proteins function as homo- or hetero-oligomers; therefore, attempts to understand and regulate protein functions require knowledge of protein oligomer structures. The number of available experimental protein structures is increasing, and oligomer structures can be predicted using the experimental structures of related proteins as templates. However, template-based models may have errors due to sequence differences between the target and template proteins, which can lead to functional differences. Such structural differences may be predicted by loop modeling of local regions or refinement of the overall structure. In CAPRI (Critical Assessment of PRotein Interactions) round 30, we used recently developed features of the GALAXY protein modeling package, including template-based structure prediction, loop modeling, model refinement, and protein-protein docking to predict protein complex structures from amino acid sequences. Out of the 25 CAPRI targets, medium and acceptable quality models were obtained for 14 and 1 target(s), respectively, for which proper oligomer or monomer templates could be detected. Symmetric interface loop modeling on oligomer model structures successfully improved model quality, while loop modeling on monomer model structures failed. Overall refinement of the predicted oligomer structures consistently improved the model quality, in particular in interface contacts. Proteins 2017; 85:399-407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles

NASA Astrophysics Data System (ADS)

Hsu, Justin Bo-Kai; Huang, Kai-Yao; Weng, Tzu-Ya; Huang, Chien-Hsun; Lee, Tzong-Yi

2014-01-01

Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors). Alternative splicing, which is an important post-transcriptional regulation in eukaryotes, gives rise to multiple mature mRNA isoforms, which encodes proteins with functional diversities. However, the regulation of RNA splicing is not yet fully elucidated, partly because SRPs have not yet been exhaustively identified and the experimental identification is labor-intensive. Therefore, we are motivated to design a new method for identifying SRPs with their functional roles in the regulation of RNA splicing. The experimentally verified SRPs were manually curated from research articles. According to the functional annotation of Splicing Related Gene Database, the collected SRPs were further categorized into four functional groups including small nuclear Ribonucleoprotein, Splicing Factor, Splicing Regulation Factor and Novel Spliceosome Protein. The composition of amino acid pairs indicates that there are remarkable differences among four functional groups of SRPs. Then, support vector machines (SVMs) were utilized to learn the predictive models for identifying SRPs as well as their functional roles. The cross-validation evaluation presents that the SVM models trained with significant amino acid pairs and functional domains could provide a better predictive performance. In addition, the independent testing demonstrates that the proposed method could accurately identify SRPs in mammals/plants as well as effectively distinguish between SRPs and RNA-binding proteins. This investigation provides a practical means to identifying potential SRPs and a perspective for exploring the regulation of RNA splicing.

Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles.

PubMed

Hsu, Justin Bo-Kai; Huang, Kai-Yao; Weng, Tzu-Ya; Huang, Chien-Hsun; Lee, Tzong-Yi

2014-01-01

Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors). Alternative splicing, which is an important post-transcriptional regulation in eukaryotes, gives rise to multiple mature mRNA isoforms, which encodes proteins with functional diversities. However, the regulation of RNA splicing is not yet fully elucidated, partly because SRPs have not yet been exhaustively identified and the experimental identification is labor-intensive. Therefore, we are motivated to design a new method for identifying SRPs with their functional roles in the regulation of RNA splicing. The experimentally verified SRPs were manually curated from research articles. According to the functional annotation of Splicing Related Gene Database, the collected SRPs were further categorized into four functional groups including small nuclear Ribonucleoprotein, Splicing Factor, Splicing Regulation Factor and Novel Spliceosome Protein. The composition of amino acid pairs indicates that there are remarkable differences among four functional groups of SRPs. Then, support vector machines (SVMs) were utilized to learn the predictive models for identifying SRPs as well as their functional roles. The cross-validation evaluation presents that the SVM models trained with significant amino acid pairs and functional domains could provide a better predictive performance. In addition, the independent testing demonstrates that the proposed method could accurately identify SRPs in mammals/plants as well as effectively distinguish between SRPs and RNA-binding proteins. This investigation provides a practical means to identifying potential SRPs and a perspective for exploring the regulation of RNA splicing.

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico.

PubMed

Saucerman, Jeffrey J; Brunton, Laurence L; Michailova, Anushka P; McCulloch, Andrew D

2003-11-28

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico

NASA Technical Reports Server (NTRS)

Saucerman, Jeffrey J.; Brunton, Laurence L.; Michailova, Anushka P.; McCulloch, Andrew D.; McCullough, A. D. (Principal Investigator)

2003-01-01

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Acute toxicity prediction to threatened and endangered ...

EPA Pesticide Factsheets

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA’s Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species. Reporting on the development and optimization of ICE models for listed species toxicity estimation

Acute Toxicity Prediction to Threatened and Endangered Species Using Interspecies Correlation Estimation (ICE) Models.

PubMed

Willming, Morgan M; Lilavois, Crystal R; Barron, Mace G; Raimondo, Sandy

2016-10-04

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA's Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model.

PubMed

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model

PubMed Central

Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

2017-01-01

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies. PMID:28487828

Computational modelling and analysis of the molecular network regulating sporulation initiation in Bacillus subtilis.

PubMed

Ihekwaba, Adaoha E C; Mura, Ivan; Barker, Gary C

2014-10-24

Bacterial spores are important contaminants in food, and the spore forming bacteria are often implicated in food safety and food quality considerations. Spore formation is a complex developmental process involving the expression of more than 500 genes over the course of 6 to 8 hrs. The process culminates in the formation of resting cells capable of resisting environmental extremes and remaining dormant for long periods of time, germinating when conditions promote further vegetative growth. Experimental observations of sporulation and germination are problematic and time consuming so that reliable models are an invaluable asset in terms of prediction and risk assessment. In this report we develop a model which assists in the interpretation of sporulation dynamics. This paper defines and analyses a mathematical model for the network regulating Bacillus subtilis sporulation initiation, from sensing of sporulation signals down to the activation of the early genes under control of the master regulator Spo0A. Our model summarises and extends other published modelling studies, by allowing the user to execute sporulation initiation in a scenario where Isopropyl β-D-1-thiogalactopyranoside (IPTG) is used as an artificial sporulation initiator as well as in modelling the induction of sporulation in wild-type cells. The analysis of the model results and the comparison with experimental data indicate that the model is good at predicting inducible responses to sporulation signals. However, the model is unable to reproduce experimentally observed accumulation of phosphorelay sporulation proteins in wild type B. subtilis. This model also highlights that the phosphorelay sub-component, which relays the signals detected by the sensor kinases to the master regulator Spo0A, is crucial in determining the response dynamics of the system. We show that there is a complex connectivity between the phosphorelay features and the master regulatory Spo0A. Additional we discovered that the experimentally observed regulation of the phosphotransferase Spo0B for wild-type B. subtilis may be playing an important role in the network which suggests that modelling of sporulation initiation may require additional experimental support.

CFD modelling of sampling locations for early detection of spontaneous combustion in long-wall gob areas.

PubMed

Yuan, Liming; Smith, Alex C

In this study, computational fluid dynamics (CFD) modeling was conducted to optimize gas sampling locations for the early detection of spontaneous heating in longwall gob areas. Initial simulations were carried out to predict carbon monoxide (CO) concentrations at various regulators in the gob using a bleeder ventilation system. Measured CO concentration values at these regulators were then used to calibrate the CFD model. The calibrated CFD model was used to simulate CO concentrations at eight sampling locations in the gob using a bleederless ventilation system to determine the optimal sampling locations for early detection of spontaneous combustion.

Unique contributions of emotion regulation and executive functions in predicting the quality of parent-child interaction behaviors.

PubMed

Shaffer, Anne; Obradović, Jelena

2017-03-01

Parenting is a cognitive, emotional, and behavioral endeavor, yet limited research investigates parents' executive functions and emotion regulation as predictors of how parents interact with their children. The current study is a multimethod investigation of parental self-regulation in relation to the quality of parenting behavior and parent-child interactions in a diverse sample of parents and kindergarten-age children. Using path analyses, we tested how parent executive functions (inhibitory control) and lack of emotion regulation strategies uniquely relate to both sensitive/responsive behaviors and positive/collaborative behaviors during observed interaction tasks. In our analyses, we accounted for parent education, financial stress, and social support as socioeconomic factors that likely relate to parent executive function and emotion regulation skills. In a diverse sample of primary caregivers (N = 102), we found that direct assessment of parent inhibitory control was positively associated with sensitive/responsive behaviors, whereas parent self-reported difficulties in using emotion regulation strategies were associated with lower levels of positive and collaborative dyadic behaviors. Parent education and financial stress predicted inhibitory control, and social support predicted emotion regulation difficulties; parent education was also a significant predictor of sensitive/responsive behaviors. Greater inhibitory control skills and fewer difficulties identifying effective emotion regulation strategies were not significantly related in our final path model. We discuss our findings in the context of current and emerging parenting interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Self-Compassion, Emotion Regulation and Stress among Australian Psychologists: Testing an Emotion Regulation Model of Self-Compassion Using Structural Equation Modeling

PubMed Central

Finlay-Jones, Amy L.; Rees, Clare S.; Kane, Robert T.

2015-01-01

Psychologists tend to report high levels of occupational stress, with serious implications for themselves, their clients, and the discipline as a whole. Recent research suggests that self-compassion is a promising construct for psychologists in terms of its ability to promote psychological wellbeing and resilience to stress; however, the potential benefits of self-compassion are yet to be thoroughly explored amongst this occupational group. Additionally, while a growing body of research supports self-compassion as a key predictor of psychopathology, understanding of the processes by which self-compassion exerts effects on mental health outcomes is limited. Structural equation modelling (SEM) was used to test an emotion regulation model of self-compassion and stress among psychologists, including postgraduate trainees undertaking clinical work (n = 198). Self-compassion significantly negatively predicted emotion regulation difficulties and stress symptoms. Support was also found for our preliminary explanatory model of self-compassion, which demonstrates the mediating role of emotion regulation difficulties in the self-compassion-stress relationship. The final self-compassion model accounted for 26.2% of variance in stress symptoms. Implications of the findings and limitations of the study are discussed. PMID:26207900

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

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

Dash, Satyakam; Khodayari, Ali; Zhou, Jilai

Background. Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. Results. In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances,more » and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis–Menten kinetic parameters. Conclusions. The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.« less

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

DOE PAGES

Dash, Satyakam; Khodayari, Ali; Zhou, Jilai; ...

2017-05-02

Background. Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. Results. In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances,more » and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis–Menten kinetic parameters. Conclusions. The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.« less

Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling

PubMed Central

Li, Song; Assmann, Sarah M; Albert, Réka

2006-01-01

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited. PMID:16968132

Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

PubMed

Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

2016-01-01

Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

Testing bidirectional associations among emotion regulation strategies and substance use: a daily diary study.

PubMed

Weiss, Nicole H; Bold, Krysten W; Sullivan, Tami P; Armeli, Stephen; Tennen, Howard

2017-04-01

Alcohol and marijuana are widely used among college students. Emotion regulation strategies have been linked to alcohol and marijuana use, but little attention has been devoted to modeling the directionality of these associations. The aims of the current study were to test whether (a) daytime use of emotion regulation strategies influences the likelihood of evening substance use and (b) evening substance use influences the likelihood of next-day use of emotion regulation strategies. Longitudinal daily diary data were collected for 30 days via on-line surveys. Northeastern United States. A total of 1640 college students (mean age = 19.2 years, 54% female, 80% European American) were recruited each semester between Spring 2008 and Spring 2012. Daily diaries assessed emotion regulation strategies (distraction, reappraisal, problem-solving, avoidance) and substance use (any drinking, heavy drinking, marijuana use, co-use of any drinking/heavy drinking and marijuana). Covariates included gender, age, race/ethnicity, fraternity/sorority involvement and baseline depression. Daytime distraction [odds ratio (OR) = 0.95], reappraisal (OR = 0.95) and problem-solving (OR = 0.94) predicted lower odds of evening marijuana use (P-values < 0.02). Evening heavy drinking (OR = 0.90) and marijuana use (OR = 0.89) predicted lower odds of next-day problem-solving, with heavy drinking also predicting higher odds (OR = 1.08) of next-day avoidance and marijuana use also predicting higher odds (OR = 1.08) of next-day reappraisal (P-values < 0.03). There appear to be reciprocal relations among emotion regulation strategies and substance use: greater daytime use of distraction, reappraisal, and problem solving predicts lower evening substance use, while higher evening substance use predicts higher next-day avoidance and reappraisal but lower next-day problem-solving. © 2016 Society for the Study of Addiction.

Tuning stochastic matrix models with hydrologic data to predict the population dynamics of a riverine fish

USGS Publications Warehouse

Sakaris, P.C.; Irwin, E.R.

2010-01-01

We developed stochastic matrix models to evaluate the effects of hydrologic alteration and variable mortality on the population dynamics of a lotie fish in a regulated river system. Models were applied to a representative lotic fish species, the flathead catfish (Pylodictis olivaris), for which two populations were examined: a native population from a regulated reach of the Coosa River (Alabama, USA) and an introduced population from an unregulated section of the Ocmulgee River (Georgia, USA). Size-classified matrix models were constructed for both populations, and residuals from catch-curve regressions were used as indices of year class strength (i.e., recruitment). A multiple regression model indicated that recruitment of flathead catfish in the Coosa River was positively related to the frequency of spring pulses between 283 and 566 m3/s. For the Ocmulgee River population, multiple regression models indicated that year class strength was negatively related to mean March discharge and positively related to June low flow. When the Coosa population was modeled to experience five consecutive years of favorable hydrologic conditions during a 50-year projection period, it exhibited a substantial spike in size and increased at an overall 0.2% annual rate. When modeled to experience five years of unfavorable hydrologic conditions, the Coosa population initially exhibited a decrease in size but later stabilized and increased at a 0.4% annual rate following the decline. When the Ocmulgee River population was modeled to experience five years of favorable conditions, it exhibited a substantial spike in size and increased at an overall 0.4% annual rate. After the Ocmulgee population experienced five years of unfavorable conditions, a sharp decline in population size was predicted. However, the population quickly recovered, with population size increasing at a 0.3% annual rate following the decline. In general, stochastic population growth in the Ocmulgee River was more erratic and variable than population growth in the Coosa River. We encourage ecologists to develop similar models for other lotic species, particularly in regulated river systems. Successful management of fish populations in regulated systems requires that we are able to predict how hydrology affects recruitment and will ultimately influence the population dynamics of fishes. ?? 2010 by the Ecological Society of America.

Rate-Based Model Predictive Control of Turbofan Engine Clearance

NASA Technical Reports Server (NTRS)

DeCastro, Jonathan A.

2006-01-01

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

A Minimalistic Resource Allocation Model to Explain Ubiquitous Increase in Protein Expression with Growth Rate

PubMed Central

Keren, Leeat; Segal, Eran; Milo, Ron

2016-01-01

Most proteins show changes in level across growth conditions. Many of these changes seem to be coordinated with the specific growth rate rather than the growth environment or the protein function. Although cellular growth rates, gene expression levels and gene regulation have been at the center of biological research for decades, there are only a few models giving a base line prediction of the dependence of the proteome fraction occupied by a gene with the specific growth rate. We present a simple model that predicts a widely coordinated increase in the fraction of many proteins out of the proteome, proportionally with the growth rate. The model reveals how passive redistribution of resources, due to active regulation of only a few proteins, can have proteome wide effects that are quantitatively predictable. Our model provides a potential explanation for why and how such a coordinated response of a large fraction of the proteome to the specific growth rate arises under different environmental conditions. The simplicity of our model can also be useful by serving as a baseline null hypothesis in the search for active regulation. We exemplify the usage of the model by analyzing the relationship between growth rate and proteome composition for the model microorganism E.coli as reflected in recent proteomics data sets spanning various growth conditions. We find that the fraction out of the proteome of a large number of proteins, and from different cellular processes, increases proportionally with the growth rate. Notably, ribosomal proteins, which have been previously reported to increase in fraction with growth rate, are only a small part of this group of proteins. We suggest that, although the fractions of many proteins change with the growth rate, such changes may be partially driven by a global effect, not necessarily requiring specific cellular control mechanisms. PMID:27073913

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Hippocampal Erk Mechanisms Linking Prediction Error to Fear Extinction: Roles of Shock Expectancy and Contextual Aversive Valence

ERIC Educational Resources Information Center

Huh, Kyu Hwan; Guzman, Yomayra F.; Tronson, Natalie C.; Guedea, Anita L.; Gao, Can; Radulovic, Jelena

2009-01-01

Extinction of fear requires learning that anticipated aversive events no longer occur. Animal models reveal that sustained phosphorylation of the extracellular signal-regulated kinase (Erk) in hippocampal CA1 neurons plays an important role in this process. However, the key signals triggering and regulating the activity of Erk are not known. By…

Adolescents' Deliberate Self-Harm, Interpersonal Stress, and the Moderating Effects of Self-Regulation: A Two-Wave Longitudinal Analysis

ERIC Educational Resources Information Center

Jutengren, Goran; Kerr, Margaret; Stattin, Hakan

2011-01-01

The predictive effects of peer victimization and harsh parenting on deliberate self-harm were examined. As derived from the experiential avoidance model, the study also tested whether these links were moderated by individual self-regulation approaches. Data were collected at two points in time from 880 junior high school students (mean age =…

Predictive Effects of Good Self-Control and Poor Regulation on Alcohol-Related Outcomes: Do Protective Behavioral Strategies Mediate?

PubMed Central

Pearson, Matthew R.; Kite, Benjamin A.; Henson, James M.

2016-01-01

In the present study, we examined whether use of protective behavioral strategies mediated the relationship between self-control constructs and alcohol-related outcomes. According to the two-mode model of self-control, good self-control (planfulness; measured with Future Time Perspective, Problem Solving, and Self-Reinforcement) and poor regulation (impulsivity; measured with Present Time Perspective, Poor Delay of Gratification, Distractibility) are theorized to be relatively independent constructs rather than opposite ends of a single continuum. The analytic sample consisted of 278 college student drinkers (68% women) who responded to a battery of surveys at a single time point. Using a structural equation model based on the two-mode model of self-control, we found that good self-control predicted increased use of three types of protective behavioral strategies (Manner of Drinking, Limiting/Stopping Drinking, and Serious Harm Reduction). Poor regulation was unrelated to use of protective behavioral strategies, but had direct effects on alcohol use and alcohol problems. Further, protective behavioral strategies mediated the relationship between good self-control and alcohol use. The clinical implications of these findings are discussed. PMID:22663345

The use of predictive models to optimize risk of decisions.

PubMed

Baranyi, József; Buss da Silva, Nathália

2017-01-02

The purpose of this paper is to set up a mathematical framework that risk assessors and regulators could use to quantify the "riskiness" of a particular recommendation (choice/decision). The mathematical theory introduced here can be used for decision support systems. We point out that efficient use of predictive models in decision making for food microbiology needs to consider three major points: (1) the uncertainty and variability of the used information based on which the decision is to be made; (2) the validity of the predictive models aiding the assessor; and (3) the cost generated by the difference between the a-priory choice and the a-posteriori outcome. Copyright © 2016 Elsevier B.V. All rights reserved.

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

Regulation of persistent sodium currents by glycogen synthase kinase 3 encodes daily rhythms of neuronal excitability

NASA Astrophysics Data System (ADS)

Paul, Jodi R.; Dewoskin, Daniel; McMeekin, Laura J.; Cowell, Rita M.; Forger, Daniel B.; Gamble, Karen L.

2016-11-01

How neurons encode intracellular biochemical signalling cascades into electrical signals is not fully understood. Neurons in the central circadian clock in mammals provide a model system to investigate electrical encoding of biochemical timing signals. Here, using experimental and modelling approaches, we show how the activation of glycogen synthase kinase 3 (GSK3) contributes to neuronal excitability through regulation of the persistent sodium current (INaP). INaP exhibits a day/night difference in peak magnitude and is regulated by GSK3. Using mathematical modelling, we predict and confirm that GSK3 activation of INaP affects the action potential afterhyperpolarization, which increases the spontaneous firing rate without affecting the resting membrane potential. Together, these results demonstrate a crucial link between the molecular circadian clock and electrical activity, providing examples of kinase regulation of electrical activity and the propagation of intracellular signals in neuronal networks.

A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes.

PubMed

Ponitz, Claire Cameron; McClelland, Megan M; Matthews, J S; Morrison, Frederick J

2009-05-01

The authors examined a new assessment of behavioral regulation and contributions to achievement and teacher-rated classroom functioning in a sample (N = 343) of kindergarteners from 2 geographical sites in the United States. Behavioral regulation was measured with the Head-Toes-Knees-Shoulders (HTKS) task, a structured observation requiring children to perform the opposite of a dominant response to 4 different oral commands. Results revealed considerable variability in HTKS scores. Evidence for construct validity was found in positive correlations with parent ratings of attentional focusing and inhibitory control and teacher ratings of classroom behavioral regulation. Hierarchical linear modeling indicated that higher levels of behavioral regulation in the fall predicted stronger levels of achievement in the spring and better teacher-rated classroom self-regulation (all ps < .01) but not interpersonal skills. Evidence for domain specificity emerged, in which gains in behavioral regulation predicted gains in mathematics but not in language and literacy over the kindergarten year (p < .01) after site, child gender, and other background variables were controlled. Discussion focuses on the importance of behavioral regulation for successful adjustment to the demands of kindergarten. Copyright 2009 APA, all rights reserved

Kinesin-8 Motors Improve Nuclear Centering by Promoting Microtubule Catastrophe

NASA Astrophysics Data System (ADS)

Glunčić, Matko; Maghelli, Nicola; Krull, Alexander; Krstić, Vladimir; Ramunno-Johnson, Damien; Pavin, Nenad; Tolić, Iva M.

2015-02-01

In fission yeast, microtubules push against the cell edge, thereby positioning the nucleus in the cell center. Kinesin-8 motors regulate microtubule catastrophe; however, their role in nuclear positioning is not known. Here we develop a physical model that describes how kinesin-8 motors affect nuclear centering by promoting a microtubule catastrophe. Our model predicts the improved centering of the nucleus in the presence of motors, which we confirmed experimentally in living cells. The model also predicts a characteristic time for the recentering of a displaced nucleus, which is supported by our experiments where we displaced the nucleus using optical tweezers.

An Imbalance of Approach and Effortful Control Predicts Externalizing Problems: Support for Extending the Dual-Systems Model into Early Childhood.

PubMed

Jonas, Katherine; Kochanska, Grazyna

2018-01-25

Although the association between deficits in effortful control and later externalizing behavior is well established, many researchers (Nigg Journal of Child Psychology and Psychiatry, 47(3-4), 395-422, 2006; Steinberg Developmental Review, 28(1), 78-106, 2008) have hypothesized this association is actually the product of the imbalance of dual systems, or two underlying traits: approach and self-regulation. Very little research, however, has deployed a statistically robust strategy to examine that compelling model; further, no research has done so using behavioral measures, particularly in longitudinal studies. We examined the imbalance of approach and self-regulation (effortful control, EC) as predicting externalizing problems. Latent trait models of approach and EC were derived from behavioral measures collected from 102 children in a community sample at 25, 38, 52, and 67 months (2 to 5 ½ years), and used to predict externalizing behaviors, modeled as a latent trait derived from parent-reported measures at 80, 100, 123, and 147 months (6 ½ to 12 years). The imbalance hypothesis was supported: Children with an imbalance of approach and EC had more externalizing behavior problems in middle childhood and early preadolescence, relative to children with equal levels of the two traits.

To Regulate or Not to Regulate? Views on Electronic Cigarette Regulations and Beliefs about the Reasons for and against Regulation

PubMed Central

Sanders-Jackson, Ashley; Tan, Andy S. L.; Bigman, Cabral A.; Mello, Susan; Niederdeppe, Jeff

2016-01-01

Background Policies designed to restrict marketing, access to, and public use of electronic cigarettes (e-cigarettes) are increasingly under debate in various jurisdictions in the US. Little is known about public perceptions of these policies and factors that predict their support or opposition. Methods Using a sample of US adults from Amazon Mechanical Turk in May 2015, this paper identifies beliefs about the benefits and costs of regulating e-cigarettes and identifies which of these beliefs predict support for e-cigarette restricting policies. Results A higher proportion of respondents agreed with 8 different reasons to regulate e-cigarettes (48.5% to 83.3% agreement) versus 7 reasons not to regulate e-cigarettes (11.5% to 18.9%). The majority of participants agreed with 7 out of 8 reasons for regulation. When all reasons to regulate or not were included in a final multivariable model, beliefs about protecting people from secondhand vapor and protecting youth from trying e-cigarettes significantly predicted stronger support for e-cigarette restricting policies, whereas concern about government intrusion into individual choices was associated with reduced support. Discussion This research identifies key beliefs that may underlie public support or opposition to policies designed to regulate the marketing and use of e-cigarettes. Advocates on both sides of the issue may find this research valuable in developing strategic campaigns related to the issue. Implications Specific beliefs of potential benefits and costs of e-cigarette regulation (protecting youth, preventing exposure to secondhand vapor, and government intrusion into individual choices) may be effectively deployed by policy makers or health advocates in communicating with the public. PMID:27517716

To Regulate or Not to Regulate? Views on Electronic Cigarette Regulations and Beliefs about the Reasons for and against Regulation.

PubMed

Sanders-Jackson, Ashley; Tan, Andy S L; Bigman, Cabral A; Mello, Susan; Niederdeppe, Jeff

2016-01-01

Policies designed to restrict marketing, access to, and public use of electronic cigarettes (e-cigarettes) are increasingly under debate in various jurisdictions in the US. Little is known about public perceptions of these policies and factors that predict their support or opposition. Using a sample of US adults from Amazon Mechanical Turk in May 2015, this paper identifies beliefs about the benefits and costs of regulating e-cigarettes and identifies which of these beliefs predict support for e-cigarette restricting policies. A higher proportion of respondents agreed with 8 different reasons to regulate e-cigarettes (48.5% to 83.3% agreement) versus 7 reasons not to regulate e-cigarettes (11.5% to 18.9%). The majority of participants agreed with 7 out of 8 reasons for regulation. When all reasons to regulate or not were included in a final multivariable model, beliefs about protecting people from secondhand vapor and protecting youth from trying e-cigarettes significantly predicted stronger support for e-cigarette restricting policies, whereas concern about government intrusion into individual choices was associated with reduced support. This research identifies key beliefs that may underlie public support or opposition to policies designed to regulate the marketing and use of e-cigarettes. Advocates on both sides of the issue may find this research valuable in developing strategic campaigns related to the issue. Specific beliefs of potential benefits and costs of e-cigarette regulation (protecting youth, preventing exposure to secondhand vapor, and government intrusion into individual choices) may be effectively deployed by policy makers or health advocates in communicating with the public.

Toddler Emotion Regulation with Mothers and Fathers: Temporal Associations Between Negative Affect and Behavioral Strategies

PubMed Central

Ekas, Naomi V.; Braungart-Rieker, Julia M.; Lickenbrock, Diane M.; Zentall, Shannon R.; Maxwell, Scott M.

2010-01-01

The present study investigated temporal associations between putative emotion regulation strategies and negative affect in 20-month-old toddlers. Toddlers’ parent-focused, self-distraction, and toy-focused strategies, as well as negative affect, were rated on a second-by-second basis during laboratory parent-toddler interactions. Longitudinal mixed-effects models were conducted to determine the degree to which behavioral strategy use predicts subsequent negative affect and negative affect predicts subsequent strategy use. Results with mother-toddler and father-toddler dyads indicated that parent-focused strategies with an unresponsive parent were followed by increases in negative affect, whereas toy-focused strategies were followed by decreases in negative affect. Results also indicated that toddler negative affect serves to regulate behavioral strategy use within both parent contexts. PMID:21552335

Perceived autonomy support, motivation regulations and the self-evaluative tendencies of student dancers.

PubMed

Quested, Eleanor; Duda, Joan L

2011-03-01

Limited research has considered the social-environmental and motivational processes predictive of self evaluations and body-related concerns. Evidence suggests that low self-esteem, poor body evaluations, and associated anxieties are particularly prevalent among the student dance population. Grounded in self-determination theory (SDT), this study examined the relationships among perceptions of autonomy support, motivation regulations, and self-evaluations of body-related concerns in the context of vocational dance. Three hundred and ninety-two dancers completed questionnaires regarding their perceptions of autonomy support in their dance school, reasons for engaging in dance, self-esteem, social physique anxiety (SPA), and body dissatisfaction. Structural equation modeling analyses revealed that perceived autonomy support predicted intrinsic motivation (+) and amotivation (-). Extrinsic regulation positively predicted SPA. Amotivation mediated the associations between perceptions of autonomy support and dancers' self-esteem, SPA, and body dissatisfaction. The utility of SDT in understanding predictors of self-worth, physical evaluations, and associated concerns was supported. Moreover, this study provides preliminary evidence supporting the applicability of SDT in dance contexts.

Acute stress alters transcript expression pattern and reduces processing of proBDNF to mature BDNF in Dicentrarchus labrax

PubMed Central

2010-01-01

Background Stress involves alterations of brain functioning that may precipitate to mood disorders. The neurotrophin Brain Derived Neurotrophic Factor (BDNF) has recently been involved in stress-induced adaptation. BDNF is a key regulator of neuronal plasticity and adaptive processes. Regulation of BDNF is complex and may reflect not only stress-specific mechanisms but also hormonal and emotional responses. For this reason we used, as an animal model of stress, a fish whose brain organization is very similar to that of higher vertebrates, but is generally considered free of emotional reactions. Results We provide a comprehensive characterization of BDNF gene in the Dicentrarchus labrax and its transcriptional, translational and post-translational regulation following acute stress. While total BDNF mRNA levels are unchanged, BDNF transcripts 1c and 1d resulted down regulated after acute stress. Acute stress induces also a significant increase in proBDNF levels and reduction in mature BDNF suggesting altered regulation of proBDNF proteolytic processing. Notably, we provide here the first evidence that fishes possess a simplified proteolytic regulation of BDNF since the pro28Kda form, generated by the SKI-1 protease in mammals, is absent in fishes because the cleavage site has first emerged in reptilians. Finally, we show that the proBDNF/totBDNF ratio is a highly predictive novel quantitative biomarker to detect stress in fishes with sensitivity = 100%, specificity = 87%, and Negative Predictive Value = 100%. Conclusion The high predictivity of proBDNF/totBDNF ratio for stress in lower vertebrates indicates that processing of BDNF is a central mechanism in adaptation to stress and predicts that a similar regulation of pro/mature BDNF has likely been conserved throughout evolution of vertebrates from fish to man. PMID:20074340

Acute stress alters transcript expression pattern and reduces processing of proBDNF to mature BDNF in Dicentrarchus labrax.

PubMed

Tognoli, Chiara; Rossi, Federica; Di Cola, Francesco; Baj, Gabriele; Tongiorgi, Enrico; Terova, Genciana; Saroglia, Marco; Bernardini, Giovanni; Gornati, Rosalba

2010-01-14

Stress involves alterations of brain functioning that may precipitate to mood disorders. The neurotrophin Brain Derived Neurotrophic Factor (BDNF) has recently been involved in stress-induced adaptation. BDNF is a key regulator of neuronal plasticity and adaptive processes. Regulation of BDNF is complex and may reflect not only stress-specific mechanisms but also hormonal and emotional responses. For this reason we used, as an animal model of stress, a fish whose brain organization is very similar to that of higher vertebrates, but is generally considered free of emotional reactions. We provide a comprehensive characterization of BDNF gene in the Dicentrarchus labrax and its transcriptional, translational and post-translational regulation following acute stress. While total BDNF mRNA levels are unchanged, BDNF transcripts 1c and 1d resulted down regulated after acute stress. Acute stress induces also a significant increase in proBDNF levels and reduction in mature BDNF suggesting altered regulation of proBDNF proteolytic processing. Notably, we provide here the first evidence that fishes possess a simplified proteolytic regulation of BDNF since the pro28Kda form, generated by the SKI-1 protease in mammals, is absent in fishes because the cleavage site has first emerged in reptilians. Finally, we show that the proBDNF/totBDNF ratio is a highly predictive novel quantitative biomarker to detect stress in fishes with sensitivity = 100%, specificity = 87%, and Negative Predictive Value = 100%. The high predictivity of proBDNF/totBDNF ratio for stress in lower vertebrates indicates that processing of BDNF is a central mechanism in adaptation to stress and predicts that a similar regulation of pro/mature BDNF has likely been conserved throughout evolution of vertebrates from fish to man.

Thermodynamics-based models of transcriptional regulation with gene sequence.

PubMed

Wang, Shuqiang; Shen, Yanyan; Hu, Jinxing

2015-12-01

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.

PubMed

Bachmann, Julie; Raue, Andreas; Schilling, Marcel; Böhm, Martin E; Kreutz, Clemens; Kaschek, Daniel; Busch, Hauke; Gretz, Norbert; Lehmann, Wolf D; Timmer, Jens; Klingmüller, Ursula

2011-07-19

Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.

Modeling Dynamic Regulatory Processes in Stroke.

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

McDermott, Jason E.; Jarman, Kenneth D.; Taylor, Ronald C.

2012-10-11

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to developmore » dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.« less

Modeling Systems-Level Regulation of Host Immune Responses

PubMed Central

Thakar, Juilee; Pilione, Mylisa; Kirimanjeswara, Girish; Harvill, Eric T; Albert, Réka

2007-01-01

Many pathogens are able to manipulate the signaling pathways responsible for the generation of host immune responses. Here we examine and model a respiratory infection system in which disruption of host immune functions or of bacterial factors changes the dynamics of the infection. We synthesize the network of interactions between host immune components and two closely related bacteria in the genus Bordetellae. We incorporate existing experimental information on the timing of immune regulatory events into a discrete dynamic model, and verify the model by comparing the effects of simulated disruptions to the experimental outcome of knockout mutations. Our model indicates that the infection time course of both Bordetellae can be separated into three distinct phases based on the most active immune processes. We compare and discuss the effect of the species-specific virulence factors on disrupting the immune response during their infection of naive, antibody-treated, diseased, or convalescent hosts. Our model offers predictions regarding cytokine regulation, key immune components, and clearance of secondary infections; we experimentally validate two of these predictions. This type of modeling provides new insights into the virulence, pathogenesis, and host adaptation of disease-causing microorganisms and allows systems-level analysis that is not always possible using traditional methods. PMID:17559300

Identifying Stride-To-Stride Control Strategies in Human Treadmill Walking

PubMed Central

Dingwell, Jonathan B.; Cusumano, Joseph P.

2015-01-01

Variability is ubiquitous in human movement, arising from internal and external noise, inherent biological redundancy, and from the neurophysiological control actions that help regulate movement fluctuations. Increased walking variability can lead to increased energetic cost and/or increased fall risk. Conversely, biological noise may be beneficial, even necessary, to enhance motor performance. Indeed, encouraging more variability actually facilitates greater improvements in some forms of locomotor rehabilitation. Thus, it is critical to identify the fundamental principles humans use to regulate stride-to-stride fluctuations in walking. This study sought to determine how humans regulate stride-to-stride fluctuations in stepping movements during treadmill walking. We developed computational models based on pre-defined goal functions to compare if subjects, from each stride to the next, tried to maintain the same speed as the treadmill, or instead stay in the same position on the treadmill. Both strategies predicted average behaviors empirically indistinguishable from each other and from that of humans. These strategies, however, predicted very different stride-to-stride fluctuation dynamics. Comparisons to experimental data showed that human stepping movements were generally well-predicted by the speed-control model, but not by the position-control model. Human subjects also exhibited no indications they corrected deviations in absolute position only intermittently: i.e., closer to the boundaries of the treadmill. Thus, humans clearly do not adopt a control strategy whose primary goal is to maintain some constant absolute position on the treadmill. Instead, humans appear to regulate their stepping movements in a way most consistent with a strategy whose primary goal is to try to maintain the same speed as the treadmill at each consecutive stride. These findings have important implications both for understanding how biological systems regulate walking in general and for being able to harness these mechanisms to develop more effective rehabilitation interventions to improve locomotor performance. PMID:25910253

Promoter architecture dictates cell-to-cell variability in gene expression.

PubMed

Jones, Daniel L; Brewster, Robert C; Phillips, Rob

2014-12-19

Variability in gene expression among genetically identical cells has emerged as a central preoccupation in the study of gene regulation; however, a divide exists between the predictions of molecular models of prokaryotic transcriptional regulation and genome-wide experimental studies suggesting that this variability is indifferent to the underlying regulatory architecture. We constructed a set of promoters in Escherichia coli in which promoter strength, transcription factor binding strength, and transcription factor copy numbers are systematically varied, and used messenger RNA (mRNA) fluorescence in situ hybridization to observe how these changes affected variability in gene expression. Our parameter-free models predicted the observed variability; hence, the molecular details of transcription dictate variability in mRNA expression, and transcriptional noise is specifically tunable and thus represents an evolutionarily accessible phenotypic parameter. Copyright © 2014, American Association for the Advancement of Science.

Integrating discrete stochastic models and single-cell experiments to infer predictive models of MAPK-induced transcription dynamics

NASA Astrophysics Data System (ADS)

Munsky, Brian

2015-03-01

MAPK signal-activated transcription plays central roles in myriad biological processes including stress adaptation responses and cell fate decisions. Recent single-cell and single-molecule experiments have advanced our ability to quantify the spatial, temporal, and stochastic fluctuations for such signals and their downstream effects on transcription regulation. This talk explores how integrating such experiments with discrete stochastic computational analyses can yield quantitative and predictive understanding of transcription regulation in both space and time. We use single-molecule mRNA fluorescence in situ hybridization (smFISH) experiments to reveal locations and numbers of multiple endogenous mRNA species in 100,000's of individual cells, at different times and under different genetic and environmental perturbations. We use finite state projection methods to precisely and efficiently compute the full joint probability distributions of these mRNA, which capture measured spatial, temporal and correlative fluctuations. By combining these experimental and computational tools with uncertainty quantification, we systematically compare models of varying complexity and select those which give optimally precise and accurate predictions in new situations. We use these tools to explore two MAPK-activated gene regulation pathways. In yeast adaptation to osmotic shock, we analyze Hog1 kinase activation of transcription for three different genes STL1 (osmotic stress), CTT1 (oxidative stress) and HSP12 (heat shock). In human osteosarcoma cells under serum induction, we analyze ERK activation of c-Fos transcription.

Models for financing the regulation of pharmaceutical promotion

PubMed Central

2012-01-01

Pharmaceutical companies spend huge sums promoting their products whereas regulation of promotional activities is typically underfinanced. Any option for financing the monitoring and regulation of promotion should adhere to three basic principles: stability, predictability and lack of (perverse) ties between the level of financing and performance. This paper explores the strengths and weaknesses of six different models. All these six models considered here have positive and negative features and none may necessarily be ideal in any particular country. Different countries may choose to utilize a combination of two or more of these models in order to raise sufficient revenue. Financing of regulation of drug promotion should more than pay for itself through the prevention of unnecessary drug costs and the avoidance of adverse health effects due to inappropriate prescribing. However, it involves an initial outlay of money that is currently not being spent and many national governments, in both rich and poor countries, are unwilling to incur extra costs. PMID:22784944

The interaction between self-regulation and motivation prospectively predicting problem behavior in adolescence.

PubMed

Rhodes, Jessica D; Colder, Craig R; Trucco, Elisa M; Speidel, Carolyn; Hawk, Larry W; Lengua, Liliana J; Das Eiden, Rina; Wieczorek, William

2013-01-01

A large literature suggests associations between self-regulation and motivation and adolescent problem behavior; however, this research has mostly pitted these constructs against one another or tested them in isolation. Following recent neural-systems based theories (e.g., Ernst & Fudge, 2009 ), the present study investigated the interactions between self-regulation and approach and avoidance motivation prospectively predicting delinquency and depressive symptoms in early adolescence. The community sample included 387 adolescents aged 11 to 13 years old (55% female; 17% minority). Laboratory tasks were used to assess self-regulation and approach and avoidance motivation, and adolescent self-reports were used to measure depressive symptoms and delinquency. Analyses suggested that low levels of approach motivation were associated with high levels of depressive symptoms, but only at high levels of self-regulation (p = .01). High levels of approach were associated with high levels of rule breaking, but only at low levels of self-regulation (p < .05). These findings support contemporary neural-based systems theories that posit integration of motivational and self-regulatory individual differences via moderational models to understand adolescent problem behavior.

The Interaction Between Self-Regulation and Motivation Prospectively Predicting Problem Behavior in Adolescence

PubMed Central

Rhodes, Jessica D.; Colder, Craig R.; Trucco, Elisa M.; Speidel, Carolyn; Hawk, Larry W.; Lengua, Liliana J.; Eiden, Rina Das; Wiezcorek, William

2013-01-01

Objective A large literature suggests associations between self-regulation and motivation and adolescent problem behavior, however this research has mostly pitted these constructs against one another or tested them in isolation. Following recent neural-systems based theories (e.g., Ernst & Fudge, 2009), the present study investigated the interactions between self-regulation and approach and avoidance motivation prospectively predicting delinquency and depressive symptoms in early adolescence. Method The community sample included 387 adolescents aged 11–13 years old (55% female; 17% minority). Laboratory tasks were used to assess self-regulation and approach and avoidance motivation, and adolescent self-reports were used to measure depressive symptoms and delinquency. Results Analyses suggested that low levels of approach motivation were associated with high levels of depressive symptoms, but only at high levels of self-regulation (p = .01). High levels of approach were associated with high levels of rule breaking, but only at low levels of self-regulation (p < .05). Conclusions These findings support contemporary neural-based systems theories that posit integration of motivational and self-regulatory individual differences via moderational models to understand adolescent problem behavior. PMID:23477426

BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals.

PubMed

Kim, Minsuk; Sun, Gwanggyu; Lee, Dong-Yup; Kim, Byung-Gee

2017-01-01

Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models. We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets. MATLAB code is available at https://github.com/kms1041/BeReTa (github). byungkim@snu.ac.krSupplementary information: 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.

A multi-level model accounting for the effects of JAK2-STAT5 signal modulation in erythropoiesis.

PubMed

Lai, Xin; Nikolov, Svetoslav; Wolkenhauer, Olaf; Vera, Julio

2009-08-01

We develop a multi-level model, using ordinary differential equations, based on quantitative experimental data, accounting for murine erythropoiesis. At the sub-cellular level, the model includes a description of the regulation of red blood cell differentiation through Epo-stimulated JAK2-STAT5 signalling activation, while at the cell population level the model describes the dynamics of (STAT5-mediated) red blood cell differentiation from their progenitors. Furthermore, the model includes equations depicting the hypoxia-mediated regulation of hormone erythropoietin blood levels. Take all together, the model constitutes a multi-level, feedback loop-regulated biological system, involving processes in different organs and at different organisational levels. We use our model to investigate the effect of deregulation in the proteins involved in the JAK2-STAT5 signalling pathway in red blood cells. Our analysis results suggest that down-regulation in any of the three signalling system components affects the hematocrit level in an individual considerably. In addition, our analysis predicts that exogenous Epo injection (an already existing treatment for several blood diseases) may compensate the effects of single down-regulation of Epo hormone level, STAT5 or EpoR/JAK2 expression level, and that it may be insufficient to counterpart a combined down-regulation of all the elements in the JAK2-STAT5 signalling cascade.

Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

PubMed

Gu, Quan; Nagaraj, Shivashankar H; Hudson, Nicholas J; Dalrymple, Brian P; Reverter, Antonio

2011-01-12

Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information. We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively. The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

A Linear Empirical Model of Self-Regulation on Flourishing, Health, Procrastination, and Achievement, Among University Students

PubMed Central

Garzón-Umerenkova, Angélica; de la Fuente, Jesús; Amate, Jorge; Paoloni, Paola V.; Fadda, Salvatore; Pérez, Javier Fiz

2018-01-01

This research aimed to analyze the linear bivariate correlation and structural relations between self-regulation -as a central construct-, with flow, health, procrastination and academic performance, in an academic context. A total of 363 college students took part, 101 men (27.8%) and 262 women (72.2%). Participants had an average age of 22 years and were between the first and fifth year of studies. They were from five different programs and two universities in Bogotá city (Colombia). A validated ad hoc questionnaire of physical and psychological health was applied along with a battery of tests to measure self-regulation, procrastination, and flourishing. To establish an association relationship, Pearson bivariate correlations were performed using SPSS software (v. 22.0), and structural relationship predictive analysis was performed using an SEM on AMOS software (v. 22.0). Regarding this linear association, it was established that (1) self-regulation has a significant positive association on flourishing and overall health, and a negative effect on procrastination. Regarding the structural relation, it confirmed that (2) self-regulation is a direct and positive predictor of flourishing and health; (3) self-regulation predicts procrastination directly and negatively, and academic performance indirectly and positively; and (4) age and gender have a prediction effect on the analyzed variables. Implications, limitations and future research scope are discussed. PMID:29706922

A Linear Empirical Model of Self-Regulation on Flourishing, Health, Procrastination, and Achievement, Among University Students.

PubMed

Garzón-Umerenkova, Angélica; de la Fuente, Jesús; Amate, Jorge; Paoloni, Paola V; Fadda, Salvatore; Pérez, Javier Fiz

2018-01-01

This research aimed to analyze the linear bivariate correlation and structural relations between self-regulation -as a central construct-, with flow, health, procrastination and academic performance, in an academic context. A total of 363 college students took part, 101 men (27.8%) and 262 women (72.2%). Participants had an average age of 22 years and were between the first and fifth year of studies. They were from five different programs and two universities in Bogotá city (Colombia). A validated ad hoc questionnaire of physical and psychological health was applied along with a battery of tests to measure self-regulation, procrastination, and flourishing. To establish an association relationship, Pearson bivariate correlations were performed using SPSS software (v. 22.0), and structural relationship predictive analysis was performed using an SEM on AMOS software (v. 22.0). Regarding this linear association, it was established that (1) self-regulation has a significant positive association on flourishing and overall health, and a negative effect on procrastination. Regarding the structural relation, it confirmed that (2) self-regulation is a direct and positive predictor of flourishing and health; (3) self-regulation predicts procrastination directly and negatively, and academic performance indirectly and positively; and (4) age and gender have a prediction effect on the analyzed variables. Implications, limitations and future research scope are discussed.

Rainfall and temperature distinguish between Karnal bunt positive and negative years in wheat fields in Texas.

PubMed

Workneh, F; Allen, T W; Nash, G H; Narasimhan, B; Srinivasan, R; Rush, C M

2008-01-01

Karnal bunt of wheat, caused by the fungus Tilletia indica, is an internationally regulated disease. Since its first detection in central Texas in 1997, regions in which the disease was detected have been under strict federal quarantine regulations resulting in significant economic losses. A study was conducted to determine the effect of weather factors on incidence of the disease since its first detection in Texas. Weather variables (temperature and rainfall amount and frequency) were collected and used as predictors in discriminant analysis for classifying bunt-positive and -negative fields using incidence data for 1997 and 2000 to 2003 in San Saba County. Rainfall amount and frequency were obtained from radar (Doppler radar) measurements. The three weather variables correctly classified 100% of the cases into bunt-positive or -negative fields during the specific period overlapping the stage of wheat susceptibility (boot to soft dough) in the region. A linear discriminant-function model then was developed for use in classification of new weather variables into the bunt occurrence groups (+ or -). The model was evaluated using weather data for 2004 to 2006 for San Saba area (central Texas), and data for 2001 and 2002 for Olney area (north-central Texas). The model correctly predicted bunt occurrence in all cases except for the year 2004. The model was also evaluated for site-specific prediction of the disease using radar rainfall data and in most cases provided similar results as the regional level evaluation. The humid thermal index (HTI) model (widely used for assessing risk of Karnal bunt) agreed with our model in all cases in the regional level evaluation, including the year 2004 for the San Saba area, except for the Olney area where it incorrectly predicted weather conditions in 2001 as unfavorable. The current model has a potential to be used in a spray advisory program in regulated wheat fields.

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

DOE PAGES

Williams, Alex H.; O'Donnell, Cian; Sejnowski, Terrence J.; ...

2016-12-30

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’. Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimatesmore » of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. In conclusion, these findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons.« less

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

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

Williams, Alex H.; O'Donnell, Cian; Sejnowski, Terrence J.

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’. Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimatesmore » of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. In conclusion, these findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons.« less

A quantitative model of honey bee colony population dynamics.

PubMed

Khoury, David S; Myerscough, Mary R; Barron, Andrew B

2011-04-18

Since 2006 the rate of honey bee colony failure has increased significantly. As an aid to testing hypotheses for the causes of colony failure we have developed a compartment model of honey bee colony population dynamics to explore the impact of different death rates of forager bees on colony growth and development. The model predicts a critical threshold forager death rate beneath which colonies regulate a stable population size. If death rates are sustained higher than this threshold rapid population decline is predicted and colony failure is inevitable. The model also predicts that high forager death rates draw hive bees into the foraging population at much younger ages than normal, which acts to accelerate colony failure. The model suggests that colony failure can be understood in terms of observed principles of honey bee population dynamics, and provides a theoretical framework for experimental investigation of the problem.

Learning and adaptation in the management of waterfowl harvests

USGS Publications Warehouse

Johnson, Fred A.

2011-01-01

A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

Price-cap Regulation, Uncertainty and the Price Evolution of New Pharmaceuticals.

PubMed

Shajarizadeh, Ali; Hollis, Aidan

2015-08-01

This paper examines the effect of the regulations restricting price increases on the evolution of pharmaceutical prices. A novel theoretical model shows that this policy leads firms to price new drugs with uncertain demand above the expected value initially. Price decreases after drug launch are more likely, the higher the uncertainty. We empirically test the model's predictions using data from the Canadian pharmaceutical market. The level of uncertainty is shown to play a crucial role in drug pricing strategies. © 2014 The Authors. Health Economics Published by John Wiley & Sons Ltd.

What can 35 years and over 700,000 measurements tell us about noise exposure in the mining industry?

PubMed

Roberts, Benjamin; Sun, Kan; Neitzel, Richard L

2017-01-01

To analyse over 700,000 cross-sectional measurements from the Mine Safety and Health Administration (MHSA) and develop statistical models to predict noise exposure for a worker. Descriptive statistics were used to summarise the data. Two linear regression models were used to predict noise exposure based on MSHA-permissible exposure limit (PEL) and action level (AL), respectively. Twofold cross validation was used to compare the exposure estimates from the models to actual measurement. The mean difference and t-statistic was calculated for each job title to determine whether the model predictions were significantly different from the actual data. Measurements were acquired from MSHA through a Freedom of Information Act request. From 1979 to 2014, noise exposure has decreased. Measurements taken before the implementation of MSHA's revised noise regulation in 2000 were on average 4.5 dBA higher than after the law was implemented. Both models produced exposure predictions that were less than 1 dBA different than the holdout data. Overall noise levels in mines have been decreasing. However, this decrease has not been uniform across all mining sectors. The exposure predictions from the model will be useful to help predict hearing loss in workers in the mining industry.

Predicting preschoolers' social-cognitive play behavior: attachment, peers, temperament, and physiological regulation.

PubMed

Porter, Christin L

2009-04-01

Research on children's social-cognitive play typologies (i.e., active and passive forms of solitary and social play) suggests links of early play behaviors and later social development and risk status. To date, few studies have examined simultaneously suspected links between children's social-cognitive play types and factors believed to shape these early social-play behaviors. This study examined a simultaneous model of individual (temperament, physiology) and relational variables (attachment, peer networks) believed to influence children's social-cognitive play types, including individual characteristics drawn from the Child Behavior Questionnaire which measures dimensions of shyness and impulsivity, a lab-based assessment of social withdrawal, and physiological markers linked to social regulation (cardiac vagal tone and vagal regulation). Children's attachment status to parents was gathered using Q-Sort methodology, and a measure of previous peer network size was obtained from parents' reports to examine potential links between relational history and social-cognitive play types. Predictive discriminant function analysis showed that children's (N = 54, age range 35 to 58 months) social-cognitive play was better predicted on the basis of multiple independent variables than individual, zero-order relations. When predicting children's social-cognitive play typologies, a multidimensional view which encompasses both individual characteristics and social-relational variables may best predict social -cognitive play types and help understanding of children's social trajectories.

Internal working models and adjustment of physically abused children: the mediating role of self-regulatory abilities.

PubMed

Hawkins, Amy L; Haskett, Mary E

2014-01-01

Abused children's internal working models (IWM) of relationships are known to relate to their socioemotional adjustment, but mechanisms through which negative representations increase vulnerability to maladjustment have not been explored. We sought to expand the understanding of individual differences in IWM of abused children and investigate the mediating role of self-regulation in links between IWM and adjustment. Cluster analysis was used to subgroup 74 physically abused children based on their IWM. Internal working models were identified by children's representations, as measured by a narrative story stem task. Self-regulation was assessed by teacher report and a behavioral task, and adjustment was measured by teacher report. Cluster analyses indicated two subgroups of abused children with distinct patterns of IWMs. Cluster membership predicted internalizing and externalizing problems. Associations between cluster membership and adjustment were mediated by children's regulation, as measured by teacher reports of many aspects of regulation. There was no support for mediation when regulation was measured by a behavioral task that tapped more narrow facets of regulation. Abused children exhibit clinically relevant individual differences in their IWMs; these models are linked to adjustment in the school setting, possibly through children's self-regulation. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

Investigation of the Self-Regulated Learning Strategies of Students from the Faculty of Education Using Ordinal Logistic Regression Analysis

ERIC Educational Resources Information Center

Bozpolat, Ebru

2016-01-01

The purpose of this study was to reveal whether the low, medium, and high level self-regulated learning strategies of third year students at the Education Faculty of Cumhuriyet University can be predicted by the variables of gender, academic self-efficacy, and general academic average. The study uses the Relational Screening Model. The dependent…

The Relationships among Students' Future-Oriented Goals and Subgoals, Perceived Task Instrumentality, and Task-Oriented Self-Regulation Strategies in an Academic Environment

ERIC Educational Resources Information Center

Tabachnick, Sharon E.; Miller, Raymond B.; Relyea, George E.

2008-01-01

The authors performed path analysis, followed by a bootstrap procedure, to test the predictions of a model explaining the relationships among students' distal future goals (both extrinsic and intrinsic), their adoption of a middle-range subgoal, their perceptions of task instrumentality, and their proximal task-oriented self-regulation strategies.…

Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models

NASA Astrophysics Data System (ADS)

Kovač-Andrić, Elvira; Sheta, Alaa; Faris, Hossam; Gajdošik, Martina Šrajer

2016-07-01

Ozone is one of the most significant secondary pollutants with numerous negative effects on human health and environment including plants and vegetation. Therefore, more effort is made recently by governments and associations to predict ozone concentrations which could help in establishing better plans and regulation for environment protection. In this study, we use two Artificial Neural Network based approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models, one for urban and another one for rural area in the eastern part of Croatia. The evaluation of actual against the predicted ozone concentrations revealed that MLP and RBF models are very competitive for the training and testing data in the case of Kopački Rit area whereas in the case of Osijek city, MLP shows better evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequent feature selection process has improved the prediction power of RBF network.

The roles of community biomass and species pools in the regulation of plant diversity

USGS Publications Warehouse

Grace, J.B.

2001-01-01

Considerable debate has developed over the importance of community biomass and species pools in the regulation of community diversity. Attempts to explain patterns of plant diversity as a function of community biomass or productivity have been only partially successful and in general, have explained only a fraction of the observed variation in diversity. At the same time studies that have focused on the importance of species pools have led some to conclude that diversity is primarily regulated in the short term by the size of the species pool rather than by biotic interactions. In this paper, I explore how community biomass and species pools may work in combination to regulate diversity in herbaceous plant communities. To address this problem, I employ a simple model in which the dynamics of species richness are a function of aboveground community biomass and environmentally controlled gradients in species pools. Model results lead to two main predictions about the role of biomass regulation: (1) Seasonal dynamics of richness will tend to follow a regular oscillation, with richness rising to peak values during the early to middle portion of the growing season and then declining during the latter part of the season. (2.) Seasonal dieback of aboveground tissues facilitates the long-term maintenance of high levels of richness in the community. The persistence of aboveground tissues and accumulation of litter are especially important in limiting the number of species through the suppression of recruitment. Model results also lead to two main predictions about the role of species pools: (1) The height and position of peak richness relative to community biomass will be influenced by the rate at which the species pool increases as available soil resources increase. (2) Variations in nonresource environmental factors (e.g. soil pH or soil salinity) have the potential to regulate species pools in a way that is uncorrelated with aboveground biomass. Under extreme conditions, such nonresource effects can create a unimodal envelope of biomass-richness values. Available evidence from the literature provides partial support for these predictions, though additional data are needed to provide more convincing tests.

Problematic Internet use and problematic alcohol use from the cognitive-behavioral model: a longitudinal study among adolescents.

PubMed

Gámez-Guadix, Manuel; Calvete, Esther; Orue, Izaskun; Las Hayas, Carlota

2015-01-01

Problematic Internet use (PIU) and problematic alcohol use are two pervasive problems during adolescence that share similar characteristics and predictors. The first objective of this study was to analyze the temporal and reciprocal relationships among the main components of PIU from the cognitive-behavioral model (preference for online social interaction, mood regulation through the Internet, deficient self-regulation, and negative consequences). The second objective was to examine the temporal and reciprocal relationships between PIU components and problematic alcohol use. We also examined whether these relationships differ between males and females. The sample comprised 801 Spanish adolescents (mean age=14.92, SD=1.01) who completed the measures both at Time 1 (T1) and Time 2 (T2) six months apart. We used structural equation modeling to analyze the relationship among the variables. Results showed that deficient self-regulation at T1 predicted an increase in preference for online interactions, mood regulation, and negative consequences of the Internet at T2. In turn, the emergence of negative consequences of PIU at T1 predicted a rise in problematic alcohol use at T2. Longitudinal relationships between different components of PIU and between the components of PIU and problematic alcohol use were invariant across genders. Deficient self-regulation, consisting of diminished self-control over cognition and behaviors related to the Internet, plays a central role in the maintenance of PIU, increasing the preference for online interactions, mood regulation, and negative consequences from Internet use over time. In turn, adolescents who present negative consequences of PIU are vulnerable targets for problematic alcohol use. Copyright © 2014 Elsevier Ltd. All rights reserved.

A New Approach to Identifying the Drivers of Regulation Compliance Using Multivariate Behavioural Models

PubMed Central

Thomas, Alyssa S.; Milfont, Taciano L.; Gavin, Michael C.

2016-01-01

Non-compliance with fishing regulations can undermine management effectiveness. Previous bivariate approaches were unable to untangle the complex mix of factors that may influence fishers’ compliance decisions, including enforcement, moral norms, perceived legitimacy of regulations and the behaviour of others. We compared seven multivariate behavioural models of fisher compliance decisions using structural equation modeling. An online survey of over 300 recreational fishers tested the ability of each model to best predict their compliance with two fishing regulations (daily and size limits). The best fitting model for both regulations was composed solely of psycho-social factors, with social norms having the greatest influence on fishers’ compliance behaviour. Fishers’ attitude also directly affected compliance with size limit, but to a lesser extent. On the basis of these findings, we suggest behavioural interventions to target social norms instead of increasing enforcement for the focal regulations in the recreational blue cod fishery in the Marlborough Sounds, New Zealand. These interventions could include articles in local newspapers and fishing magazines highlighting the extent of regulation compliance as well as using respected local fishers to emphasize the benefits of compliance through public meetings or letters to the editor. Our methodological approach can be broadly applied by natural resource managers as an effective tool to identify drivers of compliance that can then guide the design of interventions to decrease illegal resource use. PMID:27727292

Coordinated action of histone modification and microRNA regulations in human genome.

PubMed

Wang, Xuan; Zheng, Guantao; Dong, Dong

2015-10-10

Both histone modifications and microRNAs (miRNAs) play pivotal role in gene expression regulation. Although numerous studies have been devoted to explore the gene regulation by miRNA and epigenetic regulations, their coordinated actions have not been comprehensively examined. In this work, we systematically investigated the combinatorial relationship between miRNA and epigenetic regulation by taking advantage of recently published whole genome-wide histone modification data and high quality miRNA targeting data. The results showed that miRNA targets have distinct histone modification patterns compared with non-targets in their promoter regions. Based on this finding, we proposed a machine learning approach to fit predictive models on the task to discern whether a gene is targeted by a specific miRNA. We found a considerable advantage in both sensitivity and specificity in diverse human cell lines. Finally, we found that our predicted miRNA targets are consistently annotated with Gene Ontology terms. Our work is the first genome-wide investigation of the coordinated action of miRNA and histone modification regulations, which provide a guide to deeply understand the complexity of transcriptional regulation. Copyright © 2015 Elsevier B.V. All rights reserved.

Sting, Carry and Stock: How Corpse Availability Can Regulate De-Centralized Task Allocation in a Ponerine Ant Colony

PubMed Central

Schmickl, Thomas; Karsai, Istvan

2014-01-01

We develop a model to produce plausible patterns of task partitioning in the ponerine ant Ectatomma ruidum based on the availability of living prey and prey corpses. The model is based on the organizational capabilities of a “common stomach” through which the colony utilizes the availability of a natural (food) substance as a major communication channel to regulate the income and expenditure of the very same substance. This communication channel has also a central role in regulating task partitioning of collective hunting behavior in a supply&demand-driven manner. Our model shows that task partitioning of the collective hunting behavior in E. ruidum can be explained by regulation due to a common stomach system. The saturation of the common stomach provides accessible information to individual ants so that they can adjust their hunting behavior accordingly by engaging in or by abandoning from stinging or transporting tasks. The common stomach is able to establish and to keep stabilized an effective mix of workforce to exploit the prey population and to transport food into the nest. This system is also able to react to external perturbations in a de-centralized homeostatic way, such as to changes in the prey density or to accumulation of food in the nest. In case of stable conditions the system develops towards an equilibrium concerning colony size and prey density. Our model shows that organization of work through a common stomach system can allow Ectatomma ruidum to collectively forage for food in a robust, reactive and reliable way. The model is compared to previously published models that followed a different modeling approach. Based on our model analysis we also suggest a series of experiments for which our model gives plausible predictions. These predictions are used to formulate a set of testable hypotheses that should be investigated empirically in future experimentation. PMID:25493558

Toddler signaling regulates mesodermal cell migration downstream of Nodal signaling

PubMed Central

Norris, Megan L; Pauli, Andrea; Gagnon, James A; Lord, Nathan D; Rogers, Katherine W; Mosimann, Christian; Zon, Leonard I

2017-01-01

Toddler/Apela/Elabela is a conserved secreted peptide that regulates mesendoderm development during zebrafish gastrulation. Two non-exclusive models have been proposed to explain Toddler function. The ‘specification model’ postulates that Toddler signaling enhances Nodal signaling to properly specify endoderm, whereas the ‘migration model’ posits that Toddler signaling regulates mesendodermal cell migration downstream of Nodal signaling. Here, we test key predictions of both models. We find that in toddler mutants Nodal signaling is initially normal and increasing endoderm specification does not rescue mesendodermal cell migration. Mesodermal cell migration defects in toddler mutants result from a decrease in animal pole-directed migration and are independent of endoderm. Conversely, endodermal cell migration defects are dependent on a Cxcr4a-regulated tether of the endoderm to mesoderm. These results suggest that Toddler signaling regulates mesodermal cell migration downstream of Nodal signaling and indirectly affects endodermal cell migration via Cxcr4a-signaling. PMID:29117894

A Comparison of Autonomous Regulation and Negative Self-Evaluative Emotions as Predictors of Smoking Behavior Change among College Students

PubMed Central

Lee, Hyoung S.; Catley, Delwyn; Harris, Kari Jo

2011-01-01

This study compared autonomous self-regulation and negative self-evaluative emotions as predictors of smoking behavior change in college student smokers (N=303) in a smoking cessation intervention study. Although the two constructs were moderately correlated, latent growth curve modeling revealed that only autonomous regulation, but not negative self-evaluative emotions, was negatively related to the number of days smoked. Results suggest that the two variables tap different aspects of motivation to change smoking behaviors, and that autonomous regulation predicts smoking behavior change better than negative self-evaluative emotions. PMID:21911436

A comparison of autonomous regulation and negative self-evaluative emotions as predictors of smoking behavior change among college students.

PubMed

Lee, Hyoung S; Catley, Delwyn; Harris, Kari Jo

2012-05-01

This study compared autonomous self-regulation and negative self-evaluative emotions as predictors of smoking behavior change in college student smokers (N = 303) in a smoking cessation intervention study. Although the two constructs were moderately correlated, latent growth curve modeling revealed that only autonomous regulation, but not negative self-evaluative emotions, was negatively related to the number of days smoked. Results suggest that the two variables tap different aspects of motivation to change smoking behaviors, and that autonomous regulation predicts smoking behavior change better than negative self-evaluative emotions.

Effects of adenosine triphosphate concentration on motor force regulation during skeletal muscle contraction

NASA Astrophysics Data System (ADS)

Wei, J.; Dong, C.; Chen, B.

2017-04-01

We employ a mechanical model of sarcomere to quantitatively investigate how adenosine triphosphate (ATP) concentration affects motor force regulation during skeletal muscle contraction. Our simulation indicates that there can be negative cross-bridges resisting contraction within the sarcomere and higher ATP concentration would decrease the resistance force from negative cross-bridges by promoting their timely detachment. It is revealed that the motor force is well regulated only when ATP concentration is above a certain level. These predictions may provide insights into the role of ATP in regulating coordination among multiple motors.

Summary of the key features of seven biomathematical models of human fatigue and performance.

PubMed

Mallis, Melissa M; Mejdal, Sig; Nguyen, Tammy T; Dinges, David F

2004-03-01

Biomathematical models that quantify the effects of circadian and sleep/wake processes on the regulation of alertness and performance have been developed in an effort to predict the magnitude and timing of fatigue-related responses in a variety of contexts (e.g., transmeridian travel, sustained operations, shift work). This paper summarizes key features of seven biomathematical models reviewed as part of the Fatigue and Performance Modeling Workshop held in Seattle, WA, on June 13-14, 2002. The Workshop was jointly sponsored by the National Aeronautics and Space Administration, U.S. Department of Defense, U.S. Army Medical Research and Materiel Command, Office of Naval Research, Air Force Office of Scientific Research, and U.S. Department of Transportation. An invitation was sent to developers of seven biomathematical models that were commonly cited in scientific literature and/or supported by government funding. On acceptance of the invitation to attend the Workshop, developers were asked to complete a survey of the goals, capabilities, inputs, and outputs of their biomathematical models of alertness and performance. Data from the completed surveys were summarized and juxtaposed to provide a framework for comparing features of the seven models. Survey responses revealed that models varied greatly relative to their reported goals and capabilities. While all modelers reported that circadian factors were key components of their capabilities, they differed markedly with regard to the roles of sleep and work times as input factors for prediction: four of the seven models had work time as their sole input variable(s), while the other three models relied on various aspects of sleep timing for model input. Models also differed relative to outputs: five sought to predict results from laboratory experiments, field, and operational data, while two models were developed without regard to predicting laboratory experimental results. All modelers provided published papers describing their models, with three of the models being proprietary. Although all models appear to have been fundamentally influenced by the two-process model of sleep regulation by Borbély, there is considerable diversity among them in the number and type of input and output variables, and their stated goals and capabilities.

Summary of the key features of seven biomathematical models of human fatigue and performance

NASA Technical Reports Server (NTRS)

Mallis, Melissa M.; Mejdal, Sig; Nguyen, Tammy T.; Dinges, David F.

2004-01-01

BACKGROUND: Biomathematical models that quantify the effects of circadian and sleep/wake processes on the regulation of alertness and performance have been developed in an effort to predict the magnitude and timing of fatigue-related responses in a variety of contexts (e.g., transmeridian travel, sustained operations, shift work). This paper summarizes key features of seven biomathematical models reviewed as part of the Fatigue and Performance Modeling Workshop held in Seattle, WA, on June 13-14, 2002. The Workshop was jointly sponsored by the National Aeronautics and Space Administration, U.S. Department of Defense, U.S. Army Medical Research and Materiel Command, Office of Naval Research, Air Force Office of Scientific Research, and U.S. Department of Transportation. METHODS: An invitation was sent to developers of seven biomathematical models that were commonly cited in scientific literature and/or supported by government funding. On acceptance of the invitation to attend the Workshop, developers were asked to complete a survey of the goals, capabilities, inputs, and outputs of their biomathematical models of alertness and performance. Data from the completed surveys were summarized and juxtaposed to provide a framework for comparing features of the seven models. RESULTS: Survey responses revealed that models varied greatly relative to their reported goals and capabilities. While all modelers reported that circadian factors were key components of their capabilities, they differed markedly with regard to the roles of sleep and work times as input factors for prediction: four of the seven models had work time as their sole input variable(s), while the other three models relied on various aspects of sleep timing for model input. Models also differed relative to outputs: five sought to predict results from laboratory experiments, field, and operational data, while two models were developed without regard to predicting laboratory experimental results. All modelers provided published papers describing their models, with three of the models being proprietary. CONCLUSIONS: Although all models appear to have been fundamentally influenced by the two-process model of sleep regulation by Borbely, there is considerable diversity among them in the number and type of input and output variables, and their stated goals and capabilities.

Regulation mechanisms in mixed and pure culture microbial fermentation.

PubMed

Hoelzle, Robert D; Virdis, Bernardino; Batstone, Damien J

2014-11-01

Mixed-culture fermentation is a key central process to enable next generation biofuels and biocommodity production due to economic and process advantages over application of pure cultures. However, a key limitation to the application of mixed-culture fermentation is predicting culture product response, related to metabolic regulation mechanisms. This is also a limitation in pure culture bacterial fermentation. This review evaluates recent literature in both pure and mixed culture studies with a focus on understanding how regulation and signaling mechanisms interact with metabolic routes and activity. In particular, we focus on how microorganisms balance electron sinking while maximizing catabolic energy generation. Analysis of these mechanisms and their effect on metabolism dynamics is absent in current models of mixed-culture fermentation. This limits process prediction and control, which in turn limits industrial application of mixed-culture fermentation. A key mechanism appears to be the role of internal electron mediating cofactors, and related regulatory signaling. This may determine direction of electrons towards either hydrogen or reduced organics as end-products and may form the basis for future mechanistic models. © 2014 Wiley Periodicals, Inc.

Develop a Systems Approach to Characterizing and Predicting Thyroid Toxicity using an Amphibian Model

EPA Science Inventory

This research makes use of in vitro and in vivo approaches to understand and discriminate the compensatory and toxicological responses of the highly regulated HPT system. Development of an initial systems model will be based on the current understanding of the HPT axis and the co...

Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana

PubMed Central

Locke, James C W; Kozma-Bognár, László; Gould, Peter D; Fehér, Balázs; Kevei, Éva; Nagy, Ferenc; Turner, Matthew S; Hall, Anthony; Millar, Andrew J

2006-01-01

Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse. PMID:17102804

Positive and negative eating expectancies in disordered eating among women and men.

PubMed

Hayaki, Jumi; Free, Sarah

2016-08-01

Deficits in emotion regulation are known to characterize disordered eating patterns including binge eating, purging, and dietary restraint, though much of this work has been conducted exclusively on women. Eating expectancies, or expectations regarding reinforcement from food and eating, constitute one cognitive mechanism that is thought to serve as a proximal influence on eating behavior. Previous research shows that eating to manage negative affect (a negative eating expectancy) is associated with eating pathology in women, but less is known about eating as a reward or for pleasure (a positive eating expectancy). In addition, no prior work has examined eating expectancies among men. This study examines the role of emotion regulation and eating expectancies on disordered eating in women and men. Participants were 121 female and 80 male undergraduates who completed self-report measures of emotion regulation, eating expectancies, and disordered eating. In women, body mass index (BMI), emotion regulation, and eating to manage negative affect directly predicted disordered eating in the final multivariate model, whereas eating for pleasure or reward was inversely associated with disordered eating. However, in men, emotion regulation predicted disordered eating, but not when eating expectancies were added to the model. In the final model, only BMI and eating to manage negative affect contributed significantly to the variance in disordered eating. These findings suggest that some correlates of eating pathology, particularly eating expectancies, may vary by gender. Future research should continue to examine gender differences in the explanatory mechanisms underlying disordered eating. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuroscientific Model of Motivational Process

PubMed Central

Kim, Sung-il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment. PMID:23459598

Neuroscientific model of motivational process.

PubMed

Kim, Sung-Il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment.

Regulation of Motivation: Predicting Students' Homework Motivation Management at the Secondary School Level

ERIC Educational Resources Information Center

Xu, Jianzhong

2014-01-01

This study examines models of variables posited to predict students' homework motivation management (HMM), based on survey data from 866 8th graders (61 classes) and 745 11th graders (46 classes) in the south-eastern USA. Most of the variance in HMM occurred at the student level, with parent education as the only significant predictor at the class…

UTCI-Fiala multi-node model of human heat transfer and temperature regulation

NASA Astrophysics Data System (ADS)

Fiala, Dusan; Havenith, George; Bröde, Peter; Kampmann, Bernhard; Jendritzky, Gerd

2012-05-01

The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.

Does intrinsic motivation strengthen physical activity habit? Modeling relationships between self-determination, past behaviour, and habit strength.

PubMed

Gardner, Benjamin; Lally, Phillippa

2013-10-01

Habit formation is thought to aid maintenance of physical activity, but little research is available into determinants of habit strength aside from repeated performance. Previous work has shown that intrinsically motivated physical activity, underpinned by inherent satisfaction derived from activity, is more likely to be sustained. We explored whether this might reflect a tendency for self-determined activity to become more strongly habitual. A sample of 192 adults aged 18-30 completed measures of motivational regulation, intention, behaviour, and habit strength. Results showed that self-determined regulation interacted with past behaviour in predicting habit strength: prior action was more predictive of habit strength among more autonomously motivated participants. There was an unexpected direct effect of self-determined regulation on habit strength, independently of past behaviour. Findings offer possible directions for future habit formation work.

Modeling and Prediction of Fan Noise

NASA Technical Reports Server (NTRS)

Envia, Ed

2008-01-01

Fan noise is a significant contributor to the total noise signature of a modern high bypass ratio aircraft engine and with the advent of ultra high bypass ratio engines like the geared turbofan, it is likely to remain so in the future. As such, accurate modeling and prediction of the basic characteristics of fan noise are necessary ingredients in designing quieter aircraft engines in order to ensure compliance with ever more stringent aviation noise regulations. In this paper, results from a comprehensive study aimed at establishing the utility of current tools for modeling and predicting fan noise will be summarized. It should be emphasized that these tools exemplify present state of the practice and embody what is currently used at NASA and Industry for predicting fan noise. The ability of these tools to model and predict fan noise is assessed against a set of benchmark fan noise databases obtained for a range of representative fan cycles and operating conditions. Detailed comparisons between the predicted and measured narrowband spectral and directivity characteristics of fan nose will be presented in the full paper. General conclusions regarding the utility of current tools and recommendations for future improvements will also be given.

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

PubMed

Hu, Chelsea Y; Takahashi, Melissa K; Zhang, Yan; Lucks, Julius B

2018-05-22

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Modeling the cost and benefit of proteome regulation in a growing bacterial cell

NASA Astrophysics Data System (ADS)

Sharma, Pooja; Pratim Pandey, Parth; Jain, Sanjay

2018-07-01

Escherichia coli cells differentially regulate the production of metabolic and ribosomal proteins in order to stay close to an optimal growth rate in different environments, and exhibit the bacterial growth laws as a consequence. We present a simple mathematical model of a growing-dividing cell in which an internal dynamical mechanism regulates the allocation of proteomic resources between different protein sectors. The model allows an endogenous determination of the growth rate of the cell as a function of cellular and environmental parameters, and reproduces the bacterial growth laws. We use the model and its variants to study the balance between the cost and benefit of regulation. A cost is incurred because cellular resources are diverted to produce the regulatory apparatus. We show that there is a window of environments or a ‘niche’ in which the unregulated cell has a higher fitness than the regulated cell. Outside this niche there is a large space of constant and time varying environments in which regulation is an advantage. A knowledge of the ‘niche boundaries’ allows one to gain an intuitive understanding of the class of environments in which regulation is an advantage for the organism and which would therefore favour the evolution of regulation. The model allows us to determine the ‘niche boundaries’ as a function of cellular parameters such as the size of the burden of the regulatory apparatus. This class of models may be useful in elucidating various tradeoffs in cells and in making in-silico predictions relevant for synthetic biology.

Model-based investigation of the circadian clock and cell cycle coupling in mouse embryonic fibroblasts: Prediction of RevErb-α up-regulation during mitosis.

PubMed

Traynard, Pauline; Feillet, Céline; Soliman, Sylvain; Delaunay, Franck; Fages, François

2016-11-01

Experimental observations have put in evidence autonomous self-sustained circadian oscillators in most mammalian cells, and proved the existence of molecular links between the circadian clock and the cell cycle. Some mathematical models have also been built to assess conditions of control of the cell cycle by the circadian clock. However, recent studies in individual NIH3T3 fibroblasts have shown an unexpected acceleration of the circadian clock together with the cell cycle when the culture medium is enriched with growth factors, and the absence of such acceleration in confluent cells. In order to explain these observations, we study a possible entrainment of the circadian clock by the cell cycle through a regulation of clock genes around the mitosis phase. We develop a computational model and a formal specification of the observed behavior to investigate the conditions of entrainment in period and phase. We show that either the selective activation of RevErb-α or the selective inhibition of Bmal1 transcription during the mitosis phase, allow us to fit the experimental data on both period and phase, while a uniform inhibition of transcription during mitosis seems incompatible with the phase data. We conclude on the arguments favoring the RevErb-α up-regulation hypothesis and on some further predictions of the model. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Computational Model Predicting Disruption of Blood Vessel Development

PubMed Central

Kleinstreuer, Nicole; Dix, David; Rountree, Michael; Baker, Nancy; Sipes, Nisha; Reif, David; Spencer, Richard; Knudsen, Thomas

2013-01-01

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology. PMID:23592958

Motivational "spill-over" during weight control: increased self-determination and exercise intrinsic motivation predict eating self-regulation.

PubMed

Mata, Jutta; Silva, Marlene N; Vieira, Paulo N; Carraça, Eliana V; Andrade, Ana M; Coutinho, Sílvia R; Sardinha, Luis B; Teixeira, Pedro J

2009-11-01

Successful weight management relies on at least two health behaviors, eating and exercise. However, little is known about their interaction on a motivational and behavioral level. Based on the Hierarchical Model of Motivation the authors examined whether exercise-specific motivation can transfer to eating regulation during a lifestyle weight control program. The authors further investigated whether general, treatment-related, and exercise motivation underlie the relation between increased exercise and improved eating regulation. Overweight/obese women participated in a 1-year randomized controlled trial (N = 239). The intervention focused on promoting physical activity and internal motivation for exercise and weight loss, following Self-Determination Theory. The control group received general health education. General and exercise specific self-determination, eating self-regulation variables, and physical activity behavior. General self-determination and more autonomous exercise motivation predicted eating self-regulation over 12 months. Additionally, general and exercise self-determination fully mediated the relation between physical activity and eating self-regulation. Increased general self-determination and exercise motivation seem to facilitate improvements in eating self-regulation during weight control in women. These motivational mechanisms also underlie the relationship between improvements in exercise behavior and eating regulation. PsycINFO Database Record (c) 2009 APA, all rights reserved.

Androgen responses to reproductive competition of males pursuing either fixed or plastic alternative reproductive tactics.

PubMed

von Kuerthy, Corinna; Ros, Albert F H; Taborsky, Michael

2016-11-15

Alternative reproductive tactics (ARTs), which can be plastic or fixed for life, may be characterized by distinct hormonal profiles. The relative plasticity hypothesis predicts flexible androgen regulation for adult males pursuing plastic tactics, but a less flexible regulation for males using a fixed tactic throughout life. Furthermore, androgen profiles may respond to changes in the social environment, as predicted by the social reciprocity models of hormone/behaviour interactions. The cichlid fish Lamprologus callipterus provides a rare opportunity to study the roles of androgens for male ARTs within a single species, because fixed and plastic ARTs coexist. We experimentally exposed males to competitors pursuing either the same or different tactics to test predictions of the relative plasticity and the social reciprocity models. Androgen profiles of different male types partly comply with predictions derived from the relative plasticity hypothesis: males of the plastic bourgeois/sneaker male trajectory showed different 11-ketotestosterone (11-KT) levels when pursuing either bourgeois or parasitic sneaker male behaviours. Surprisingly, males pursuing the fixed dwarf male tactic showed the highest free and conjugated 11-KT and testosterone (T) levels. Our experimental social challenges significantly affected the free 11-KT levels of bourgeois males, but the androgen responses did not differ between challenges involving different types of competitors. Furthermore, the free T-responses of the bourgeois males correlated with their aggressive behaviour exhibited against competitors. Our results provide new insights into the endocrine responsiveness of fixed and plastic ARTs, confirming and refuting some predictions of both the relative plasticity and the social reciprocity models. © 2016. Published by The Company of Biologists Ltd.

Pan-Arctic modelling of net ecosystem exchange of CO2

PubMed Central

Shaver, G. R.; Rastetter, E. B.; Salmon, V.; Street, L. E.; van de Weg, M. J.; Rocha, A.; van Wijk, M. T.; Williams, M.

2013-01-01

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic. PMID:23836790

Development of Thresholds and Exceedance Probabilities for Influent Water Quality to Meet Drinking Water Regulations

NASA Astrophysics Data System (ADS)

Reeves, K. L.; Samson, C.; Summers, R. S.; Balaji, R.

2017-12-01

Drinking water treatment utilities (DWTU) are tasked with the challenge of meeting disinfection and disinfection byproduct (DBP) regulations to provide safe, reliable drinking water under changing climate and land surface characteristics. DBPs form in drinking water when disinfectants, commonly chlorine, react with organic matter as measured by total organic carbon (TOC), and physical removal of pathogen microorganisms are achieved by filtration and monitored by turbidity removal. Turbidity and TOC in influent waters to DWTUs are expected to increase due to variable climate and more frequent fires and droughts. Traditional methods for forecasting turbidity and TOC require catchment specific data (i.e. streamflow) and have difficulties predicting them under non-stationary climate. A modelling framework was developed to assist DWTUs with assessing their risk for future compliance with disinfection and DBP regulations under changing climate. A local polynomial method was developed to predict surface water TOC using climate data collected from NOAA, Normalized Difference Vegetation Index (NDVI) data from the IRI Data Library, and historical TOC data from three DWTUs in diverse geographic locations. Characteristics from the DWTUs were used in the EPA Water Treatment Plant model to determine thresholds for influent TOC that resulted in DBP concentrations within compliance. Lastly, extreme value theory was used to predict probabilities of threshold exceedances under the current climate. Results from the utilities were used to produce a generalized TOC threshold approach that only requires water temperature and bromide concentration. The threshold exceedance model will be used to estimate probabilities of exceedances under projected climate scenarios. Initial results show that TOC can be forecasted using widely available data via statistical methods, where temperature, precipitation, Palmer Drought Severity Index, and NDVI with various lags were shown to be important predictors of TOC, and TOC thresholds can be determined using water temperature and bromide concentration. Results include a model to predict influent turbidity and turbidity thresholds, similar to the TOC models, as well as probabilities of threshold exceedances for TOC and turbidity under changing climate.

Predictors of early growth in academic achievement: the head-toes-knees-shoulders task

PubMed Central

McClelland, Megan M.; Cameron, Claire E.; Duncan, Robert; Bowles, Ryan P.; Acock, Alan C.; Miao, Alicia; Pratt, Megan E.

2014-01-01

Children's behavioral self-regulation and executive function (EF; including attentional or cognitive flexibility, working memory, and inhibitory control) are strong predictors of academic achievement. The present study examined the psychometric properties of a measure of behavioral self-regulation called the Head-Toes-Knees-Shoulders (HTKS) by assessing construct validity, including relations to EF measures, and predictive validity to academic achievement growth between prekindergarten and kindergarten. In the fall and spring of prekindergarten and kindergarten, 208 children (51% enrolled in Head Start) were assessed on the HTKS, measures of cognitive flexibility, working memory (WM), and inhibitory control, and measures of emergent literacy, mathematics, and vocabulary. For construct validity, the HTKS was significantly related to cognitive flexibility, working memory, and inhibitory control in prekindergarten and kindergarten. For predictive validity in prekindergarten, a random effects model indicated that the HTKS significantly predicted growth in mathematics, whereas a cognitive flexibility task significantly predicted growth in mathematics and vocabulary. In kindergarten, the HTKS was the only measure to significantly predict growth in all academic outcomes. An alternative conservative analytical approach, a fixed effects analysis (FEA) model, also indicated that growth in both the HTKS and measures of EF significantly predicted growth in mathematics over four time points between prekindergarten and kindergarten. Results demonstrate that the HTKS involves cognitive flexibility, working memory, and inhibitory control, and is substantively implicated in early achievement, with the strongest relations found for growth in achievement during kindergarten and associations with emergent mathematics. PMID:25071619

A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation

PubMed Central

Farasat, Iman; Salis, Howard M.

2016-01-01

The ability to precisely modify genomes and regulate specific genes will greatly accelerate several medical and engineering applications. The CRISPR/Cas9 (Type II) system binds and cuts DNA using guide RNAs, though the variables that control its on-target and off-target activity remain poorly characterized. Here, we develop and parameterize a system-wide biophysical model of Cas9-based genome editing and gene regulation to predict how changing guide RNA sequences, DNA superhelical densities, Cas9 and crRNA expression levels, organisms and growth conditions, and experimental conditions collectively control the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites with both canonical and non-canonical PAMs. We combine statistical thermodynamics and kinetics to model Cas9:crRNA complex formation, diffusion, site selection, reversible R-loop formation, and cleavage, using large amounts of structural, biochemical, expression, and next-generation sequencing data to determine kinetic parameters and develop free energy models. Our results identify DNA supercoiling as a novel mechanism controlling Cas9 binding. Using the model, we predict Cas9 off-target binding frequencies across the lambdaphage and human genomes, and explain why Cas9’s off-target activity can be so high. With this improved understanding, we propose several rules for designing experiments for minimizing off-target activity. We also discuss the implications for engineering dCas9-based genetic circuits. PMID:26824432

A mathematical model of airway and pulmonary arteriole smooth muscle.

PubMed

Wang, Inga; Politi, Antonio Z; Tania, Nessy; Bai, Yan; Sanderson, Michael J; Sneyd, James

2008-03-15

Airway hyperresponsiveness is a major characteristic of asthma and is believed to result from the excessive contraction of airway smooth muscle cells (SMCs). However, the identification of the mechanisms responsible for airway hyperresponsiveness is hindered by our limited understanding of how calcium (Ca2+), myosin light chain kinase (MLCK), and myosin light chain phosphatase (MLCP) interact to regulate airway SMC contraction. In this work, we present a modified Hai-Murphy cross-bridge model of SMC contraction that incorporates Ca2+ regulation of MLCK and MLCP. A comparative fit of the model simulations to experimental data predicts 1), that airway and arteriole SMC contraction is initiated by fast activation by Ca2+ of MLCK; 2), that airway SMC, but not arteriole SMC, is inhibited by a slower activation by Ca2+ of MLCP; and 3), that the presence of a contractile agonist inhibits MLCP to enhance the Ca2+ sensitivity of airway and arteriole SMCs. The implication of these findings is that murine airway SMCs exploit a Ca2+-dependent mechanism to favor a default state of relaxation. The rate of SMC relaxation is determined principally by the rate of release of the latch-bridge state, which is predicted to be faster in airway than in arteriole. In addition, the model also predicts that oscillations in calcium concentration, commonly observed during agonist-induced smooth muscle contraction, cause a significantly greater contraction than an elevated steady calcium concentration.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Predicted effects of proposed new regulation plans on sedge/grass meadows of Lake Ontario

USGS Publications Warehouse

Wilcox, D.A.; Xie, Y.

2008-01-01

Previously described models for predicting the percent of Lake Ontario wetlands that would be occupied by sedge/grass-dominated meadow marsh were used to test four proposed new plans for regulation of lake levels and to make comparisons with the current plan and unregulated conditions. The models for drowned river mouth, barrier beach, open embayment, and protected embayment wetlands assessed responses to lake levels that would be generated by each plan under net total supplies modified from those that occurred from1900 to 2000. In years when reduced supplies would allow meadow marsh regeneration, simulated unregulated lake levels produced the most meadow marsh in all wetland geomorphic types; current Plan 1958DD produced the least. Overall predicted percent meadow marsh under the test plans decreased in the order B+, 2007, D+, and A+, and the latter three plans produced rather similar results in many cases. Lower percentages of meadow marsh under some plans were due to insufficient low lake levels that could allow soils to dry and restrict invasion by cattails, as well as lack of periodic high lake levels that could kill invading upland plants. An assessment of seasonal lake-level characteristics demonstrated that Plan 2007 would reduce mean winter lake levels by 13 cm or more than Plan B+ and springtime lake levels by more than 10 cm. These seasonal differences could result in less winter habitat for muskrats and reduced access to spring spawning habitats for fish such as northern pike. Our model results provide important information for use in the process of selecting a new regulation plan for Lake Ontario.

Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River

USGS Publications Warehouse

Cole, Jeffrey C.; Maloney, Kelly O.; Schmid, Matthias; McKenna, James E.

2014-01-01

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58–1.311, NSE = 0.99–0.97, d = 0.98–0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = −0.10 to −1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = −4.1 to −10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77–0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Developing and testing temperature models for regulated systems: A case study on the Upper Delaware River

NASA Astrophysics Data System (ADS)

Cole, Jeffrey C.; Maloney, Kelly O.; Schmid, Matthias; McKenna, James E.

2014-11-01

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58-1.311, NSE = 0.99-0.97, d = 0.98-0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = -0.10 to -1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = -4.1 to -10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77-0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Towards predicting coral calcification responses to ocean acidification: A combined modeling and experimental approach

NASA Astrophysics Data System (ADS)

Mollica, N. R.; Guo, W.; Cohen, A. L.; Foster, G. L.; Barkley, H.

2016-02-01

Experiments show that ocean acidification is detrimental to coral calcification. Nevertheless, coral sensitivities to OA vary and the mechanism(s) underlying these variable responses are not fully understood. One hypothesis is that ocean acidification affects the ability of coral's to regulate the pH of fluid at the site of calcification. We developed a numerical model of coral calcification that simulates corals' pH regulation based on physiochemical principles and predicts the rate of calcification [1]. Here we apply this model to Palauan corals, and seek to test the model's efficacy by comparing the predicted coral calcification responses with experimental measurements. Four coral cores were collected from two sites of different pH (7.84 and 8.04 respectively). Their bulk annual calcification rates, quantified from average density and extension rate measurements, vary from .83 to 1.39 g cm-2 year-1 for the low pH site and from 0.75 to 1.21 g cm-2 year-1for the high pH site. The higher bulk calcification rates observed in corals from the low pH site contrasts the expected general decrease in calcification in low pH seawater, and differs from our model prediction. We suspect this apparent discrepancy arises because fast-calcifying corals in low pH water are able to modulate the pH of fluid at the site of calcification. We test this hypothesis using boron isotope measurements from each coral. In addition, a more accurate measurement of instantaneous calcification, considering the number of corallites per measured area and the exact surface area of each polyp's 3-dimensional calcification site is applied. [1] Guo, W. (2014). AGU Fall Meeting, Abstract B41B-0033.

Late-adoptions in adolescence: Can attachment and emotion regulation influence behaviour problems? A controlled study using a moderation approach.

PubMed

Pace, Cecilia Serena; Di Folco, Simona; Guerriero, Viviana

2018-03-01

A growing body of research suggests that, compared to normative adolescence, adoptive adolescence could be considered a specific risk condition characterized by more psychiatric problems, attachment insecurity, and emotional regulation difficulties as consequences of negative experiences in preadoption relationships. The current study explores (a) a moderation model of adoption status on the association between attachment representations (secure, dismissing, preoccupied, and disorganized) and behavioural problems and (b) a moderation model of adoption status on the association between emotion regulation processes (cognitive reappraisal and expressive suppression) and behavioural problems. Both the moderation models were controlled for verbal skills. Forty-six adopted adolescents and a control group of 34 nonadopted peers (12-16 years old) living with both their biological parents were assessed using the Friends and Family Interview, the Emotion Regulation Questionnaire for Children and Adolescents, the Child Behaviour Check List 6-18, and the verbal subtests of the Wechsler Intelligence Scale for Children, the latter as control measure. Results showed that adoption status (but not attachment) positively predicted externalizing and total behaviour problems, whereas attachment disorganization (but not adoption status) positively predicted internalizing problems in both group. Moreover, low cognitive reappraisal had a negative impact on externalizing problems only for adopted adolescents, but not for nonadopted youths. The clinical implications of these findings are discussed in order to enhance effective intervention with adopted adolescents and their parents. Copyright © 2017 John Wiley & Sons, Ltd.

Using Improved Equation of State to Model Simultaneous Nucleation and Bubble Growth in Thermoplastic Foams

NASA Astrophysics Data System (ADS)

Khan, Irfan; Costeux, Stephane; Adrian, David; Cristancho, Diego

2013-11-01

Due to environmental regulations carbon-dioxide (CO2) is increasingly being used to replace traditional blowing agents in thermoplastic foams. CO2 is dissolved in the polymer matrix under supercritical conditions. In order to predict the effect of process parameters on foam properties using numerical modeling, the P-V-T relationship of the blowing agents should accurately be represented at the supercritical state. Previous studies in the area of foam modeling have all used ideal gas equation of state to predict the behavior of the blowing agent. In this work the Peng-Robinson equation of state is being used to model the blowing agent during its diffusion into the growing bubble. The model is based on the popular ``Influence Volume Approach,'' which assumes a growing boundary layer with depleted blowing agent surrounds each bubble. Classical nucleation theory is used to predict the rate of nucleation of bubbles. By solving the mass balance, momentum balance and species conservation equations for each bubble, the model is capable of predicting average bubble size, bubble size distribution and bulk porosity. The effect of the improved model on the bubble growth and foam properties are discussed.

Molecular Theory for Electrokinetic Transport in pH-Regulated Nanochannels.

PubMed

Kong, Xian; Jiang, Jian; Lu, Diannan; Liu, Zheng; Wu, Jianzhong

2014-09-04

Ion transport through nanochannels depends on various external driving forces as well as the structural and hydrodynamic inhomogeneity of the confined fluid inside of the pore. Conventional models of electrokinetic transport neglect the discrete nature of ionic species and electrostatic correlations important at the boundary and often lead to inconsistent predictions of the surface potential and the surface charge density. Here, we demonstrate that the electrokinetic phenomena can be successfully described by the classical density functional theory in conjunction with the Navier-Stokes equation for the fluid flow. The new theoretical procedure predicts ion conductivity in various pH-regulated nanochannels under different driving forces, in excellent agreement with experimental data.

Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

Advances in Estimating Methane Emissions from Enteric Fermentation

NASA Astrophysics Data System (ADS)

Kebreab, E.; Appuhamy, R.

2016-12-01

Methane from enteric fermentation of livestock is the largest contributor to the agricultural GHG emissions. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. Most countries use a fixed number (kg methane/year) or calculate as a proportion of energy intake to estimate enteric methane emissions in national inventories. However, diet composition significantly regulates enteric methane production in addition to total feed intake and thus the main target in formulating mitigation options. The two current methodologies are not able to assess mitigation options, therefore, new estimation methods are required that can take feed composition into account. The availability of information on livestock production systems has increased substantially enabling the development of more detailed methane prediction models. Limited number of process-based models have been developed that represent biological relationships in methane production, however, these require extensive inputs and specialized software that may not be easily available. Empirical models may provide a better alternative in practical situations due to less input requirements. Several models have been developed in the last 10 years but none of them work equally well across all regions of the world. The more successful models particularly in North America require three major inputs: feed (or energy) intake, fiber and fat concentration of the diet. Given the significant variability of emissions within regions, models that are able to capture regional variability of feed intake and diet composition perform the best in model evaluation with independent data. The utilization of such models may reduce uncertainties associated with prediction of methane emissions and allow a better examination and representation of policies regulating emissions from cattle.

Assessment of potential for small hydro/solar power integration in a mountainous, data sparse region: the role of hydrological prediction accuracy

NASA Astrophysics Data System (ADS)

Borga, Marco; Francois, Baptiste; Creutin, Jean-Dominique; Hingray, Benoit; Zoccatelli, Davide; Tardivo, Gianmarco

2015-04-01

In many parts of the world, integration of small hydropower and solar/wind energy sources along river systems is examined as a way to meet pressing renewable energy targets. Depending on the space and time scales considered, hydrometeorological variability may synchronize or desynchronize solar/wind, runoff and the demand opening the possibility to use their complementarity to smooth the intermittency of each individual energy source. Rivers also provide important ecosystem services, including the provision of high quality downstream water supply and the maintenance of in-stream habitats. With future supply and demand of water resources both impacted by environmental change, a good understanding of the potential for the integration among hydropower and solar/wind energy sources in often sparsely gauged catchments is important. In such cases, where complex data-demanding models may be inappropriate, there is a need for simple conceptual modelling approaches that can still capture the main features of runoff generation and artificial regulation processes. In this work we focus on run-of-the-river and solar-power interaction assessment. In order to catch the three key cycles of the load fluctuation - daily, weekly and seasonal, the time step used in the study is the hourly resolution. We examine the performance of a conceptual hydrological model which includes facilities to model dam regulation and diversions and hydrological modules to account for the effect of glaciarised catchments. The model is applied to catchments of the heavily regulated Upper Adige river system (6900 km2), Eastern Italian Alps, which has a long history of hydropower generation. The model is used to characterize and predict the natural flow regime, assess the regulation impacts, and simulate co-fluctuations between run-of- the-river and solar power. The results demonstrates that the simple, conceptual modelling approach developed here can capture the main hydrological and regulation processes well at the three key cycles of the load fluctuations. A specific focus is dedicated on how the results can be communicated to stakeholders in order to provide a basis for discussing the development of new adaptive management strategies.

Analysis and prediction of leucine-rich nuclear export signals.

PubMed

la Cour, Tanja; Kiemer, Lars; Mølgaard, Anne; Gupta, Ramneek; Skriver, Karen; Brunak, Søren

2004-06-01

We present a thorough analysis of nuclear export signals and a prediction server, which we have made publicly available. The machine learning prediction method is a significant improvement over the generally used consensus patterns. Nuclear export signals (NESs) are extremely important regulators of the subcellular location of proteins. This regulation has an impact on transcription and other nuclear processes, which are fundamental to the viability of the cell. NESs are studied in relation to cancer, the cell cycle, cell differentiation and other important aspects of molecular biology. Our conclusion from this analysis is that the most important properties of NESs are accessibility and flexibility allowing relevant proteins to interact with the signal. Furthermore, we show that not only the known hydrophobic residues are important in defining a nuclear export signals. We employ both neural networks and hidden Markov models in the prediction algorithm and verify the method on the most recently discovered NESs. The NES predictor (NetNES) is made available for general use at http://www.cbs.dtu.dk/.

Parenting in Infancy and Self-Regulation in Preschool: An Investigation of the Role of Attachment History

PubMed Central

Birmingham, R.S.; Bub, K.L.; Vaughn, B.E.

2017-01-01

Parenting and attachment are critical in the emergence of self-regulation (SR) in preschool. However, most studies use general indexes of parenting quality, failing to explore the unique contributions of sensitivity and home quality to SR. Further, the nature of the interplay between parenting and attachment history is not well understood. Using a sample of 938 children from The NICHD Study of Early Child Care and Youth Development, a series of structural equation models were fit to determine whether sensitivity and home quality concurrently predicted SR at 54 months, and whether attachment mediated or moderated these pathways. Results suggest that both sensitivity and home quality uniquely predict SR. Further, these early parenting variables were each indirectly associated with SR through children's attachment history. That is, higher levels of sensitivity and home quality predicted secure attachment history, which, along with parenting, predicted more advanced SR skills at 54 months. No moderated pathways emerged, suggesting attachment history may be best conceptualized as a mediating mechanism. PMID:27894211

Dictyostelium LvsB has a regulatory role in endosomal vesicle fusion

PubMed Central

Falkenstein, Kristin; De Lozanne, Arturo

2014-01-01

ABSTRACT Defects in human lysosomal-trafficking regulator (Lyst) are associated with the lysosomal disorder Chediak–Higashi syndrome. The absence of Lyst results in the formation of enlarged lysosome-related compartments, but the mechanism for how these compartments arise is not well established. Two opposing models have been proposed to explain Lyst function. The fission model describes Lyst as a positive regulator of fission from lysosomal compartments, whereas the fusion model identifies Lyst as a negative regulator of fusion between lysosomal vesicles. Here, we used assays that can distinguish between defects in vesicle fusion versus fission. We compared the phenotype of Dictyostelium discoideum cells defective in LvsB, the ortholog of Lyst, with that of two known fission defect mutants (μ3- and WASH-null mutants). We found that the temporal localization characteristics of the post-lysosomal marker vacuolin, as well as vesicular acidity and the fusion dynamics of LvsB-null cells are distinct from those of both μ3- and WASH-null fission defect mutants. These distinctions are predicted by the fusion defect model and implicate LvsB as a negative regulator of vesicle fusion. PMID:25086066

Evaluating Air-Quality Models: Review and Outlook.

NASA Astrophysics Data System (ADS)

Weil, J. C.; Sykes, R. I.; Venkatram, A.

1992-10-01

Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (c about the mean concentration (C) over an ensemble-a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For cC large residuals exist between predicted and observed concentrations, which confuse model evaluations.This paper addresses ways of evaluating model physics in light of the large c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C-from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.In general, c models and predictions of the probability distribution of the fluctuating concentration (c), (c, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that (c approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model-ARAP-that predicts C, c, and (c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications- the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.

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

NASA Astrophysics Data System (ADS)

Lombardozzi, D.; Levis, S.; Bonan, G.; Sparks, J. P.

2012-08-01

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

What can 35 years and over 700,000 measurements tell us about noise exposure in the mining industry?

PubMed Central

Roberts, Benjamin; Sun, Kan; Neitzel, Richard L.

2017-01-01

Objective To analyze over 700,000 cross-sectional measurements from the Mine Safety and Health Administration (MHSA) and develop statistical models to predict noise exposure for a worker. Design Descriptive statistics were used to summarize the data. Two linear regression models were used to predict noise exposure based on MSHA permissible exposure limit (PEL) and action level (AL) respectively. Two-fold cross validation was used to compare the exposure estimates from the models to actual measurements in the hold out data. The mean difference and t-statistic was calculated for each job title to determine if the model exposure predictions were significantly different from the actual data. Study Sample Measurements were acquired from MSHA through a Freedom of Information Act request. Results From 1979 to 2014 the average noise measurement has decreased. Measurements taken before the implementation of MSHA’s revised noise regulation in 2000 were on average 4.5 dBA higher than after the law came in to effect. Both models produced mean exposure predictions that were less than 1 dBA different compared to the holdout data. Conclusion Overall noise levels in mines have been decreasing. However, this decrease has not been uniform across all mining sectors. The exposure predictions from the model will be useful to help predict hearing loss in workers from the mining industry. PMID:27871188

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

A model to predict the evolution of a gravel bed river under an imposed cyclic hydrograph and its application to the Trinity River

NASA Astrophysics Data System (ADS)

Viparelli, Enrica; Gaeuman, David; Wilcock, Peter; Parker, Gary

2011-02-01

Major changes in the morphology of the Trinity River in California, such as narrowing of the cross section and sedimentation of fine sediment in pools, occurred after the closure of a system of dams. These changes caused a dramatic reduction in the salmonid population and a resulting decline of the fishery. Gravel augmentation, regulated flood releases, and mechanical channel rehabilitation are currently being implemented to help restore the aquatic habitat of the river. The present paper describes a tool, named the Spawning Gravel Refresher, for designing and predicting the effects of gravel augmentation in gravel bed rivers. The tool assumes an imposed, cycled hydrograph. The model is calibrated and applied to the regulated reach of the Trinity River in four steps: (1) zeroing runs to reproduce conditions of mobile bed equilibrium as best can be estimated for the predam Trinity River, (2) runs to compare the predictions with the results of previous studies, (3) runs at an engineering time scale to reproduce the effects of the dams, and (4) runs to design gravel augmentation schemes. In the fourth group of runs, the combined effects of engineered flood flow releases and gravel augmentation are predicted. At an engineering time scale, the model indicates that the fraction of fine sediment in the surface layer and in the topmost part of the substrate should decrease when subjected to these two restoration measures, with a consequent improvement of the quality of the spawning gravel.

Experimental Demonstration of Frequency Regulation by Commercial Buildings – Part II: Results and Performance Evaluation

DOE PAGES

Vrettos, Evangelos; Kara, Emre Can; MacDonald, Jason; ...

2016-11-15

This paper is the second part of a two-part series presenting the results from an experimental demonstration of frequency regulation in a commercial building test facility. We developed relevant building models and designed a hierarchical controller for reserve scheduling, building climate control and frequency regulation in Part I. In Part II, we introduce the communication architecture and experiment settings, and present extensive experimental results under frequency regulation. More specifically, we compute the day-ahead reserve capacity of the test facility under different assumptions and conditions. Furthermore, we demonstrate the ability of model predictive control to satisfy comfort constraints under frequency regulation,more » and show that fan speed control can track the fast-moving RegD signal of the Pennsylvania, Jersey, and Maryland Power Market (PJM) very accurately. In addition, we discuss potential effects of frequency regulation on building operation (e.g., increase in energy consumption, oscillations in supply air temperature, and effect on chiller cycling), and provide suggestions for real-world implementation projects. Our results show that hierarchical control is appropriate for frequency regulation from commercial buildings.« less

Lvr, a Signaling System That Controls Global Gene Regulation and Virulence in Pathogenic Leptospira.

PubMed

Adhikarla, Haritha; Wunder, Elsio A; Mechaly, Ariel E; Mehta, Sameet; Wang, Zheng; Santos, Luciane; Bisht, Vimla; Diggle, Peter; Murray, Gerald; Adler, Ben; Lopez, Francesc; Townsend, Jeffrey P; Groisman, Eduardo; Picardeau, Mathieu; Buschiazzo, Alejandro; Ko, Albert I

2018-01-01

Leptospirosis is an emerging zoonotic disease with more than 1 million cases annually. Currently there is lack of evidence for signaling pathways involved during the infection process of Leptospira . In our comprehensive genomic analysis of 20 Leptospira spp. we identified seven pathogen-specific Two-Component System (TCS) proteins. Disruption of two these TCS genes in pathogenic Leptospira strain resulted in loss-of-virulence in a hamster model of leptospirosis. Corresponding genes lvrA and lvrB (leptospira virulence regulator ) are juxtaposed in an operon and are predicted to encode a hybrid histidine kinase and a hybrid response regulator, respectively. Transcriptome analysis of lvr mutant strains with disruption of one ( lvrB ) or both genes ( lvrA/B ) revealed global transcriptional regulation of 850 differentially expressed genes. Phosphotransfer assays demonstrated that LvrA phosphorylates LvrB and predicted further signaling downstream to one or more DNA-binding response regulators, suggesting that it is a branched pathway. Phylogenetic analyses indicated that lvrA and lvrB evolved independently within different ecological lineages in Leptospira via gene duplication. This study uncovers a novel-signaling pathway that regulates virulence in pathogenic Leptospira (Lvr), providing a framework to understand the molecular bases of regulation in this life-threatening bacterium.

Lvr, a Signaling System That Controls Global Gene Regulation and Virulence in Pathogenic Leptospira

PubMed Central

Adhikarla, Haritha; Wunder, Elsio A.; Mechaly, Ariel E.; Mehta, Sameet; Wang, Zheng; Santos, Luciane; Bisht, Vimla; Diggle, Peter; Murray, Gerald; Adler, Ben; Lopez, Francesc; Townsend, Jeffrey P.; Groisman, Eduardo; Picardeau, Mathieu; Buschiazzo, Alejandro; Ko, Albert I.

2018-01-01

Leptospirosis is an emerging zoonotic disease with more than 1 million cases annually. Currently there is lack of evidence for signaling pathways involved during the infection process of Leptospira. In our comprehensive genomic analysis of 20 Leptospira spp. we identified seven pathogen-specific Two-Component System (TCS) proteins. Disruption of two these TCS genes in pathogenic Leptospira strain resulted in loss-of-virulence in a hamster model of leptospirosis. Corresponding genes lvrA and lvrB (leptospira virulence regulator) are juxtaposed in an operon and are predicted to encode a hybrid histidine kinase and a hybrid response regulator, respectively. Transcriptome analysis of lvr mutant strains with disruption of one (lvrB) or both genes (lvrA/B) revealed global transcriptional regulation of 850 differentially expressed genes. Phosphotransfer assays demonstrated that LvrA phosphorylates LvrB and predicted further signaling downstream to one or more DNA-binding response regulators, suggesting that it is a branched pathway. Phylogenetic analyses indicated that lvrA and lvrB evolved independently within different ecological lineages in Leptospira via gene duplication. This study uncovers a novel-signaling pathway that regulates virulence in pathogenic Leptospira (Lvr), providing a framework to understand the molecular bases of regulation in this life-threatening bacterium. PMID:29600195

Positive affect predicts avoidance goals in social interaction anxiety: testing a hierarchical model of social goals.

PubMed

Trew, Jennifer L; Alden, Lynn E

2012-01-01

Models of self-regulation suggest that social goals may contribute to interpersonal and affective difficulties, yet little research has addressed this issue in the context of social anxiety. The present studies evaluated a hierarchical model of approach and avoidance in the context of social interaction anxiety, with affect as a mediating factor in the relationship between motivational tendencies and social goals. This model was refined in one undergraduate sample (N = 186) and cross-validated in a second sample (N = 195). The findings support hierarchical relationships between motivational tendencies, social interaction anxiety, affect, and social goals, with higher positive affect predicting fewer avoidance goals in both samples. Implications for the treatment of social interaction anxiety are discussed.

Demonstration of an Integrated Compliance Model for Predicting Copper Fate and Effects in DoD Harbors

DTIC Science & Technology

2008-11-01

seawater that does not include the natural ingredients that buffer the toxic effects of contaminants. As such, federal WQC could be overprotective ...regulation was overprotective (Earley et al., 2007). Implementation of a site-specific WQS in both cases could reduce the likelihood of TMDL actions...for site-specific factors that regulate bioavailability and toxicity, and thus are often overprotective (Seligman and Zirino, 1998; Zirino and

Conformity and dietary disinhibition: a test of the ego-strength model of self-regulation.

PubMed

Kahan, Dana; Polivy, Janet; Herman, C Peter

2003-03-01

Ego-strength depletion was examined as an explanation for dietary disinhibition in restrained eaters. We predicted that the depletion of ego strength resulting from having to choose whether to conform would undermine dietary restraint. Participants completed an Asch-type conformity task, after which they completed a taste-rating task in which food intake was measured. As predicted, restrained eaters who repeatedly exercised choice ate significantly more than did restrained eaters who did not exercise choice. An ego-strength model of dietary restraint is discussed. Copyright 2003 by Wiley Periodicals, Inc.

The mediating role of metacognition in the relationship between executive function and self-regulated learning.

PubMed

Follmer, D Jake; Sperling, Rayne A

2016-12-01

Researchers have demonstrated significant relations among executive function, metacognition, and self-regulated learning. However, prior research emphasized the use of indirect measures of executive function and did not evaluate how specific executive functions are related to participants' self-regulated learning. The primary goals of the current study were to examine and test the relations among executive function, metacognition, and self-regulated learning as well as to examine how self-regulated learning is informed by executive function. The sample comprised 117 undergraduate students attending a large, Mid-Atlantic research university in the United States. Participants were individually administered direct and indirect measures of executive function, metacognition, and self-regulated learning. A mediation model specifying the relations among the regulatory constructs was proposed. In multiple linear regression analyses, executive function predicted metacognition and self-regulated learning. Direct measures of inhibition and shifting accounted for a significant amount of the variance in metacognition and self-regulated learning beyond an indirect measure of executive functioning. Separate mediation analyses indicated that metacognition mediated the relationship between executive functioning and self-regulated learning as well as between specific executive functions and self-regulated learning. The findings of this study are supported by previous research documenting the relations between executive function and self-regulated learning, and extend prior research by examining the manner in which executive function and self-regulated learning are linked. The findings provide initial support for executive functions as key processes, mediated by metacognition, that predict self-regulated learning. Implications for the contribution of executive functions to self-regulated learning are discussed. © 2016 The British Psychological Society.

Autonomous and controlled motivational regulations for multiple health-related behaviors: between- and within-participants analyses

PubMed Central

Hagger, M.S.; Hardcastle, S.J.; Chater, A.; Mallett, C.; Pal, S.; Chatzisarantis, N.L.D.

2014-01-01

Self-determination theory has been applied to the prediction of a number of health-related behaviors with self-determined or autonomous forms of motivation generally more effective in predicting health behavior than non-self-determined or controlled forms. Research has been confined to examining the motivational predictors in single health behaviors rather than comparing effects across multiple behaviors. The present study addressed this gap in the literature by testing the relative contribution of autonomous and controlling motivation to the prediction of a large number of health-related behaviors, and examining individual differences in self-determined motivation as a moderator of the effects of autonomous and controlling motivation on health behavior. Participants were undergraduate students (N = 140) who completed measures of autonomous and controlled motivational regulations and behavioral intention for 20 health-related behaviors at an initial occasion with follow-up behavioral measures taken four weeks later. Path analysis was used to test a process model for each behavior in which motivational regulations predicted behavior mediated by intentions. Some minor idiosyncratic findings aside, between-participants analyses revealed significant effects for autonomous motivational regulations on intentions and behavior across the 20 behaviors. Effects for controlled motivation on intentions and behavior were relatively modest by comparison. Intentions mediated the effect of autonomous motivation on behavior. Within-participants analyses were used to segregate the sample into individuals who based their intentions on autonomous motivation (autonomy-oriented) and controlled motivation (control-oriented). Replicating the between-participants path analyses for the process model in the autonomy- and control-oriented samples did not alter the relative effects of the motivational orientations on intention and behavior. Results provide evidence for consistent effects of autonomous motivation on intentions and behavior across multiple health-related behaviors with little evidence of moderation by individual differences. Findings have implications for the generalizability of proposed effects in self-determination theory and intentions as a mediator of distal motivational factors on health-related behavior. PMID:25750803

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

PubMed Central

Williams, Alex H; O'Donnell, Cian; Sejnowski, Terrence J; O'Leary, Timothy

2016-01-01

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’ (Doyle and Kiebler, 2011). Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimates of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons. DOI: http://dx.doi.org/10.7554/eLife.20556.001 PMID:28034367

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Epidemic patch models applied to pandemic influenza: contact matrix, stochasticity, robustness of predictions.

PubMed

Lunelli, Antonella; Pugliese, Andrea; Rizzo, Caterina

2009-07-01

Due to the recent emergence of H5N1 virus, the modelling of pandemic influenza has become a relevant issue. Here we present an SEIR model formulated to simulate a possible outbreak in Italy, analysing its structure and, more generally, the effect of including specific details into a model. These details regard population heterogeneities, such as age and spatial distribution, as well as stochasticity, that regulates the epidemic dynamics when the number of infectives is low. We discuss and motivate the specific modelling choices made when building the model and investigate how the model details influence the predicted dynamics. Our analysis may help in deciding which elements of complexity are worth including in the design of a deterministic model for pandemic influenza, in a balance between, on the one hand, keeping the model computationally efficient and the number of parameters low and, on the other hand, maintaining the necessary realistic features.

Hygienic food handling behaviors: attempting to bridge the intention-behavior gap using aspects from temporal self-regulation theory.

PubMed

Fulham, Elizabeth; Mullan, Barbara

2011-06-01

An estimated 25% of the populations of both the United States and Australia suffer from foodborne illness every year, generally as a result of incorrect food handling practices. The aim of the current study was to determine through the application of the theory of planned behavior what motivates these behaviors and to supplement the model with two aspects of temporal self-regulation theory--behavioral prepotency and executive function--in an attempt to bridge the "intention-behavior gap." A prospective 1-week design was utilized to investigate the prediction of food hygiene using the theory of planned behavior with the additional variables of behavioral prepotency and executive function. One hundred forty-nine undergraduate psychology students completed two neurocognitive executive function tasks and a self-report questionnaire assessing theory of planned behavior variables, behavioral prepotency, and intentions to perform hygienic food handling behaviors. A week later, behavior was assessed via a follow-up self-report questionnaire. It was found that subjective norm and perceived behavioral control predicted intentions and intentions predicted behavior. However, behavioral prepotency was found to be the strongest predictor of behavior, over and above intentions, suggesting that food hygiene behavior is habitual. Neither executive function measure of self-regulation predicted any additional variance. These results provide support for the utility of the theory of planned behavior in this health domain, but the augmentation of the theory with two aspects of temporal self-regulation theory was only partially successful.

Evaluation of display and control concepts for a terminal configured vehicle in final approach in a windshear environment

NASA Technical Reports Server (NTRS)

Levison, W. H.

1978-01-01

A revised treatment of nonrandom inputs was incorporated in the model. Response behavior was observed for two display configurations (a pictorial EADI presentation and a flight-director configuration requiring use of a panel-mounted airspeed indicator), two control configurations (attitude and velocity control wheel steering), and two shear environments, each of which contained a head-to-tail shear and a vertical component. In general, performance trends predicted by the model were confirmed experimentally. Experimental and analytical results both indicated superiority to the EADI display with respect to regulation of height and airspeed errors. Velocity steering allowed tighter regulation of height errors, but control parameters had little influence on airspeed regulation. Model analysis indicated that display-related differences could be ascribed to differences in the quality of speed-related information provided by the two displays.

First-Principles-Driven Model-Based Optimal Control of the Current Profile in NSTX-U

NASA Astrophysics Data System (ADS)

Ilhan, Zeki; Barton, Justin; Wehner, William; Schuster, Eugenio; Gates, David; Gerhardt, Stefan; Kolemen, Egemen; Menard, Jonathan

2014-10-01

Regulation in time of the toroidal current profile is one of the main challenges toward the realization of the next-step operational goals for NSTX-U. A nonlinear, control-oriented, physics-based model describing the temporal evolution of the current profile is obtained by combining the magnetic diffusion equation with empirical correlations obtained at NSTX-U for the electron density, electron temperature, and non-inductive current drives. In this work, the proposed model is embedded into the control design process to synthesize a time-variant, linear-quadratic-integral, optimal controller capable of regulating the safety factor profile around a desired target profile while rejecting disturbances. Neutral beam injectors and the total plasma current are used as actuators to shape the current profile. The effectiveness of the proposed controller in regulating the safety factor profile in NSTX-U is demonstrated via closed-loop predictive simulations carried out in PTRANSP. Supported by PPPL.

Pathway Model of the Kinetics of the TGFbeta Antagonist Smad7 and Cross-Talk with the ATM and WNT Pathways

NASA Technical Reports Server (NTRS)

Carra, Claudio; Wang, Minli; Huff, Janice L.; Hada, Megumi; ONeill, Peter; Cucinotta, Francis A.

2010-01-01

Signal transduction controls cellular and tissue responses to radiation. Transforming growth factor beta (TGFbeta) is an important regulator of cell growth and differentiation and tissue homeostasis, and is often dis-regulated in tumor formation. Mathematical models of signal transduction pathways can be used to elucidate how signal transduction varies with radiation quality, and dose and dose-rate. Furthermore, modeling of tissue specific responses can be considered through mechanistic based modeling. We developed a mathematical model of the negative feedback regulation by Smad7 in TGFbeta-Smad signaling and are exploring possible connections to the WNT/beta -catenin, and ATM/ATF2 signaling pathways. A pathway model of TGFbeta-Smad signaling that includes Smad7 kinetics based on data in the scientific literature is described. Kinetic terms included are TGFbeta/Smad transcriptional regulation of Smad7 through the Smad3-Smad4 complex, Smad7-Smurf1 translocation from nucleus to cytoplasm, and Smad7 negative feedback regulation of the TGFO receptor through direct binding to the TGFO receptor complex. The negative feedback controls operating in this pathway suggests non-linear responses in signal transduction, which are described mathematically. We then explored possibilities for cross-talk mediated by Smad7 between DNA damage responses mediated by ATM, and with the WNT pathway and consider the design of experiments to test model driven hypothesis. Numerical comparisons of the mathematical model to experiments and representative predictions are described.

Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

PubMed

Kiviniemi, Marc T; Brown-Kramer, Carolyn R

2015-05-01

Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. © The Author(s) 2015.

Final Report of the Mid-Atlantic Marine Wildlife Surveys, Modeling, and Data

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

Saracino-Brown, Jocelyn; Smith, Courtney; Gilman, Patrick

The Wind Program hosted a two-day workshop on July 24-25, 2012 with scientists and regulators engaged in marine ecological survey, modeling, and database efforts pertaining to the waters of the Mid-Atlantic region. The workshop was planned by Federal agency, academic, and private partners to promote collaboration between ongoing offshore ecological survey efforts, and to promote the collaborative development of complementary predictive models and compatible databases. The meeting primarily focused on efforts to establish and predict marine mammal, seabird, and sea turtle abundance, density, and distributions extending from the shoreline to the edge of the Exclusive Economic Zone between Nantucket Sound,more » Massachusetts and Cape Hatteras, North Carolina.« less

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

PubMed Central

Messinis, Dimitris E.; Melas, Ioannis N.; Hur, Junguk; Varshney, Navya; Alexopoulos, Leonidas G.

2018-01-01

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers. PMID:29341478

Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry.

PubMed

Rowlinson, Steve; Jia, Yunyan Andrea

2014-04-01

Existing heat stress risk management guidelines recommended by international standards are not practical for the construction industry which needs site supervision staff to make instant managerial decisions to mitigate heat risks. The ability of the predicted heat strain (PHS) model [ISO 7933 (2004). Ergonomics of the thermal environment analytical determination and interpretation of heat stress using calculation of the predicted heat strain. Geneva: International Standard Organisation] to predict maximum allowable exposure time (D lim) has now enabled development of localized, action-triggering and threshold-based guidelines for implementation by lay frontline staff on construction sites. This article presents a protocol for development of two heat stress management tools by applying the PHS model to its full potential. One of the tools is developed to facilitate managerial decisions on an optimized work-rest regimen for paced work. The other tool is developed to enable workers' self-regulation during self-paced work.

Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures.

PubMed

Selby-Pham, Sophie N B; Howell, Kate S; Dunshea, Frank R; Ludbey, Joel; Lutz, Adrian; Bennett, Louise

2018-04-15

A diet rich in phytochemicals confers benefits for health by reducing the risk of chronic diseases via regulation of oxidative stress and inflammation (OSI). For optimal protective bio-efficacy, the time required for phytochemicals and their metabolites to reach maximal plasma concentrations (T max ) should be synchronised with the time of increased OSI. A statistical model has been reported to predict T max of individual phytochemicals based on molecular mass and lipophilicity. We report the application of the model for predicting the absorption profile of an uncharacterised phytochemical mixture, herein referred to as the 'functional fingerprint'. First, chemical profiles of phytochemical extracts were acquired using liquid chromatography mass spectrometry (LC-MS), then the molecular features for respective components were used to predict their plasma absorption maximum, based on molecular mass and lipophilicity. This method of 'functional fingerprinting' of plant extracts represents a novel tool for understanding and optimising the health efficacy of plant extracts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of l-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli

PubMed Central

2013-01-01

Background Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. Results We constructed an l-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for l-glutamic acid production; the results of this process corresponded with previous experimental data regarding l-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of l-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model l-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in l-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. Conclusions In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation. PMID:24053676

Analysis of L-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli.

PubMed

Nishio, Yousuke; Ogishima, Soichi; Ichikawa, Masao; Yamada, Yohei; Usuda, Yoshihiro; Masuda, Tadashi; Tanaka, Hiroshi

2013-09-22

Understanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required. We constructed an L-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for L-glutamic acid production; the results of this process corresponded with previous experimental data regarding L-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of L-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model L-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in L-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model. In this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation.

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

DTIC Science & Technology

2018-03-01

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

Which Fearful Toddlers Should We Worry About? Context, Fear Regulation, and Anxiety Risk

PubMed Central

Buss, Kristin A.

2010-01-01

The current study tests a model of risk for anxiety in fearful toddlers characterized by the regulation of the intensity of withdrawal behavior across a variety of contexts. Participants included 111, low-risk, 24-month-old toddlers followed longitudinally each year through the fall of their kindergarten year. The key hypothesis was that being fearful in situations that are relatively low in threat (i.e., are predictable, controllable, and in which children have many coping resources) is an early precursor to risk for anxiety development as measured by parent and teacher report of anxious behaviors in kindergarten. Results supported the prediction such that it is not how much fear is expressed, but when the fear is expressed and how it is expressed that is important for characterizing adaptive behavior. Implications are discussed for a model of risk that includes the regulation of fear, the role of eliciting context, social wariness, and the importance of examining developmental transitions, such as the start of formal schooling. These findings have implications for the way we identify fearful children who may be at risk for developing anxiety-related problems. PMID:21463035

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.

Long-term regulation in the cardiovascular system - Cornerstone in the development of a composite physiological model

NASA Technical Reports Server (NTRS)

White, R. J.

1974-01-01

The present work discusses a model of the cardiovascular system and related subsystems capable of long-term simulations of the type desired for in-space hypogravic human physiological performance prediction. The discussion centers around the model of Guyton and modifications of it. In order to draw attention to the fluid handling capabilities of the model, one of several transfusion simulations performed is presented, namely, the isotonic saline transfusion simulation.

An evaluation of selected in silico models for the assessment ...

EPA Pesticide Factsheets

Skin sensitization remains an important endpoint for consumers, manufacturers and regulators. Although the development of alternative approaches to assess skin sensitization potential has been extremely active over many years, the implication of regulations such as REACH and the Cosmetics Directive in EU has provided a much stronger impetus to actualize this research into practical tools for decision making. Thus there has been considerable focus on the development, evaluation, and integration of alternative approaches for skin sensitization hazard and risk assessment. This includes in silico approaches such as (Q)SARs and expert systems. This study aimed to evaluate the predictive performance of a selection of in silico models and then to explore whether combining those models led to an improvement in accuracy. A dataset of 473 substances that had been tested in the local lymph node assay (LLNA) was compiled. This comprised 295 sensitizers and 178 non-sensitizers. Four freely available models were identified - 2 statistical models VEGA and MultiCASE model A33 for skin sensitization (MCASE A33) from the Danish National Food Institute and two mechanistic models Toxtree’s Skin sensitization Reaction domains (Toxtree SS Rxn domains) and the OASIS v1.3 protein binding alerts for skin sensitization from the OECD Toolbox (OASIS). VEGA and MCASE A33 aim to predict sensitization as a binary score whereas the mechanistic models identified reaction domains or structura

A Modified Obesity Proneness Model Predicts Adolescent Weight Concerns and Inability to Self-Regulate Eating

ERIC Educational Resources Information Center

Nickelson, Jen; Bryant, Carol A.; McDermott, Robert J.; Buhi, Eric R.; DeBate, Rita D.

2012-01-01

Background: The prevalence of obesity among high school students has risen in recent decades. Many high school students report trying to lose weight and some engage in disordered eating to do so. The obesity proneness model suggests that parents may influence their offspring's development of disordered eating. This study examined the viability of…

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Trauma exposure interacts with impulsivity in predicting emotion regulation and depressive mood

PubMed Central

Ceschi, Grazia; Billieux, Joël; Hearn, Melissa; Fürst, Guillaume; Van der Linden, Martial

2014-01-01

Background Traumatic exposure may modulate the expression of impulsive behavioral dispositions and change the implementation of emotion regulation strategies associated with depressive mood. Past studies resulted in only limited comprehension of these relationships, especially because they failed to consider impulsivity as a multifactorial construct. Objective Based on Whiteside and Lynam's multidimensional model that identifies four distinct dispositional facets of impulsive-like behaviors, namely urgency, (lack of) premeditation, (lack of) perseverance, and sensation seeking (UPPS), the current study used a sample of community volunteers to investigate whether an interaction exists between impulsivity facets and lifetime trauma exposure in predicting cognitive emotion regulation and depressive mood. Methods Ninety-three adults completed questionnaires measuring lifetime trauma exposure, impulsivity, cognitive emotion regulation, and depressive mood. Results Results showed that trauma-exposed participants with a strong disposition toward urgency (predisposition to act rashly in intense emotional contexts) tended to use fewer appropriate cognitive emotion regulation strategies than other individuals. Unexpectedly, participants lacking in perseverance (predisposition to have difficulties concentrating on demanding tasks) used more appropriate emotion regulation strategies if they had experienced traumatic events during their life than if they had not. Emotion regulation mediated the path between these two impulsivity facets and depressive mood. Conclusions Together, these findings suggest that impulsivity has a differential impact on emotion regulation and depressive mood depending on lifetime exposure to environmental factors, especially traumatic events. PMID:25317255

Do microbial processes regulate the stability of a coral atoll's enclosed pelagic ecosystem?

EPA Science Inventory

Complex marine ecosystems contain multiple feedback cycles that can cause unexpected responses to perturbations. To better predict these responses, complicated models are increasingly being developed to enable the study of feedback cycles. However, the sparseness of ecological da...

cDREM: inferring dynamic combinatorial gene regulation.

PubMed

Wise, Aaron; Bar-Joseph, Ziv

2015-04-01

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.

Can the big five factors of personality predict lymphocyte counts?

PubMed

Ožura, Ana; Ihan, Alojz; Musek, Janek

2012-03-01

Psychological stress is known to affect the immune system. The Limbic Hypothalamic Pituitary Adrenal (LHPA) axis has been identified as the principal path of the bidirectional communication between the immune system and the central nervous system with significant psychological activators. Personality traits acted as moderators of the relationship between life conflicts and psychological distress. This study focuses on the relationship between the Big Five factors of personality and immune regulation as indicated by Lymphocyte counts. Our study included 32 professional soldiers from the Slovenian Army that completed the Big Five questionnaire (Goldberg IPIP-300). We also assessed their white blood cell counts with a detailed lymphocyte analysis using flow cytometry. The correlations between personality variables and immune system parameters were calculated. Furthermore, regression analyses were performed using personality variables as predictors and immune parameters as criteria. The results demonstrated that the model using the Big Five factors as predictors of Lymphocyte counts is significant in predicting the variance in NK and B cell counts. Agreeableness showed the strongest predictive function. The results offer support for the theoretical models that stressed the essential links between personality and immune regulation. Further studies with larger samples examining the Big five factors and immune system parameters are needed.

An Integrative Model for Phytochrome B Mediated Photomorphogenesis: From Protein Dynamics to Physiology

PubMed Central

Kircher, Stefan; Kirchenbauer, Daniel; Timmer, Jens; Nagy, Ferenc; Schäfer, Eberhard; Fleck, Christian

2010-01-01

Background Plants have evolved various sophisticated mechanisms to respond and adapt to changes of abiotic factors in their natural environment. Light is one of the most important abiotic environmental factors and it regulates plant growth and development throughout their entire life cycle. To monitor the intensity and spectral composition of the ambient light environment, plants have evolved multiple photoreceptors, including the red/far-red light-sensing phytochromes. Methodology/Principal Findings We have developed an integrative mathematical model that describes how phytochrome B (phyB), an essential receptor in Arabidopsis thaliana, controls growth. Our model is based on a multiscale approach and connects the mesoscopic intracellular phyB protein dynamics to the macroscopic growth phenotype. To establish reliable and relevant parameters for the model phyB regulated growth we measured: accumulation and degradation, dark reversion kinetics and the dynamic behavior of different nuclear phyB pools using in vivo spectroscopy, western blotting and Fluorescence Recovery After Photobleaching (FRAP) technique, respectively. Conclusions/Significance The newly developed model predicts that the phyB-containing nuclear bodies (NBs) (i) serve as storage sites for phyB and (ii) control prolonged dark reversion kinetics as well as partial reversibility of phyB Pfr in extended darkness. The predictive power of this mathematical model is further validated by the fact that we are able to formalize a basic photobiological observation, namely that in light-grown seedlings hypocotyl length depends on the total amount of phyB. In addition, we demonstrate that our theoretical predictions are in excellent agreement with quantitative data concerning phyB levels and the corresponding hypocotyl lengths. Hence, we conclude that the integrative model suggested in this study captures the main features of phyB-mediated photomorphogenesis in Arabidopsis. PMID:20502669

Infant negative reactivity defines the effects of parent-child synchrony on physiological and behavioral regulation of social stress.

PubMed

Pratt, Maayan; Singer, Magi; Kanat-Maymon, Yaniv; Feldman, Ruth

2015-11-01

How infants shape their own development has puzzled developmentalists for decades. Recent models suggest that infant dispositions, particularly negative reactivity and regulation, affect outcome by determining the extent of parental effects. Here, we used a microanalytic experimental approach and proposed that infants with varying levels of negative reactivity will be differentially impacted by parent-infant synchrony in predicting physiological and behavioral regulation of increasing social stress during an experimental paradigm. One hundred and twenty-two mother-infant dyads (4-6 months) were observed in the face-to-face still face (SF) paradigm and randomly assigned to three experimental conditions: SF with touch, standard SF, and SF with arms' restraint. Mother-infant synchrony and infant negative reactivity were observed at baseline, and three mechanisms of behavior regulation were microcoded; distress, disengagement, and social regulation. Respiratory sinus arrhythmia baseline, reactivity, and recovery were quantified. Structural equation modeling provided support for our hypothesis. For physiological regulation, infants high in negative reactivity receiving high mother-infant synchrony showed greater vagal withdrawal, which in turn predicted comparable levels of vagal recovery to that of nonreactive infants. In behavioral regulation, only infants low in negative reactivity who received high synchrony were able to regulate stress by employing social engagement cues during the SF phase. Distress was reduced only among calm infants to highly synchronous mothers, and disengagement was lowest among highly reactive infants experiencing high mother-infant synchrony. Findings chart two pathways by which synchrony may bolster regulation in infants of high and low reactivity. Among low reactive infants, synchrony builds a social repertoire for handling interpersonal stress, whereas in highly reactive infants, it constructs a platform for repeated reparation of momentary interactive "failures" and reduces the natural tendency of stressed infants to disengage from source of distress. Implications for the construction of synchrony-focused interventions targeting infants of varying dispositions are discussed.

Challenges and opportunities to improve understanding on wetland ecosystem and function at the local catchment scale: data fusion, data-model integration, and prediction uncertainty.

NASA Astrophysics Data System (ADS)

Yeo, I. Y.

2016-12-01

Wetlands are valuable landscape features that provide important ecosystem functions and services. The ecosystem processes in wetlands are highly dependent on the hydrology. However, hydroperiod (i.e., change dynamics in inundation extent) is highly variable spatially and temporarily, and extremely difficult to predict owing to the complexity in hydrological processes within wetlands and its interaction with surrounding areas. This study reports the challenges and progress in assessing the catchment scale benefits of wetlands to regulate hydrological regime and water quality improvement in agricultural watershed. A process-based watershed model, Soil and Water Assessment Tool (SWAT) was improved to simulate the cumulative impacts of wetlands on downstream. Newly developed remote sensing products from LiDAR intensity and time series Landsat records, which show the inter-annual changes in fraction inundation, were utilized to describe the change status of inundated areas within forested wetlands, develop spatially varying wetland parameters, and evaluate the predicted inundated areas at the landscape level. We outline the challenges on developing the time series inundation mapping products at a high spatial and temporal resolution and reconciling the catchment scale model with the moderate remote sensing products. We then highlight the importance of integrating spatialized information to model calibration and evaluation to address the issues of equi-finality and prediction uncertainty. This integrated approach was applied to the upper region of Choptank River Watershed, the agricultural watershed in the Coastal Plain of Chesapeake Bay Watershed (in US). In the Mid- Atlantic US, the provision of pollution regulation services provided by wetlands has been emphasized due to declining water quality within the Chesapeake Bay and watersheds, and the preservation and restoration of wetlands has become the top priority to manage nonpoint source water pollution.

Plant hormone signaling during development: insights from computational models.

PubMed

Oliva, Marina; Farcot, Etienne; Vernoux, Teva

2013-02-01

Recent years have seen an impressive increase in our knowledge of the topology of plant hormone signaling networks. The complexity of these topologies has motivated the development of models for several hormones to aid understanding of how signaling networks process hormonal inputs. Such work has generated essential insights into the mechanisms of hormone perception and of regulation of cellular responses such as transcription in response to hormones. In addition, modeling approaches have contributed significantly to exploring how spatio-temporal regulation of hormone signaling contributes to plant growth and patterning. New tools have also been developed to obtain quantitative information on hormone distribution during development and to test model predictions, opening the way for quantitative understanding of the developmental roles of hormones. Copyright © 2012 Elsevier Ltd. All rights reserved.

Humidity-regulated dormancy onset in the Fabaceae: a conceptual model and its ecological implications for the Australian wattle Acacia saligna.

PubMed

Tozer, Mark G; Ooi, Mark K J

2014-09-01

Seed dormancy enhances fitness by preventing seeds from germinating when the probability of seedling survival and recruitment is low. The onset of physical dormancy is sensitive to humidity during ripening; however, the implications of this mechanism for seed bank dynamics have not been quantified. This study proposes a model that describes how humidity-regulated dormancy onset may control the accumulation of a dormant seed bank, and seed experiments are conducted to calibrate the model for an Australian Fabaceae, Acacia saligna. The model is used to investigate the impact of climate on seed dormancy and to forecast the ecological implications of human-induced climate change. The relationship between relative humidity and dormancy onset was quantified under laboratory conditions by exposing freshly matured non-dormant seeds to constant humidity levels for fixed durations. The model was field-calibrated by measuring the response of seeds exposed to naturally fluctuating humidity. The model was applied to 3-hourly records of humidity spanning the period 1972-2007 in order to estimate both temporal variability in dormancy and spatial variability attributable to climatic differences among populations. Climate change models were used to project future changes in dormancy onset. A sigmoidal relationship exists between dormancy and humidity under both laboratory and field conditions. Seeds ripened under field conditions became dormant following very short exposure to low humidity (<20 %). Prolonged exposure at higher humidity did not increase dormancy significantly. It is predicted that populations growing in a temperate climate produce 33-55 % fewer dormant seeds than those in a Mediterranean climate; however, dormancy in temperate populations is predicted to increase as a result of climate change. Humidity-regulated dormancy onset may explain observed variation in physical dormancy. The model offers a systematic approach to modelling this variation in population studies. Forecast changes in climate have the potential to alter the seed bank dynamics of species with physical dormancy regulated by this mechanism, with implications for their capacity to delay germination and exploit windows for recruitment. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Humidity-regulated dormancy onset in the Fabaceae: a conceptual model and its ecological implications for the Australian wattle Acacia saligna

PubMed Central

Tozer, Mark G.; Ooi, Mark K. J.

2014-01-01

Background and aims Seed dormancy enhances fitness by preventing seeds from germinating when the probability of seedling survival and recruitment is low. The onset of physical dormancy is sensitive to humidity during ripening; however, the implications of this mechanism for seed bank dynamics have not been quantified. This study proposes a model that describes how humidity-regulated dormancy onset may control the accumulation of a dormant seed bank, and seed experiments are conducted to calibrate the model for an Australian Fabaceae, Acacia saligna. The model is used to investigate the impact of climate on seed dormancy and to forecast the ecological implications of human-induced climate change. Methods The relationship between relative humidity and dormancy onset was quantified under laboratory conditions by exposing freshly matured non-dormant seeds to constant humidity levels for fixed durations. The model was field-calibrated by measuring the response of seeds exposed to naturally fluctuating humidity. The model was applied to 3-hourly records of humidity spanning the period 1972–2007 in order to estimate both temporal variability in dormancy and spatial variability attributable to climatic differences among populations. Climate change models were used to project future changes in dormancy onset. Key Results A sigmoidal relationship exists between dormancy and humidity under both laboratory and field conditions. Seeds ripened under field conditions became dormant following very short exposure to low humidity (<20 %). Prolonged exposure at higher humidity did not increase dormancy significantly. It is predicted that populations growing in a temperate climate produce 33–55 % fewer dormant seeds than those in a Mediterranean climate; however, dormancy in temperate populations is predicted to increase as a result of climate change. Conclusions Humidity-regulated dormancy onset may explain observed variation in physical dormancy. The model offers a systematic approach to modelling this variation in population studies. Forecast changes in climate have the potential to alter the seed bank dynamics of species with physical dormancy regulated by this mechanism, with implications for their capacity to delay germination and exploit windows for recruitment. PMID:25015069

Listeria monocytogenes Induces a Virulence-Dependent microRNA Signature That Regulates the Immune Response in Galleria mellonella

PubMed Central

Mannala, Gopala K.; Izar, Benjamin; Rupp, Oliver; Schultze, Tilman; Goesmann, Alexander; Chakraborty, Trinad; Hain, Torsten

2017-01-01

microRNAs (miRNAs) coordinate several physiological and pathological processes by regulating the fate of mRNAs. Studies conducted in vitro indicate a role of microRNAs in the control of host-microbe interactions. However, there is limited understanding of miRNA functions in in vivo models of bacterial infections. In this study, we systematically explored changes in miRNA expression levels of Galleria mellonella larvae (greater-wax moth), a model system that recapitulates the vertebrate innate immunity, following infection with L. monocytogenes. Using an insect-specific miRNA microarray with more than 2000 probes, we found differential expression of 90 miRNAs (39 upregulated and 51 downregulated) in response to infection with L. monocytogenes. We validated the expression of a subset of miRNAs which have mammalian homologs of known or predicted function. In contrast, non-pathogenic L. innocua failed to induce these miRNAs, indicating a virulence-dependent miRNA deregulation. To predict miRNA targets using established algorithms, we generated a publically available G. mellonella transcriptome database. We identified miRNA targets involved in innate immunity, signal transduction and autophagy, including spätzle, MAP kinase, and optineurin, respectively, which exhibited a virulence-specific differential expression. Finally, in silico estimation of minimum free energy of miRNA-mRNA duplexes of validated microRNAs and target transcripts revealed a regulatory network of the host immune response to L. monocytogenes. In conclusion, this study provides evidence for a role of miRNAs in the regulation of the innate immune response following bacterial infection in a simple, rapid and scalable in vivo model that may predict host-microbe interactions in higher vertebrates. PMID:29312175

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.

PubMed

Hess, Becky M; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C; Wiley, H Steven; Ahring, Birgitte K; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

PubMed Central

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. Steven; Ahring, Birgitte K.; Linggi, Bryan

2013-01-01

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions. PMID:23840410

Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

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

Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng

2013-06-19

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. Wemore » found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.« less

Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation.

PubMed

Weber, Michael; Sotoca, Ana M; Kupfer, Peter; Guthke, Reinhard; van Zoelen, Everardus J

2013-11-12

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network. Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1. Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies.

PubMed

Selimkhanov, Jangir; Thompson, W Clayton; Patterson, Terrell A; Hadcock, John R; Scott, Dennis O; Maurer, Tristan S; Musante, Cynthia J

2016-01-01

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies

PubMed Central

Selimkhanov, Jangir; Patterson, Terrell A.; Scott, Dennis O.; Maurer, Tristan S.; Musante, Cynthia J.

2016-01-01

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology. PMID:27227543

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

Changing clothes easily: connexin41.8 regulates skin pattern variation.

PubMed

Watanabe, Masakatsu; Kondo, Shigeru

2012-05-01

The skin patterns of animals are very important for their survival, yet the mechanisms involved in skin pattern formation remain unresolved. Turing's reaction-diffusion model presents a well-known mathematical explanation of how animal skin patterns are formed, and this model can predict various animal patterns that are observed in nature. In this study, we used transgenic zebrafish to generate various artificial skin patterns including a narrow stripe with a wide interstripe, a narrow stripe with a narrow interstripe, a labyrinth, and a 'leopard' pattern (or donut-like ring pattern). In this process, connexin41.8 (or its mutant form) was ectopically expressed using the mitfa promoter. Specifically, the leopard pattern was generated as predicted by Turing's model. Our results demonstrate that the pigment cells in animal skin have the potential and plasticity to establish various patterns and that the reaction-diffusion principle can predict skin patterns of animals. © 2012 John Wiley & Sons A/S.

A Mathematical Model of Airway and Pulmonary Arteriole Smooth Muscle

PubMed Central

Wang, Inga; Politi, Antonio Z.; Tania, Nessy; Bai, Yan; Sanderson, Michael J.; Sneyd, James

2008-01-01

Airway hyperresponsiveness is a major characteristic of asthma and is believed to result from the excessive contraction of airway smooth muscle cells (SMCs). However, the identification of the mechanisms responsible for airway hyperresponsiveness is hindered by our limited understanding of how calcium (Ca2+), myosin light chain kinase (MLCK), and myosin light chain phosphatase (MLCP) interact to regulate airway SMC contraction. In this work, we present a modified Hai-Murphy cross-bridge model of SMC contraction that incorporates Ca2+ regulation of MLCK and MLCP. A comparative fit of the model simulations to experimental data predicts 1), that airway and arteriole SMC contraction is initiated by fast activation by Ca2+ of MLCK; 2), that airway SMC, but not arteriole SMC, is inhibited by a slower activation by Ca2+ of MLCP; and 3), that the presence of a contractile agonist inhibits MLCP to enhance the Ca2+ sensitivity of airway and arteriole SMCs. The implication of these findings is that murine airway SMCs exploit a Ca2+-dependent mechanism to favor a default state of relaxation. The rate of SMC relaxation is determined principally by the rate of release of the latch-bridge state, which is predicted to be faster in airway than in arteriole. In addition, the model also predicts that oscillations in calcium concentration, commonly observed during agonist-induced smooth muscle contraction, cause a significantly greater contraction than an elevated steady calcium concentration. PMID:18065464

Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.

PubMed

Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong

2010-12-01

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.

Modeling the risk of water pollution by pesticides from imbalanced data.

PubMed

Trajanov, Aneta; Kuzmanovski, Vladimir; Real, Benoit; Perreau, Jonathan Marks; Džeroski, Sašo; Debeljak, Marko

2018-04-30

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation. In addition, the data describing water pollution with pesticides are, in most cases, very imbalanced. This is due to strict regulations for pesticide applications, which lead to only a few pollution events. In this study, we propose the use of data mining to build models for assessing the risk of water pollution by pesticides in field-drained outflow water. Unlike the mechanistic models, the models generated by data mining are based on easily obtainable empirical data, while the parameterization of the models is not influenced by the subjectivity of ecological modelers. We used empirical data from field trials at the La Jaillière experimental site in France and applied the random forests algorithm to build predictive models that predict "risky" and "not-risky" pesticide application events. To address the problems of the imbalanced classes in the data, cost-sensitive learning and different measures of predictive performance were used. Despite the high imbalance between risky and not-risky application events, we managed to build predictive models that make reliable predictions. The proposed modeling approach can be easily applied to other ecological modeling problems where we encounter empirical data with highly imbalanced classes.

Within Your Control? When Problem Solving May Be Most Helpful.

PubMed

Sarfan, Laurel D; Gooch, Peter; Clerkin, Elise M

2017-08-01

Emotion regulation strategies have been conceptualized as adaptive or maladaptive, but recent evidence suggests emotion regulation outcomes may be context-dependent. The present study tested whether the adaptiveness of a putatively adaptive emotion regulation strategy-problem solving-varied across contexts of high and low controllability. The present study also tested rumination, suggested to be one of the most putatively maladaptive strategies, which was expected to be associated with negative outcomes regardless of context. Participants completed an in vivo speech task, in which they were randomly assigned to a controllable ( n = 65) or an uncontrollable ( n = 63) condition. Using moderation analyses, we tested whether controllability interacted with emotion regulation use to predict negative affect, avoidance, and perception of performance. Partially consistent with hypotheses, problem solving was associated with certain positive outcomes (i.e., reduced behavioral avoidance) in the controllable (vs. uncontrollable) condition. Consistent with predictions, rumination was associated with negative outcomes (i.e., desired avoidance, negative affect, negative perception of performance) in both conditions. Overall, findings partially support contextual models of emotion regulation, insofar as the data suggest that the effects of problem solving may be more adaptive in controllable contexts for certain outcomes, whereas rumination may be maladaptive regardless of context.

Schooling effects on preschoolers’ self-regulation, early literacy, and language growth

PubMed Central

Skibbe, Lori E.; Connor, Carol McDonald; Morrison, Frederick J.; Jewkes, Abigail M.

2010-01-01

The present study examined the influence of schooling during children’s first and second years of preschool for children who experienced different amounts of preschool (i.e., one or two years), but who were essentially the same chronological age. Children (n = 76) were tested in the fall and spring of the school year using measures of self-regulation, decoding, letter knowledge, and vocabulary. Using hierarchical linear modeling (HLM), preschool was not associated with children’s development of self-regulation in either year. For decoding and letter knowledge, children finishing their second year of preschool had higher scores, although both groups of children grew similarly during the school year. Thus, our results suggest that the first and second years of preschool are both systematically associated with decoding and letter knowledge gains, and the effects are cumulative (two years predicted greater gains overall than did one year of preschool). Finally, children’s chronological age, and not whether they experienced one versus two years of preschool, predicted children’s vocabulary and self-regulation outcomes. Implications for preschool curricula and instruction are discussed, including the increasing emphasis on literacy learning prior to kindergarten entry and the need to address self-regulation development along with academic learning. PMID:24068856

How Coaches' Motivations Mediate Between Basic Psychological Needs and Well-Being/Ill-Being.

PubMed

Alcaraz, Saul; Torregrosa, Miquel; Viladrich, Carme

2015-01-01

The purpose of the present research was to test how behavioral regulations are mediated between basic psychological needs and psychological well-being and ill-being in a sample of team-sport coaches. Based on self-determination theory, we hypothesized a model where satisfaction and thwarting of the basic psychological needs predicted coaches' behavioral regulations, which in turn led them to experience well-being (i.e., subjective vitality, positive affect) or ill-being (i.e., perceived stress, negative affect). Three-hundred and two coaches participated in the study (Mage = 25.97 years; 82% male). For each instrument employed, the measurement model with the best psychometric properties was selected from a sequence of nested models sustained by previous research, including exploratory structural equation models and confirmatory factor analysis. These measurement models were included in 3 structural equation models to test for mediation: partial mediation, complete mediation, and absence of mediation. The results provided support for the partial mediation model. Coaches' motivation mediated the relationships from both relatedness need satisfaction and basic psychological needs thwarting for coaches' well-being. In contrast, relationships between basic psychological needs satisfaction and thwarting and ill-being were only predicted by direct effects. Our results highlight that 3 conditions seem necessary for coaches to experience psychological well-being in their teams: basic psychological needs satisfaction, especially relatedness; lack of basic psychological needs thwarting; and self-determined motivation.

Belief state representation in the dopamine system.

PubMed

Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2018-05-14

Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty. Mice are first trained in a task with two potential states defined by different reward amounts. During testing, intermediate-sized rewards are given in rare trials. Dopamine activity is a non-monotonic function of reward size, consistent with RL models operating on belief states. Furthermore, the magnitude of dopamine responses quantitatively predicts changes in behavior. These results establish the critical role of state inference in RL.

A modeling approach to evaluate the balance between bioactivation and detoxification of MeIQx in human hepatocytes

PubMed Central

Delannée, Victorien; Théret, Nathalie; Siegel, Anne

2017-01-01

Background Heterocyclic aromatic amines (HAA) are environmental and food contaminants that are potentially carcinogenic for humans. 2-Amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) is one of the most abundant HAA formed in cooked meat. MeIQx is metabolized by cytochrome P450 1A2 in the human liver into detoxificated and bioactivated products. Once bioactivated, MeIQx metabolites can lead to DNA adduct formation responsible for further genome instability. Methods Using a computational approach, we developed a numerical model for MeIQx metabolism in the liver that predicts the MeIQx biotransformation into detoxification or bioactivation pathways according to the concentration of MeIQx. Results Our results demonstrate that (1) the detoxification pathway predominates, (2) the ratio between detoxification and bioactivation pathways is not linear and shows a maximum at 10 µM of MeIQx in hepatocyte cell models, and (3) CYP1A2 is a key enzyme in the system that regulates the balance between bioactivation and detoxification. Our analysis suggests that such a ratio could be considered as an indicator of MeIQx genotoxicity at a low concentration of MeIQx. Conclusions Our model permits the investigation of the balance between bioactivation (i.e., DNA adduct formation pathway through the prediction of potential genotoxic compounds) and detoxification of MeIQx in order to predict the behaviour of this environmental contaminant in the human liver. It highlights the importance of complex regulations of enzyme competitions that should be taken into account in any further multi-organ models. PMID:28879062

Mediators of the Association of Major Depressive Syndrome and Anxiety Syndrome with Postpartum Smoking Relapse

PubMed Central

Correa-Fernández, Virmarie; Ji, Lingyun; Castro, Yessenia; Heppner, Whitney L.; Vidrine, Jennifer Irvin; Costello, Tracy J.; Mullen, Patricia Dolan; Cofta-Woerpel, Ludmila; Velasquez, Mary M.; Greisinger, Anthony; Cinciripini, Paul M.; Wetter, David W.

2012-01-01

Objective Based on conceptual models of addiction and affect regulation, this study examined the mechanisms linking current major depressive syndrome (MDS) and anxiety syndrome (AS) to postpartum smoking relapse. Method Data were collected in a randomized clinical trial from 251 women who quit smoking during pregnancy. Simple and multiple mediation models of the relations of MDS and AS with postpartum relapse were examined using linear regression, continuation ratio logit models, and a Bootstrapping procedure to test the indirect effects. Results Both MDS and AS significantly predicted postpartum smoking relapse. After adjusting for MDS, AS significantly predicted relapse. However, after adjusting for AS, MDS no longer predicted relapse. Situationally-based self-efficacy, expectancies of controlling negative affect by means other than smoking, and various dimensions of primary and secondary tobacco dependence individually mediated the effect of both MDS and AS on relapse. In multiple mediation models, self-efficacy in negative/affective situations significantly mediated the effect of MDS and AS on relapse. Conclusion The findings underscore the negative impact of depression and anxiety on postpartum smoking relapse, and suggest that the effects of MDS on postpartum relapse may be largely explained by comorbid AS. The current investigation provided mixed support for affect regulation models of addiction. Cognitive and tobacco dependence-related aspects of negative and positive reinforcement significantly mediated the relationship of depression and anxiety with relapse, while affect and stress did not. The findings emphasize the unique role of low agency with respect to abstaining from smoking in negative affective situations as a key predictor of postpartum smoking relapse. PMID:22390410

Mediators of the association of major depressive syndrome and anxiety syndrome with postpartum smoking relapse.

PubMed

Correa-Fernández, Virmarie; Ji, Lingyun; Castro, Yessenia; Heppner, Whitney L; Vidrine, Jennifer Irvin; Costello, Tracy J; Mullen, Patricia Dolan; Cofta-Woerpel, Ludmila; Velasquez, Mary M; Greisinger, Anthony; Cinciripini, Paul M; Wetter, David W

2012-08-01

Based on conceptual models of addiction and affect regulation, this study examined the mechanisms linking current major depressive syndrome (MDS) and anxiety syndrome (AS) to postpartum smoking relapse. Data were collected in a randomized clinical trial from 251 women who quit smoking during pregnancy. Simple and multiple mediation models of the relations of MDS and AS with postpartum relapse were examined using linear regression, continuation ratio logit models, and a bootstrapping procedure to test the indirect effects. Both MDS and AS significantly predicted postpartum smoking relapse. After adjusting for MDS, AS significantly predicted relapse. However, after adjusting for AS, MDS no longer predicted relapse. Situationally based self-efficacy, expectancies of controlling negative affect by means other than smoking, and various dimensions of primary and secondary tobacco dependence individually mediated the effect of both MDS and AS on relapse. In multiple mediation models, self-efficacy in negative/affective situations significantly mediated the effect of MDS and AS on relapse. The findings underscore the negative impact of depression and anxiety on postpartum smoking relapse and suggest that the effects of MDS on postpartum relapse may be largely explained by comorbid AS. The current investigation provided mixed support for affect regulation models of addiction. Cognitive and tobacco dependence-related aspects of negative and positive reinforcement significantly mediated the relationship of depression and anxiety with relapse, whereas affect and stress did not. The findings emphasize the unique role of low agency with respect to abstaining from smoking in negative affective situations as a key predictor of postpartum smoking relapse. © 2012 American Psychological Association

Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids

NASA Astrophysics Data System (ADS)

Babqi, Abdulrahman Jamal

This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software platform. This dissertation also proposes a control and power sharing strategy for small-scale microgrids in both grid-connected and islanded modes based on centralized FCS-MPC. In grid-connected mode, the controller was capable of managing the output power of each DG and enabling flexible power regulation between the microgrid and the utility grid. In islanded mode, the controller regulated the microgrid voltage and frequency, and provided a precise power sharing scheme among the DGs. In addition, the power sharing can be adjusted flexibly by changing the sharing ratio. The proposed control also enabled plug-and-play operation. Moreover, a smooth transition between the two modes of operation was achieved without any disturbance in the system. Case studies were carried out in order to validate the proposed control strategy with the PSCAD/EMTDA software package.

Acute toxicity prediction to threatened and endangered species using Interspecies Correlation Estimation (ICE) models

EPA Science Inventory

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA’s Internet application Web-ICE is a suite of Interspecies Correlati...

A Matlab-Based Graphical User Interface for Simulation and Control Design of a Hydrogen Mixer

NASA Technical Reports Server (NTRS)

Richter, Hanz; Figueroa, Fernando

2003-01-01

A Graphical User Interface (GUI) that facilitates prediction and control design tasks for a propellant mixer is described. The Hydrogen mixer is used in rocket test stand operations at the NASA John C. Stennis Space Center. The mixer injects gaseous hydrogen (GH2) into a stream of liquid hydrogen (LH2) to obtain a combined flow with desired thermodynamic properties. The flows of GH2 and LH2 into the mixer are regulated by two control valves, and a third control valve is installed at the exit of the mixer to regulate the combined flow. The three valves may be simultaneously operated in order to achieve any desired combination of total flow, exit temperature and mixer pressure within the range of operation. The mixer, thus, constitutes a three-input, three-output system. A mathematical model of the mixer has been obtained and validated with experimental data. The GUI presented here uses the model to predict mixer response under diverse conditions.

Global Quantitative Modeling of Chromatin Factor Interactions

PubMed Central

Zhou, Jian; Troyanskaya, Olga G.

2014-01-01

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions. PMID:24675896

Predicting sugar consumption: Application of an integrated dual-process, dual-phase model.

PubMed

Hagger, Martin S; Trost, Nadine; Keech, Jacob J; Chan, Derwin K C; Hamilton, Kyra

2017-09-01

Excess consumption of added dietary sugars is related to multiple metabolic problems and adverse health conditions. Identifying the modifiable social cognitive and motivational constructs that predict sugar consumption is important to inform behavioral interventions aimed at reducing sugar intake. We tested the efficacy of an integrated dual-process, dual-phase model derived from multiple theories to predict sugar consumption. Using a prospective design, university students (N = 90) completed initial measures of the reflective (autonomous and controlled motivation, intentions, attitudes, subjective norm, perceived behavioral control), impulsive (implicit attitudes), volitional (action and coping planning), and behavioral (past sugar consumption) components of the proposed model. Self-reported sugar consumption was measured two weeks later. A structural equation model revealed that intentions, implicit attitudes, and, indirectly, autonomous motivation to reduce sugar consumption had small, significant effects on sugar consumption. Attitudes, subjective norm, and, indirectly, autonomous motivation to reduce sugar consumption predicted intentions. There were no effects of the planning constructs. Model effects were independent of the effects of past sugar consumption. The model identified the relative contribution of reflective and impulsive components in predicting sugar consumption. Given the prominent role of the impulsive component, interventions that assist individuals in managing cues-to-action and behavioral monitoring are likely to be effective in regulating sugar consumption. Copyright © 2017 Elsevier Ltd. All rights reserved.

A model of clearance rate regulation in mussels

NASA Astrophysics Data System (ADS)

Fréchette, Marcel

2012-10-01

Clearance rate regulation has been modelled as an instantaneous response to food availability, independent of the internal state of the animals. This view is incompatible with latent effects during ontogeny and phenotypic flexibility in clearance rate. Internal-state regulation of clearance rate is required to account for these patterns. Here I develop a model of internal-state based regulation of clearance rate. External factors such as suspended sediments are included in the model. To assess the relative merits of instantaneous regulation and internal-state regulation, I modelled blue mussel clearance rate and growth using a DEB model. In the usual standard feeding module, feeding is governed by a Holling's Type II response to food concentration. In the internal-state feeding module, gill ciliary activity and thus clearance rate are driven by internal reserve level. Factors such as suspended sediments were not included in the simulations. The two feeding modules were compared on the basis of their ability to capture the impact of latent effects, of environmental heterogeneity in food abundance and of physiological flexibility on clearance rate and individual growth. The Holling feeding module was unable to capture the effect of any of these sources of variability. In contrast, the internal-state feeding module did so without any modification or ad hoc calibration. Latent effects, however, appeared transient. With simple annual variability in temperature and food concentration, the relationship between clearance rate and food availability predicted by the internal-state feeding module was quite similar to that observed in Norwegian fjords. I conclude that in contrast with the usual Holling feeding module, internal-state regulation of clearance rate is consistent with well-documented growth and clearance rate patterns.

Model of transcriptional activation by MarA in Escherichia coli.

PubMed

Wall, Michael E; Markowitz, David A; Rosner, Judah L; Martin, Robert G

2009-12-01

The AraC family transcription factor MarA activates approximately 40 genes (the marA/soxS/rob regulon) of the Escherichia coli chromosome resulting in different levels of resistance to a wide array of antibiotics and to superoxides. Activation of marA/soxS/rob regulon promoters occurs in a well-defined order with respect to the level of MarA; however, the order of activation does not parallel the strength of MarA binding to promoter sequences. To understand this lack of correspondence, we developed a computational model of transcriptional activation in which a transcription factor either increases or decreases RNA polymerase binding, and either accelerates or retards post-binding events associated with transcription initiation. We used the model to analyze data characterizing MarA regulation of promoter activity. The model clearly explains the lack of correspondence between the order of activation and the MarA-DNA affinity and indicates that the order of activation can only be predicted using information about the strength of the full MarA-polymerase-DNA interaction. The analysis further suggests that MarA can activate without increasing polymerase binding and that activation can even involve a decrease in polymerase binding, which is opposite to the textbook model of activation by recruitment. These findings are consistent with published chromatin immunoprecipitation assays of interactions between polymerase and the E. coli chromosome. We find that activation involving decreased polymerase binding yields lower latency in gene regulation and therefore might confer a competitive advantage to cells. Our model yields insights into requirements for predicting the order of activation of a regulon and enables us to suggest that activation might involve a decrease in polymerase binding which we expect to be an important theme of gene regulation in E. coli and beyond.

Oil and Gas Supply Modeling

NASA Astrophysics Data System (ADS)

Gass, S. I.

1982-05-01

The theoretical and applied state of the art of oil and gas supply models was discussed. The following areas were addressed: the realities of oil and gas supply, prediction of oil and gas production, problems in oil and gas modeling, resource appraisal procedures, forecasting field size and production, investment and production strategies, estimating cost and production schedules for undiscovered fields, production regulations, resource data, sensitivity analysis of forecasts, econometric analysis of resource depletion, oil and gas finding rates, and various models of oil and gas supply.

Assessment of soil erosion risk in Komering watershed, South Sumatera, using SWAT model

NASA Astrophysics Data System (ADS)

Salsabilla, A.; Kusratmoko, E.

2017-07-01

Changes in land use watershed led to environmental degradation. Estimated loss of soil erosion is often difficult due to some factors such as topography, land use, climate and human activities. This study aims to predict soil erosion hazard and sediment yield using the Soil and Water Assessment Tools (SWAT) hydrological model. The SWAT was chosen because it can simulate the model with limited data. The study area is Komering watershed (806,001 Ha) in South Sumatera Province. There are two factors land management intervention: 1) land with agriculture, and 2) land with cultivation. These factors selected in accordance with the regulations of spatial plan area. Application of the SWAT demonstrated that the model can predict surface runoff, soil erosion loss and sediment yield. The erosion risk for each watershed can be classified and predicted its changes based on the scenarios which arranged. In this paper, we also discussed the relationship between the distribution of erosion risk and watershed's characteristics in a spatial perspective.

Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

PubMed Central

Murphy, Kevin G.; Jones, Nick S.

2018-01-01

Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity. PMID:29367240

Adjoint Method and Predictive Control for 1-D Flow in NASA Ames 11-Foot Transonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Ardema, Mark

2006-01-01

This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch

Materialists on Facebook: the self-regulatory role of social comparisons and the objectification of Facebook friends.

PubMed

Ozimek, Phillip; Baer, Fiona; Förster, Jens

2017-11-01

In this study, we examine chronic materialism as a possible motive for Facebook usage. We test an explanatory mediation model predicting that materialists use Facebook more frequently, because they compare themselves to others, they objectify and instrumentalize others, and they accumulate friends. For this, we conducted two online surveys ( N 1 = 242, N 2 = 289) assessing demographic variables, Facebook use, social comparison, materialism, objectification and instrumentalization. Results confirm the predicted mediation model. Our findings suggest that Facebook can be used as a means to an end in a way of self-regulatory processes, like satisfying of materialistic goals. The findings are the first evidence for our Social Online Self-regulation Theory (SOS-T), which contains numerous predictions that can be tested in the future.

Dynamic Redox Regulation of IL-4 Signaling.

PubMed

Dwivedi, Gaurav; Gran, Margaret A; Bagchi, Pritha; Kemp, Melissa L

2015-11-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation.

Dynamic Redox Regulation of IL-4 Signaling

PubMed Central

Dwivedi, Gaurav; Gran, Margaret A.; Bagchi, Pritha; Kemp, Melissa L.

2015-01-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation. PMID:26562652

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

Satchwell, Andrew; Cappers, Peter; Schwartz, Lisa C.

Many regulators, utilities, customer groups, and other stakeholders are reevaluating existing regulatory models and the roles and financial implications for electric utilities in the context of today’s environment of increasing distributed energy resource (DER) penetrations, forecasts of significant T&D investment, and relatively flat or negative utility sales growth. When this is coupled with predictions about fewer grid-connected customers (i.e., customer defection), there is growing concern about the potential for serious negative impacts on the regulated utility business model. Among states engaged in these issues, the range of topics under consideration is broad. Most of these states are considering whether approachesmore » that have been applied historically to mitigate the impacts of previous “disruptions” to the regulated utility business model (e.g., energy efficiency) as well as to align utility financial interests with increased adoption of such “disruptive technologies” (e.g., shareholder incentive mechanisms, lost revenue mechanisms) are appropriate and effective in the present context. A handful of states are presently considering more fundamental changes to regulatory models and the role of regulated utilities in the ownership, management, and operation of electric delivery systems (e.g., New York “Reforming the Energy Vision” proceeding).« less

Cellular pressure and volume regulation and implications for cell mechanics

NASA Astrophysics Data System (ADS)

Jiang, Hongyuan; Sun, Sean

2013-03-01

In eukaryotic cells, small changes in cell volume can serve as important signals for cell proliferation, death and migration. Volume and shape regulation also directly impacts the mechanics of the cell and multi-cellular tissues. Recent experiments found that during mitosis, eukaryotic cells establish a preferred steady volume and pressure, and the steady volume and pressure can robustly adapt to large osmotic shocks. Here we develop a mathematical model of cellular pressure and volume regulation, incorporating essential elements such as water permeation, mechano-sensitive channels, active ion pumps and active stresses in the actomyosin cortex. The model can fully explain the available experimental data, and predicts the cellular volume and pressure for several models of cell cortical mechanics. Furthermore, we show that when cells are subjected to an externally applied load, such as in an AFM indentation experiment, active regulation of volume and pressure leads to complex cellular response. We found the cell stiffness highly depends on the loading rate, which indicates the transport of water and ions might contribute to the observed viscoelasticity of cells.

Kindergarten stressors and cumulative adrenocortical activation: the "first straws" of allostatic load?

PubMed

Bush, Nicole R; Obradović, Jelena; Adler, Nancy; Boyce, W Thomas

2011-11-01

Using an ethnically diverse longitudinal sample of 338 kindergarten children, this study examined the effects of cumulative contextual stressors on children's developing hypothalamic-pituitary-adrenocortical (HPA) axis regulation as an early life indicator of allostatic load. Chronic HPA axis regulation was assessed using cumulative, multiday measures of cortisol in both the fall and spring seasons of the kindergarten year. Hierarchical linear regression analyses revealed that contextual stressors related to ethnic minority status, socioeconomic status, and family adversity each uniquely predicted children's daily HPA activity and that some of those associations were curvilinear in conformation. Results showed that the quadratic, U-shaped influences of family socioeconomic status and family adversity operate in different directions to predict children's HPA axis regulation. Results further suggested that these associations differ for White and ethnic minority children. In total, this study revealed that early childhood experiences contribute to shifts in one of the principal neurobiological systems thought to generate allostatic load, confirming the importance of early prevention and intervention efforts. Moreover, findings suggested that analyses of allostatic load and developmental theories accounting for its accrual would benefit from an inclusion of curvilinear associations in tested predictive models.

Emotion Dysregulation and Adolescent Psychopathology: A Prospective Study

PubMed Central

Hatzenbuehler, Mark L.; Nolen-Hoeksema, Susan

2011-01-01

Background Emotion regulation deficits have been consistently linked to psychopathology in cross-sectional studies. However, the direction of the relationship between emotion regulation and psychopathology is unclear. This study examined the longitudinal and reciprocal relationships between emotion regulation deficits and psychopathology in adolescents. Methods Emotion dysregulation and symptomatology (depression, anxiety, aggressive behavior, and eating pathology) were assessed in a large, diverse sample of adolescents (N = 1,065) at two time points separated by seven months. Structural equation modeling was used to examine the longitudinal and reciprocal relationships between emotion dysregulation and symptoms of psychopathology. Results The three distinct emotion processes examined here (emotional understanding, dysregulated expression of sadness and anger, and ruminative responses to distress) formed a unitary latent emotion dysregulation factor. Emotion dysregulation predicted increases in anxiety symptoms, aggressive behavior, and eating pathology after controlling for baseline symptoms but did not predict depressive symptoms. In contrast, none of the four types of psychopathology predicted increases in emotion dysregulation after controlling for baseline emotion dysregulation. Conclusions Emotion dysregulation appears to be an important transdiagnostic factor that increases risk for a wide range of psychopathology outcomes in adolescence. These results suggest targets for preventive interventions during this developmental period of risk. PMID:21718967

Relative contributions of self-efficacy, self-regulation, and self-handicapping in predicting student procrastination.

PubMed

Strunk, Kamden K; Steele, Misty R

2011-12-01

The relative contributions of self-efficacy, self-regulation, and self-handicapping student procrastination were explored. College undergraduate participants (N = 138; 40 men, 97 women, one not reporting sex) filled out the Procrastination Scale, the Self-Handicapping Scale-Short Form, and the Self-regulation and Self-handicapping scales of the Motivated Strategies for Learning Questionnaire. A hierarchical regression of the above measures indicated that self-efficacy, self-regulation, and self-handicapping all predicted scores on the Procrastination Scale, but self-regulation fully accounted for the predictive power of self-efficacy. The results suggested self-regulation and self-handicapping predict procrastination independently. These findings are discussed in relation to the literature on the concept of "self-efficacy for self-regulation" and its use in the field of procrastination research.

CN-Wheat, a functional–structural model of carbon and nitrogen metabolism in wheat culms after anthesis. II. Model evaluation

PubMed Central

Barillot, Romain; Chambon, Camille; Andrieu, Bruno

2016-01-01

Background and Aims Simulating resource allocation in crops requires an integrated view of plant functioning and the formalization of interactions between carbon (C) and nitrogen (N) metabolisms. This study evaluates the functional–structural model CN-Wheat developed for winter wheat after anthesis. Methods In CN-Wheat the acquisition and allocation of resources between photosynthetic organs, roots and grains are emergent properties of sink and source activities and transfers of mobile metabolites. CN-Wheat was calibrated for field plants under three N fertilizations at anthesis. Model parameters were taken from the literature or calibrated on the experimental data. Key Results The model was able to predict the temporal variations and the distribution of resources in the culm. Thus, CN-Wheat accurately predicted the post-anthesis kinetics of dry masses and N content of photosynthetic organs and grains in response to N fertilization. In our simulations, when soil nitrates were non-limiting, N in grains was ultimately determined by availability of C for root activity. Dry matter accumulation in grains was mostly affected by photosynthetic organ lifespan, which was regulated by protein turnover and C-regulated root activity. Conclusions The present study illustrates that the hypotheses implemented in the model were able to predict realistic dynamics and spatial patterns of C and N. CN-Wheat provided insights into the interplay of C and N metabolism and how the depletion of mobile metabolites due to grain filling ultimately results in the cessation of resource capture. This enabled us to identify processes that limit grain mass and protein content and are potential targets for plant breeding. PMID:27497243

Parenting in infancy and self-regulation in preschool: an investigation of the role of attachment history.

PubMed

Birmingham, R S; Bub, K L; Vaughn, B E

2017-04-01

Parenting and attachment are critical in the emergence of self-regulation (SR) in preschool. However, most studies use general indexes of parenting quality, failing to explore the unique contributions of sensitivity and home quality to SR. Further, the nature of the interplay between parenting and attachment history is not well understood. Using a sample of 938 children from The National Institute of Child Health and Human Development Study of Early Child Care and Youth Development, a series of structural equation models were fit to determine whether sensitivity and home quality concurrently predicted SR at 54 months, and whether attachment mediated or moderated these pathways. Results suggest that both sensitivity and home quality uniquely predict SR. Further, these early parenting variables were each indirectly associated with SR through children's attachment history. That is, higher levels of sensitivity and home quality predicted secure attachment history, which, along with parenting, predicted more advanced SR skills at 54 months. No moderated pathways emerged, suggesting that attachment history may be best conceptualized as a mediating mechanism.

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction.

PubMed

Bossi, Flavia; Fan, Jue; Xiao, Jun; Chandra, Lilyana; Shen, Max; Dorone, Yanniv; Wagner, Doris; Rhee, Seung Y

2017-06-26

The molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. To identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.

A Longitudinal Study of Emotion Regulation, Emotion Lability/Negativity, and Internalizing Symptomatology in Maltreated and Nonmaltreated Children

PubMed Central

Kim-Spoon, Jungmeen; Cicchetti, Dante; Rogosch, Fred A.

2013-01-01

The longitudinal contributions of emotion regulation and emotion lability/negativity to internalizing symptomatology were examined in a low-income sample (171 maltreated and 151 nonmaltreated children, from age 7 to 10 years). Latent difference score models indicated that, for both maltreated and nonmaltreated children, emotion regulation was a mediator between emotion lability/negativity and internalizing symptomatology, whereas emotion lability/negativity was not a mediator between emotion regulation and internalizing symptomatology. Early maltreatment was associated with high emotion lability/negativity (age 7) that contributed to poor emotion regulation (age 8), which in turn was predictive of increases in internalizing symptomatology (from age 8 to 9). The results imply important roles of emotion regulation in the development of internalizing symptomatology, especially for children with high emotion lability/negativity. PMID:23034132

The predictive value of selected serum microRNAs for acute GVHD by TaqMan MicroRNA arrays.

PubMed

Zhang, Chunyan; Bai, Nan; Huang, Wenrong; Zhang, Pengjun; Luo, Yuan; Men, Shasha; Wen, Ting; Tong, Hongli; Wang, Shuhong; Tian, Ya-Ping

2016-10-01

Currently, the diagnosis of acute graft-versus-host disease (aGVHD) is mainly based on clinical symptoms and biopsy results. This study was designed to further explore new no noninvasive biomarkers for aGVHD prediction/diagnosis. We profiled miRNAs in serum pools from patients with aGVHD (grades II-IV) (n = 9) and non-aGVHD controls (n = 9) by real-time qPCR-based TaqMan MicroRNA arrays. Then, predictive models were established using related miRNAs (n = 38) and verified by a double-blind trial (n = 54). We found that miR-411 was significantly down regulated when aGVHD developed and recovered when aGVHD was controlled, which demonstrated that miR-411 has potential as an indicator for aGVHD monitoring. We developed and validated a predictive model and a diagnostic model for aGVHD. The predictive model included two miRNAs (miR-26b and miR-374a), which could predict an increased risk for aGVHD 1 or 2 weeks in advance, with an AUC, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) of 0.722, 76.19 %, and 69.70 %, respectively. The diagnostic model included three miRNAs (miR-28-5p, miR-489, and miR-671-3p) with an AUC, PPV, and NPV of 0.841, 85.71 % and 83.33 %, respectively. Our results show that circulating miRNAs (miR-26b and miR-374a, miR-28-5p, miR-489 and miR-671-3p) may serve as biomarkers for the prediction and diagnosis of grades II-IV aGVHD.

Acute Response of the Hippocampal Transcriptome Following Mild Traumatic Brain Injury After Controlled Cortical Impact in the Rat.

PubMed

Samal, Babru B; Waites, Cameron K; Almeida-Suhett, Camila; Li, Zheng; Marini, Ann M; Samal, Nihar R; Elkahloun, Abdel; Braga, Maria F M; Eiden, Lee E

2015-10-01

We have previously demonstrated that mild controlled cortical impact (mCCI) injury to rat cortex causes indirect, concussive injury to underlying hippocampus and other brain regions, providing a reproducible model for mild traumatic brain injury (mTBI) and its neurochemical, synaptic, and behavioral sequelae. Here, we extend a preliminary gene expression study of the hippocampus-specific events occurring after mCCI and identify 193 transcripts significantly upregulated, and 21 transcripts significantly downregulated, 24 h after mCCI. Fifty-three percent of genes altered by mCCI within 24 h of injury are predicted to be expressed only in the non-neuronal/glial cellular compartment, with only 13% predicted to be expressed only in neurons. The set of upregulated genes following mCCI was interrogated using Ingenuity Pathway Analysis (IPA) augmented with manual curation of the literature (190 transcripts accepted for analysis), revealing a core group of 15 first messengers, mostly inflammatory cytokines, predicted to account for >99% of the transcript upregulation occurring 24 h after mCCI. Convergent analysis of predicted transcription factors (TFs) regulating the mCCI target genes, carried out in IPA relative to the entire Affymetrix-curated transcriptome, revealed a high concordance with TFs regulated by the cohort of 15 cytokines/cytokine-like messengers independently accounting for upregulation of the mCCI transcript cohort. TFs predicted to regulate transcription of the 193-gene mCCI cohort also displayed a high degree of overlap with TFs predicted to regulate glia-, rather than neuron-specific genes in cortical tissue. We conclude that mCCI predominantly affects transcription of non-neuronal genes within the first 24 h after insult. This finding suggests that early non-neuronal events trigger later permanent neuronal changes after mTBI, and that early intervention after mTBI could potentially affect the neurochemical cascade leading to later reported synaptic and behavioral dysfunction.

Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

PubMed Central

Band, Leah R.; Fozard, John A.; Godin, Christophe; Jensen, Oliver E.; Pridmore, Tony; Bennett, Malcolm J.; King, John R.

2012-01-01

Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties. PMID:23110897

Self-regulation and social pressure reduce prejudiced responding and increase the motivation to be non-prejudiced.

PubMed

Buzinski, Steven G; Kitchens, Michael B

2017-01-01

Self-regulation constrains the expression of prejudice, but when self-regulation falters, the immediate environment can act as an external source of prejudice regulation. This hypothesis derives from work demonstrating that external controls and internal self-regulation can prompt goal pursuit in the absence of self-imposed controls. Across four studies, we found support for this complementary model of prejudice regulation. In Study 1, self-regulatory fatigue resulted in less motivation to be non-prejudiced, compared to a non-fatigued control. In Study 2, strong (vs. weak) perceived social pressure was related to greater motivation to be non-prejudiced. In Study 3, dispositional self-regulation predicted non-prejudice motivation when perceived social pressure was weak or moderate, but not when it was strong. Finally, in Study 4 self-regulatory fatigue increased prejudice when social pressure was weak but not when it was strong.

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.

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Predicting risky sexual behavior in emerging adulthood: examination of a moderated mediation model among child sexual abuse and adult sexual assault victims.

PubMed

Littleton, Heather L; Grills, Amie E; Drum, Katherine B

2014-01-01

Although having a sexual victimization history is associated with engaging in sexual risk behavior, the mechanisms whereby sexual victimization increases risk behavior are unclear. This study examined use of sex as an affect regulation strategy as a mediator of the relationship between depressive symptoms and sexual risk behavior among 1,616 sexually active college women as well as examined having a history of child sexual abuse (CSA), adolescent/adult sexual assault (ASA), or both (CSA/ASA) as moderators. Results supported the mediated model as well as moderated mediation, where depressive symptoms were more strongly associated with use of sex as an affect regulation strategy among ASA victims, and sex as an affect regulation strategy was more strongly related to sexual risk behavior for CSA/ASA victims.

Bayesian Networks Predict Neuronal Transdifferentiation.

PubMed

Ainsworth, Richard I; Ai, Rizi; Ding, Bo; Li, Nan; Zhang, Kai; Wang, Wei

2018-05-30

We employ the language of Bayesian networks to systematically construct gene-regulation topologies from deep-sequencing single-nucleus RNA-Seq data for human neurons. From the perspective of the cell-state potential landscape, we identify attractors that correspond closely to different neuron subtypes. Attractors are also recovered for cell states from an independent data set confirming our models accurate description of global genetic regulations across differing cell types of the neocortex (not included in the training data). Our model recovers experimentally confirmed genetic regulations and community analysis reveals genetic associations in common pathways. Via a comprehensive scan of all theoretical three-gene perturbations of gene knockout and overexpression, we discover novel neuronal trans-differrentiation recipes (including perturbations of SATB2, GAD1, POU6F2 and ADARB2) for excitatory projection neuron and inhibitory interneuron subtypes. Copyright © 2018, G3: Genes, Genomes, Genetics.

Methods for Modeling Brassinosteroid-Mediated Signaling in Plant Development.

PubMed

Frigola, David; Caño-Delgado, Ana I; Ibañes, Marta

2017-01-01

Mathematical modeling of biological processes is a useful tool to draw conclusions that are contained in the data, but not directly reachable, as well as to make predictions and select the most efficient follow-up experiments. Here we outline a method to model systems of a few proteins that interact transcriptionally and/or posttranscriptionally, by representing the system as Ordinary Differential Equations and to study the model dynamics and stationary states. We exemplify this method by focusing on the regulation by the brassinosteroid (BR) signaling component BRASSINOSTEROID INSENSITIVE1 ETHYL METHYL SULFONATE SUPPRESSOR1 (BES1) of BRAVO, a quiescence-regulating transcription factor expressed in the quiescent cells of Arabidopsis thaliana roots. The method to extract the stationary states and the dynamics is provided as a Mathematica code and requires basic knowledge of the Mathematica software to be executed.

A model of self-regulation for control of chronic disease.

PubMed

Clark, Noreen M; Gong, Molly; Kaciroti, Niko

2014-10-01

Chronic disease poses increasing threat to individual and community health. The day-to-day manager of disease is the patient who undertakes actions with the guidance of a clinician. The ability of the patient to control the illness through an effective therapeutic plan is significantly influenced by social and behavioral factors. This article presents a model of patient management of chronic disease that accounts for intrapersonal and external influences on management and emphasizes the central role of self-regulatory processes in disease control. Asthma serves as a case for exploration of the model. Findings from a 5-year study of 637 children with asthma and their care-taking parents supported that the self-regulation elements of the model were reasonably stable over time and baseline values were predictive of important disease management outcomes. © 2014 Society for Public Health Education.

Landscape level influence: aquatic primary production in the Colorado River of Glen and Grand canyons

NASA Astrophysics Data System (ADS)

Yard, M. D.; Kennedy, T.; Yackulic, C. B.; Bennett, G. E.

2012-12-01

Irregular features common to canyon-bound regions intercept solar incidence (photosynthetic photon flux density [PPFD: μmol m-2 s-1]) and can affect ecosystem energetics. The Colorado River in Grand Canyon is topographically complex, typical of most streams and rivers in the arid southwest. Dam-regulated systems like the Colorado River have reduced sediment loads, and consequently increased water transparency relative to unimpounded rivers; however, sediment supply from tributaries and flow regulation that affects erosion and subsequent sediment transport, interact to create spatial and temporal variation in optical conditions in this river network. Solar incidence and suspended sediment loads regulate the amount of underwater light available for aquatic photosynthesis in this regulated river. Since light availability is depth dependent (Beer's law), benthic algae is often exposed to varying levels of desiccation or reduced light conditions due to daily flow regulation, additional factors that further constrain aquatic primary production. Considerable evidence suggests that the Colorado River food web is now energetically dependent on autotrophic production, an unusual condition since large river foodwebs are typically supported by allochthonous carbon synthesized and transported from terrestrial environments. We developed a mechanistic model to account for these regulating factors to predict how primary production might be affected by observed and alternative flow regimes proposed as part of ongoing adaptive management experimentation. Inputs to our model include empirical data (suspended sediment and temperature), and predictive relationships: 1) solar incidence reaching the water surface (topographic complexity), 2) suspended sediment-light extinction relationships (optical properties), 3) unsteady flow routing model (stage-depth relationship), 4) channel morphology (photosynthetic area), and 5) photosynthetic-irradiant response for dominant algae (Cladophora glomerata and associated epiphytes). Initial findings suggest that aquatic primary production varies spatially and temporally in response to natural processes occurring at varying spatial scales and that flow regulation per se has only a minor effect on primary production. All of these physical drivers combined are likely to structure the abundance, distribution, and interaction of aquatic biota found in this ecosystem.

A model for the pilot's use of motion cues in roll-axis tracking tasks

NASA Technical Reports Server (NTRS)

Levison, W. H.; Junker, A. M.

1977-01-01

Simulated target-following and disturbance-regulation tasks were explored with subjects using visual-only and combined visual and motion cues. The effects of motion cues on task performance and pilot response behavior were appreciably different for the two task configurations and were consistent with data reported in earlier studies for similar task configurations. The optimal-control model for pilot/vehicle systems provided a task-independent framework for accounting for the pilot's use of motion cues. Specifically, the availability of motion cues was modeled by augmenting the set of perceptual variables to include position, rate, acceleration, and accleration-rate of the motion simulator, and results were consistent with the hypothesis of attention-sharing between visual and motion variables. This straightforward informational model allowed accurate model predictions of the effects of motion cues on a variety of response measures for both the target-following and disturbance-regulation tasks.

Low temperature regulated growth of PbS quantum dots by wet chemical method

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

Kumar, Hitanshu, E-mail: hitanshuminhas@gmail.com; Barman, P. B.; Singh, Ragini Raj

2015-08-28

Narrow size distribution with regulated synthesis of lead sulfide (PbS) quantum dots (QDs) was achieved through wet chemical method. Different concentrations of 2-mercaptoethanol (capping agent) were used for tailoring the QDs size. Transmission electron microscopy and X-ray diffraction studies revealed that the QDs have mean diameters between 6 to 15 nm. The optical absorption spectra were compared to the predictions of a theoretical model for the electronic structure. The theory agrees well with experiment for QDs larger than 7 nm, but for smaller dots there is some deviation from the theoretical predictions. Consequently, the produced particles are having monodispersity, good water solubility,more » stability and may be good arguments to be biologically compatible due to the use of 2-mercaptoethanol.« less

An empirical investigation of spatial differentiation and price floor regulations in retail markets for gasoline

NASA Astrophysics Data System (ADS)

Houde, Jean-Francois

In the first essay of this dissertation, I study an empirical model of spatial competition. The main feature of my approach is to formally specify commuting paths as the "locations" of consumers in a Hotelling-type model of spatial competition. The main consequence of this location assumption is that the substitution patterns between stations depend in an intuitive way on the structure of the road network and the direction of traffic flows. The demand-side of the model is estimated by combining a model of traffic allocation with econometric techniques used to estimate models of demand for differentiated products (Berry, Levinsohn and Pakes (1995)). The estimated parameters are then used to evaluate the importance of commuting patterns in explaining the distribution of gasoline sales, and compare the economic predictions of the model with the standard home-location model. In the second and third essays, I examine empirically the effect of a price floor regulation on the dynamic and static equilibrium outcomes of the gasoline retail industry. In particular, in the second essay I study empirically the dynamic entry and exit decisions of gasoline stations, and measure the impact of a price floor on the continuation values of staying in the industry. In the third essay, I develop and estimate a static model of quantity competition subject to a price floor regulation. Both models are estimated using a rich panel dataset on the Quebec gasoline retail market before and after the implementation of a price floor regulation.

The applications of machine learning algorithms in the modeling of estrogen-like chemicals.

PubMed

Liu, Huanxiang; Yao, Xiaojun; Gramatica, Paola

2009-06-01

Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.

Relations Between Autonomous Motivation and Leisure-Time Physical Activity Participation: The Mediating Role of Self-Regulation Techniques.

PubMed

Nurmi, Johanna; Hagger, Martin S; Haukkala, Ari; Araújo-Soares, Vera; Hankonen, Nelli

2016-04-01

This study tested the predictive validity of a multitheory process model in which the effect of autonomous motivation from self-determination theory on physical activity participation is mediated by the adoption of self-regulatory techniques based on control theory. Finnish adolescents (N = 411, aged 17-19) completed a prospective survey including validated measures of the predictors and physical activity, at baseline and after one month (N = 177). A subsample used an accelerometer to objectively measure physical activity and further validate the physical activity self-report assessment tool (n = 44). Autonomous motivation statistically significantly predicted action planning, coping planning, and self-monitoring. Coping planning and self-monitoring mediated the effect of autonomous motivation on physical activity, although self-monitoring was the most prominent. Controlled motivation had no effect on self-regulation techniques or physical activity. Developing interventions that support autonomous motivation for physical activity may foster increased engagement in self-regulation techniques and positively affect physical activity behavior.

Depressive Symptoms and Problematic Internet Use Among Adolescents: Analysis of the Longitudinal Relationships from the Cognitive–Behavioral Model

PubMed Central

2014-01-01

Abstract Problematic Internet use—frequently called Internet addiction or compulsive use—represents an increasingly widespread problem among adolescents. The objective of this study was to analyze the temporal and reciprocal relations between the presence of depressive symptoms and various components of problematic Internet use (i.e., the preference for online relationships, use of the Internet for mood regulation, deficient self-regulation, and the manifestation of negative outcomes). Consequently, a longitudinal design was employed with two times separated by a 1 year interval. The sample consisted of 699 adolescents (61.1% girls) between 13 and 17 years of age. The results indicated that depressive symptoms at time 1 predicted an increase in preference for online relationships, mood regulation, and negative outcomes after 1 year. In turn, negative outcomes at time 1 predicted an increase in depressive symptoms at time 2. These results entail several practical implications for the design of prevention programs and the treatment of problematic Internet use. PMID:25405784

Dissecting innate immune responses with the tools of systems biology.

PubMed

Smith, Kelly D; Bolouri, Hamid

2005-02-01

Systems biology strives to derive accurate predictive descriptions of complex systems such as innate immunity. The innate immune system is essential for host defense, yet the resulting inflammatory response must be tightly regulated. Current understanding indicates that this system is controlled by complex regulatory networks, which maintain homoeostasis while accurately distinguishing pathogenic infections from harmless exposures. Recent studies have used high throughput technologies and computational techniques that presage predictive models and will be the foundation of a systems level understanding of innate immunity.

Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation.

PubMed

Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Wesensten, Nancy J; Kamimori, Gary H; Balkin, Thomas J; Reifman, Jaques

2014-10-07

Caffeine is the most widely consumed stimulant to counter sleep-loss effects. While the pharmacokinetics of caffeine in the body is well-understood, its alertness-restoring effects are still not well characterized. In fact, mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available. In this paper, we describe a phenomenological model of the dose-dependent effects of caffeine on psychomotor vigilance task (PVT) performance of sleep-deprived subjects. We used the two-process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose-dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses. We developed and validated the model fits and predictions on PVT lapse (number of reaction times exceeding 500 ms) data from two separate laboratory studies. At the population-average level, the model captured the effects of a range of caffeine doses (50-300 mg), yielding up to a 90% improvement over the two-process model. Individual-specific caffeine models, on average, predicted the effects up to 23% better than population-average caffeine models. The proposed model serves as a useful tool for predicting the dose-dependent effects of caffeine on the PVT performance of sleep-deprived subjects and, therefore, can be used for determining caffeine doses that optimize the timing and duration of peak performance. Published by Elsevier Ltd.

Formability prediction for AHSS materials using damage models

NASA Astrophysics Data System (ADS)

Amaral, R.; Santos, Abel D.; José, César de Sá; Miranda, Sara

2017-05-01

Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches.

Soil fauna: key to new carbon models

NASA Astrophysics Data System (ADS)

Filser, Juliane; Faber, Jack H.; Tiunov, Alexei V.; Brussaard, Lijbert; Frouz, Jan; De Deyn, Gerlinde; Uvarov, Alexei V.; Berg, Matty P.; Lavelle, Patrick; Loreau, Michel; Wall, Diana H.; Querner, Pascal; Eijsackers, Herman; José Jiménez, Juan

2016-11-01

Soil organic matter (SOM) is key to maintaining soil fertility, mitigating climate change, combatting land degradation, and conserving above- and below-ground biodiversity and associated soil processes and ecosystem services. In order to derive management options for maintaining these essential services provided by soils, policy makers depend on robust, predictive models identifying key drivers of SOM dynamics. Existing SOM models and suggested guidelines for future SOM modelling are defined mostly in terms of plant residue quality and input and microbial decomposition, overlooking the significant regulation provided by soil fauna. The fauna controls almost any aspect of organic matter turnover, foremost by regulating the activity and functional composition of soil microorganisms and their physical-chemical connectivity with soil organic matter. We demonstrate a very strong impact of soil animals on carbon turnover, increasing or decreasing it by several dozen percent, sometimes even turning C sinks into C sources or vice versa. This is demonstrated not only for earthworms and other larger invertebrates but also for smaller fauna such as Collembola. We suggest that inclusion of soil animal activities (plant residue consumption and bioturbation altering the formation, depth, hydraulic properties and physical heterogeneity of soils) can fundamentally affect the predictive outcome of SOM models. Understanding direct and indirect impacts of soil fauna on nutrient availability, carbon sequestration, greenhouse gas emissions and plant growth is key to the understanding of SOM dynamics in the context of global carbon cycling models. We argue that explicit consideration of soil fauna is essential to make realistic modelling predictions on SOM dynamics and to detect expected non-linear responses of SOM dynamics to global change. We present a decision framework, to be further developed through the activities of KEYSOM, a European COST Action, for when mechanistic SOM models include soil fauna. The research activities of KEYSOM, such as field experiments and literature reviews, together with dialogue between empiricists and modellers, will inform how this is to be done.

USING SIMPLE MATHEMATICAL MODELS FOR ESTIMATING IMPACTS TO GROUND WATER AT PETROLEUM RELEASE SITES - WORKSHOP

EPA Science Inventory

Regulators and consultants alike are routinely tasked with predicting potential future impacts to ground water resources from leaking underground storage tank (LUST) sites. Site data is usually sparse, variable, and uncertain at best. However, this type of data can be evaluated ...

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models

EPA Science Inventory

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertai...

Presence of indicator plant species as a predictor of wetland vegetation integrity

USGS Publications Warehouse

Stapanian, Martin A.; Adams, Jean V.; Gara, Brian

2013-01-01

We fit regression and classification tree models to vegetation data collected from Ohio (USA) wetlands to determine (1) which species best predict Ohio vegetation index of biotic integrity (OVIBI) score and (2) which species best predict high-quality wetlands (OVIBI score >75). The simplest regression tree model predicted OVIBI score based on the occurrence of three plant species: skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris). The lowest OVIBI scores were best predicted by the absence of the selected plant species rather than by the presence of other species. The simplest classification tree model predicted high-quality wetlands based on the occurrence of two plant species: skunk-cabbage and marsh-fern (Thelypteris palustris). The overall misclassification rate from this tree was 13 %. Again, low-quality wetlands were better predicted than high-quality wetlands by the absence of selected species rather than the presence of other species using the classification tree model. Our results suggest that a species’ wetland status classification and coefficient of conservatism are of little use in predicting wetland quality. A simple, statistically derived species checklist such as the one created in this study could be used by field biologists to quickly and efficiently identify wetland sites likely to be regulated as high-quality, and requiring more intensive field assessments. Alternatively, it can be used for advanced determinations of low-quality wetlands. Agencies can save considerable money by screening wetlands for the presence/absence of such “indicator” species before issuing permits.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

PubMed

Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

The legal and ethical concerns that arise from using complex predictive analytics in health care.

PubMed

Cohen, I Glenn; Amarasingham, Ruben; Shah, Anand; Xie, Bin; Lo, Bernard

2014-07-01

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Project HOPE—The People-to-People Health Foundation, Inc.

Phosphoproteomic biomarkers predicting histologic nonalcoholic steatohepatitis and fibrosis.

PubMed

Younossi, Zobair M; Baranova, Ancha; Stepanova, Maria; Page, Sandra; Calvert, Valerie S; Afendy, Arian; Goodman, Zachary; Chandhoke, Vikas; Liotta, Lance; Petricoin, Emanuel

2010-06-04

The progression of nonalcoholic fatty liver disease (NAFLD) has been linked to deregulated exchange of the endocrine signaling between adipose and liver tissue. Proteomic assays for the phosphorylation events that characterize the activated or deactivated state of the kinase-driven signaling cascades in visceral adipose tissue (VAT) could shed light on the pathogenesis of nonalcoholic steatohepatitis (NASH) and related fibrosis. Reverse-phase protein microarrays (RPMA) were used to develop biomarkers for NASH and fibrosis using VAT collected from 167 NAFLD patients (training cohort, N = 117; testing cohort, N = 50). Three types of models were developed for NASH and advanced fibrosis: clinical models, proteomics models, and combination models. NASH was predicted by a model that included measurements of two components of the insulin signaling pathway: AKT kinase and insulin receptor substrate 1 (IRS1). The models for fibrosis were less reliable when predictions were based on phosphoproteomic, clinical, or the combination data. The best performing model relied on levels of the phosphorylation of GSK3 as well as on two subunits of cyclic AMP regulated protein kinase A (PKA). Phosphoproteomics technology could potentially be used to provide pathogenic information about NASH and NASH-related fibrosis. This information can lead to a clinically relevant diagnostic/prognostic biomarker for NASH.

Peer victimization and peer rejection during early childhood.

PubMed

Godleski, Stephanie A; Kamper, Kimberly E; Ostrov, Jamie M; Hart, Emily J; Blakely-McClure, Sarah J

2015-01-01

The development and course of the subtypes of peer victimization is a relatively understudied topic despite the association of victimization with important developmental and clinical outcomes. Moreover, understanding potential predictors, such as peer rejection and emotion regulation, in early childhood may be especially important to elucidate possible bidirectional pathways between relational and physical victimization and rejection. The current study (N = 97) was designed to explore several gaps and limitations in the peer victimization and peer rejection literature. In particular, the prospective associations between relational and physical victimization and peer rejection over the course of 3.5 months during early childhood (i.e., 3 to 5 years old) were investigated in an integrated model. The study consisted of 97 (42 girls) preschool children recruited from four early childhood schools in the northeast of the United States. Using observations, research assistant report, and teacher report, relational and physical aggression, relational and physical victimization, peer rejection, and emotion regulation were measured in a short-term longitudinal study. Path analyses were conducted to test the overall hypothesized model. Peer rejection was found to predict increases in relational victimization. In addition, emotion regulation was found to predict decreases in peer rejection and physical victimization. Implications for research and practice are discussed, including teaching coping strategies for peer rejection and emotional distress.

Peer victimization and peer rejection during early childhood

PubMed Central

Godleski, Stephanie A.; Kamper, Kimberly E.; Ostrov, Jamie M.; Hart, Emily J.; Blakely-McClure, Sarah J.

2014-01-01

Objective The development and course of the subtypes of peer victimization is a relatively understudied topic despite the association of victimization with important developmental and clinical outcomes. Moreover, understanding potential predictors, such as peer rejection and emotion regulation, in early childhood may be especially important to elucidate possible bi-directional pathways between relational and physical victimization and rejection. The current study (N = 97) was designed to explore several gaps and limitations in the peer victimization and peer rejection literature. In particular, the prospective associations between relational and physical victimization and peer rejection over the course of 3.5 months during early childhood (i.e., 3- to 5- years-old) were investigated in an integrated model. Method The study consisted of 97 (42 girls) preschool children recruited from four early childhood schools in the northeast of the US. Using observations, research assistant report and teacher report, relational and physical aggression, relational and physical victimization, peer rejection, and emotion regulation were measured in a short-term longitudinal study. Path analyses were conducted to test the overall hypothesized model. Results Peer rejection was found to predict increases in relational victimization. In addition, emotion regulation was found to predict decreases in peer rejection and physical victimization. Conclusions Implications for research and practice are discussed, including teaching coping strategies for peer rejection and emotional distress. PMID:25133659

Predicting wood pellet stove ownership and acquisition in Albuquerque, NM

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

Lansford, R.; Skaggs, R.; Owensby, F.

1994-12-31

Wood pellet stove (WPS) ownership and acquisition in Albuquerque, New Mexico was predicted using a model of qualitative choice. Using data obtained from a telephone survey, households were divided into four groups: current WPS owners, non-owners considering ownership, non-owners not considering ownership, and those who had not heard of WPS technology. Variables used to predict what category a household will be in include homeowners` socioeconomic and home-heating characteristics. Results indicate few WPS stoves are currently in use in Albuquerque. However, current WPS owners and those considering WPS acquisition tend to have higher incomes, more years of education, larger homes, andmore » use their fireplaces more frequently than average. Clean air regulations in Albuquerque will require changes in home woodburning. The WPS is an efficient and clean device; however, lack of knowledge of WPS technology, satisfaction with current heating systems, and limited awareness of the potential impact of clean air regulations indicate WPS usage in Albuquerque will remain limited.« less

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

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

Bossi, Flavia; Fan, Jue; Xiao, Jun

Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation.more » We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.« less

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

DOE PAGES

Bossi, Flavia; Fan, Jue; Xiao, Jun; ...

2017-06-26

Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation.more » We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.« less

The role of emotion regulation in situational empathy-related responding and prosocial behaviour in the presence of negative affect.

PubMed

Hein, Sascha; Röder, Mandy; Fingerle, Michael

2016-12-15

Empathy and prosocial behaviour are crucial factors for children's positive social adjustment. Contemporary models of empathy highlight the capacity to regulate vicariously experienced emotions as a precursor to empathy-related responses (e.g., prosocial behaviour). The goal of this study was to examine the role of emotion regulation (ER) in situational empathy-related responding and prosocial behaviour. A sample of 157 children (76 boys and 81 girls; M age = 9.94 years) participated in a two-tiered interview procedure that utilised vignettes to assess empathy and prosocial behaviour. Between both phases of the interview, a negative affect was induced to investigate the influence of ER on the change between the two phases. Results from a latent change model showed that ER strategies positively predicted change scores, that is, children with higher abilities to regulate emotions showed a higher increase in empathy and prosocial behaviour. Implications for the promotion of social-emotional learning in school are discussed. © 2016 International Union of Psychological Science.

A quantitative study of the benefits of co-regulation using the spoIIA operon as an example

PubMed Central

Iber, Dagmar

2006-01-01

The distribution of most genes is not random, and functionally linked genes are often found in clusters. Several theories have been put forward to explain the emergence and persistence of operons in bacteria. Careful analysis of genomic data favours the co-regulation model, where gene organization into operons is driven by the benefits of coordinated gene expression and regulation. Direct evidence that coexpression increases the individual's fitness enough to ensure operon formation and maintenance is, however, still lacking. Here, a previously described quantitative model of the network that controls the transcription factor σF during sporulation in Bacillus subtilis is employed to quantify the benefits arising from both organization of the sporulation genes into the spoIIA operon and from translational coupling. The analysis shows that operon organization, together with translational coupling, is important because of the inherent stochastic nature of gene expression, which skews the ratios between protein concentrations in the absence of co-regulation. The predicted impact of different forms of gene regulation on fitness and survival agrees quantitatively with published sporulation efficiencies. PMID:16924264

A quantitative study of the benefits of co-regulation using the spoIIA operon as an example.

PubMed

Iber, Dagmar

2006-01-01

The distribution of most genes is not random, and functionally linked genes are often found in clusters. Several theories have been put forward to explain the emergence and persistence of operons in bacteria. Careful analysis of genomic data favours the co-regulation model, where gene organization into operons is driven by the benefits of coordinated gene expression and regulation. Direct evidence that coexpression increases the individual's fitness enough to ensure operon formation and maintenance is, however, still lacking. Here, a previously described quantitative model of the network that controls the transcription factor sigma(F) during sporulation in Bacillus subtilis is employed to quantify the benefits arising from both organization of the sporulation genes into the spoIIA operon and from translational coupling. The analysis shows that operon organization, together with translational coupling, is important because of the inherent stochastic nature of gene expression, which skews the ratios between protein concentrations in the absence of co-regulation. The predicted impact of different forms of gene regulation on fitness and survival agrees quantitatively with published sporulation efficiencies.

Mechanistic, Mathematical Model to Predict the Dynamics of Tissue Genesis in Bone Defects via Mechanical Feedback and Mediation of Biochemical Factors

PubMed Central

Moore, Shannon R.; Saidel, Gerald M.; Knothe, Ulf; Knothe Tate, Melissa L.

2014-01-01

The link between mechanics and biology in the generation and the adaptation of bone has been well studied in context of skeletal development and fracture healing. Yet, the prediction of tissue genesis within - and the spatiotemporal healing of - postnatal defects, necessitates a quantitative evaluation of mechano-biological interactions using experimental and clinical parameters. To address this current gap in knowledge, this study aims to develop a mechanistic mathematical model of tissue genesis using bone morphogenetic protein (BMP) to represent of a class of factors that may coordinate bone healing. Specifically, we developed a mechanistic, mathematical model to predict the dynamics of tissue genesis by periosteal progenitor cells within a long bone defect surrounded by periosteum and stabilized via an intramedullary nail. The emergent material properties and mechanical environment associated with nascent tissue genesis influence the strain stimulus sensed by progenitor cells within the periosteum. Using a mechanical finite element model, periosteal surface strains are predicted as a function of emergent, nascent tissue properties. Strains are then input to a mechanistic mathematical model, where mechanical regulation of BMP-2 production mediates rates of cellular proliferation, differentiation and tissue production, to predict healing outcomes. A parametric approach enables the spatial and temporal prediction of endochondral tissue regeneration, assessed as areas of cartilage and mineralized bone, as functions of radial distance from the periosteum and time. Comparing model results to histological outcomes from two previous studies of periosteum-mediated bone regeneration in a common ovine model, it was shown that mechanistic models incorporating mechanical feedback successfully predict patterns (spatial) and trends (temporal) of bone tissue regeneration. The novel model framework presented here integrates a mechanistic feedback system based on the mechanosensitivity of periosteal progenitor cells, which allows for modeling and prediction of tissue regeneration on multiple length and time scales. Through combination of computational, physical and engineering science approaches, the model platform provides a means to test new hypotheses in silico and to elucidate conditions conducive to endogenous tissue genesis. Next generation models will serve to unravel intrinsic differences in bone genesis by endochondral and intramembranous mechanisms. PMID:24967742

Preview Scheduled Model Predictive Control For Horizontal Axis Wind Turbines

NASA Astrophysics Data System (ADS)

Laks, Jason H.

This research investigates the use of model predictive control (MPC) in application to wind turbine operation from start-up to cut-out. The studies conducted are focused on the design of an MPC controller for a 650˜KW, three-bladed horizontal axis turbine that is in operation at the National Renewable Energy Laboratory's National Wind Technology Center outside of Golden, Colorado. This turbine is at the small end of utility scale turbines, but it provides advanced instrumentation and control capabilities, and there is a good probability that the approach developed in simulation for this thesis, will be field tested on the actual turbine. A contribution of this thesis is a method to combine the use of preview measurements with MPC while also providing regulation of turbine speed and cyclic blade loading. A common MPC technique provides integral-like control to achieve offset-free operation. At the same time in wind turbine applications, multiple studies have developed "feed-forward" controls based on applying a gain to an estimate of the wind speed changes obtained from an observer incorporating a disturbance model. These approaches are based on a technique that can be referred to as disturbance accommodating control (DAC). In this thesis, it is shown that offset-free tracking MPC is equivalent to a DAC approach when the disturbance gain is computed to satisfy a regulator equation. Although the MPC literature has recognized that this approach provides "structurally stable" disturbance rejection and tracking, this step is not typically divorced from the MPC computations repeated each sample hit. The DAC formulation is conceptually simpler, and essentially uncouples regulation considerations from MPC related issues. This thesis provides a self contained proof that the DAC formulation (an observer-controller and appropriate disturbance gain) provides structurally stable regulation.

Examining the Predictive Relations between Two Aspects of Self-Regulation and Growth in Preschool Children’s Early Literacy Skills

PubMed Central

Lonigan, Christopher J.; Allan, Darcey M.; Phillips, Beth M.

2016-01-01

There is strong evidence that self-regulatory processes are linked to early academic skills both concurrently and longitudinally. The majority of extant longitudinal studies, however, have been conducted using autoregressive techniques that may not accurately model change across time. The purpose of this study was to examine the unique associations between two components of self-regulation, attention and executive functioning (EF), and growth in early literacy skills over the preschool year using latent-growth-curve analysis. The sample included 1,082 preschool children (M-age = 55.0 months, SD = 3.73). Children completed measures of vocabulary, syntax, phonological awareness, print knowledge, cognitive ability, and self-regulation, and children’s classroom teachers completed a behavior rating measure. To examine the independent relations of the self-regulatory skills and cognitive ability with children’s initial early literacy skills and growth across the preschool year, growth models in which the intercept and slope were simultaneously regressed on each of the predictor variables were examined. Because of the significant relation between intercept and slope for most outcomes, slope was regressed on intercept in the models to allow a determination of direct and indirect effects of the predictors on growth in children’s language and literacy skills across the preschool year. In general, both teacher-rated inattention and directly measured EF were uniquely associated with initial skills level; however, only teacher-rated inattention uniquely predicted growth in early literacy skills. These findings suggest that teacher-ratings of inattention may measure an aspect of self-regulation that is particularly associated with the acquisition of academic skills in early childhood. PMID:27854463

Active cell-matrix coupling regulates cellular force landscapes of cohesive epithelial monolayers

NASA Astrophysics Data System (ADS)

Zhao, Tiankai; Zhang, Yao; Wei, Qiong; Shi, Xuechen; Zhao, Peng; Chen, Long-Qing; Zhang, Sulin

2018-03-01

Epithelial cells can assemble into cohesive monolayers with rich morphologies on substrates due to competition between elastic, edge, and interfacial effects. Here we present a molecularly based thermodynamic model, integrating monolayer and substrate elasticity, and force-mediated focal adhesion formation, to elucidate the active biochemical regulation over the cellular force landscapes in cohesive epithelial monolayers, corroborated by microscopy and immunofluorescence studies. The predicted extracellular traction and intercellular tension are both monolayer size and substrate stiffness dependent, suggestive of cross-talks between intercellular and extracellular activities. Our model sets a firm ground toward a versatile computational framework to uncover the molecular origins of morphogenesis and disease in multicellular epithelia.

Effect of the microtubule-associated protein tau on dynamics of single-headed motor proteins KIF1A

NASA Astrophysics Data System (ADS)

Sparacino, J.; Farías, M. G.; Lamberti, P. W.

2014-02-01

Intracellular transport based on molecular motors and its regulation are crucial to the functioning of cells. Filamentary tracks of the cells are abundantly decorated with nonmotile microtubule-associated proteins, such as tau. Motivated by experiments on kinesin-tau interactions [Dixit et al., Science 319, 1086 (2008), 10.1126/science.1152993] we developed a stochastic model of interacting single-headed motor proteins KIF1A that also takes into account the interactions between motor proteins and tau molecules. Our model reproduces experimental observations and predicts significant effects of tau on bound time and run length which suggest an important role of tau in regulation of kinesin-based transport.

Model Predictive Control of Type 1 Diabetes: An in Silico Trial

PubMed Central

Magni, Lalo; Raimondo, Davide M.; Bossi, Luca; Man, Chiara Dalla; De Nicolao, Giuseppe; Kovatchev, Boris; Cobelli, Claudio

2007-01-01

Background The development of artificial pancreas has received a new impulse from recent technological advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin pump delivery systems. However, the availability of innovative sensors and actuators, although essential, does not guarantee optimal glycemic regulation. Closed-loop control of blood glucose levels still poses technological challenges to the automatic control expert, most notable of which are the inevitable time delays between glucose sensing and insulin actuation. Methods A new in silico model is exploited for both design and validation of a linear model predictive control (MPC) glucose control system. The starting point is a recently developed meal glucose–insulin model in health, which is modified to describe the metabolic dynamics of a person with type 1 diabetes mellitus. The population distribution of the model parameters originally obtained in healthy 204 patients is modified to describe diabetic patients. Individual models of virtual patients are extracted from this distribution. A discrete-time MPC is designed for all the virtual patients from a unique input–output-linearized approximation of the full model based on the average population values of the parameters. The in silico trial simulates 4 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. Results Provided that the regulator undergoes some individual tuning, satisfactory results are obtained even if the control design relies solely on the average patient model. Only the weight on the glucose concentration error needs to be tuned in a quite straightforward and intuitive way. The ability of the MPC to take advantage of meal announcement information is demonstrated. Imperfect knowledge of the amount of ingested glucose causes only marginal deterioration of performance. In general, MPC results in better regulation than proportional integral derivative, limiting significantly the oscillation of glucose levels. Conclusions The proposed in silico trial shows the potential of MPC for artificial pancreas design. The main features are a capability to consider meal announcement information, delay compensation, and simplicity of tuning and implementation. PMID:19885152

A physiologically based toxicokinetic model for lake trout (Salvelinus namaycush).

PubMed

Lien, G J; McKim, J M; Hoffman, A D; Jenson, C T

2001-01-01

A physiologically based toxicokinetic (PB-TK) model for fish, incorporating chemical exchange at the gill and accumulation in five tissue compartments, was parameterized and evaluated for lake trout (Salvelinus namaycush). Individual-based model parameterization was used to examine the effect of natural variability in physiological, morphological, and physico-chemical parameters on model predictions. The PB-TK model was used to predict uptake of organic chemicals across the gill and accumulation in blood and tissues in lake trout. To evaluate the accuracy of the model, a total of 13 adult lake trout were exposed to waterborne 1,1,2,2-tetrachloroethane (TCE), pentachloroethane (PCE), and hexachloroethane (HCE), concurrently, for periods of 6, 12, 24 or 48 h. The measured and predicted concentrations of TCE, PCE and HCE in expired water, dorsal aortic blood and tissues were generally within a factor of two, and in most instances much closer. Variability noted in model predictions, based on the individual-based model parameterization used in this study, reproduced variability observed in measured concentrations. The inference is made that parameters influencing variability in measured blood and tissue concentrations of xenobiotics are included and accurately represented in the model. This model contributes to a better understanding of the fundamental processes that regulate the uptake and disposition of xenobiotic chemicals in the lake trout. This information is crucial to developing a better understanding of the dynamic relationships between contaminant exposure and hazard to the lake trout.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

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

Cannon, William; Zucker, Jeremy; Baxter, Douglas

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrationsmore » and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.« less

Clarification of the Relationship between Awareness of Doping of Competitive Sports Coaches and Their Instructions to Prevent Doping.

PubMed

Yamaguchi, Takumi; Horio, Ikuo; Goto, Masahiro; Miyauchi, Yoshirou; Izushi, Fumio

2016-01-01

It has been 6 years since the establishment of the position of "sports pharmacist" as one type of pharmacist. In the sporting world of Japan, sports pharmacists are expected to promote athletes' awareness of antidoping regulations and provide them with relevant education. However, currently, these pharmacists' main duty is to provide athletes and their coaches with guidance on medication. Using a model for the prediction of athletes' actions, we have worked to promote athletes' awareness of antidoping regulations and encourage sports pharmacists to perform relevant activities, such as antidoping education. As a result, we clarified that athletes' awareness regarding antidoping rules influences their actions when experiencing minor illnesses. In addition, we have proposed approaches to encourage athletes to undertake antidoping activities. The present study aimed to clarify competitive sports coaches' awareness of antidoping regulations, the instructions that those coaches give athletes when they experience minor illnesses, and coaches' awareness of athletes' usage of drugs and supplements. Analysis using a model for the prediction of actions revealed that to promote coaches' awareness of antidoping regulations, education aimed at raising their level of knowledge of doping is warranted. Furthermore, coaches were aware of the necessity of continuously providing athletes with antidoping instructions, but they did not keep sufficient track of athletes' usage of drugs and supplements. To encourage sports coaches to perform antidoping activities, it is effective to provide them with opportunities to develop their knowledge of doping prevention in their areas.

Crystal structure of Bacillus anthracis virulence regulator AtxA and effects of phosphorylated histidines on multimerization and activity

PubMed Central

Hammerstrom, Troy G.; Horton, Lori B.; Swick, Michelle C.; Joachimiak, Andrzej; Osipiuk, Jerzy; Koehler, Theresa M.

2015-01-01

Summary The Bacillus anthracis virulence regulator AtxA controls transcription of the anthrax toxin genes and capsule biosynthesis operon. AtxA activity is elevated during growth in media containing glucose and CO2/bicarbonate, and there is a positive correlation between the CO2/bicarbonate signal, AtxA activity, and homomultimerization. AtxA activity is also affected by phosphorylation at specific histidines. We show that AtxA crystallizes as a dimer. Distinct folds associated with predicted DNA-binding domains (HTH1 and HTH2) and phosphoenolpyruvate: carbohydrate phosphotransferase system-regulated domains (PRD1 and PRD2) are apparent. We tested AtxA variants containing single and double phosphomimetic (His → Asp) and phosphoablative (His → Ala) amino acid changes for activity in B. anthracis cultures and for protein-protein interactions in cell lysates. Reduced activity of AtxA H199A, lack of multimerization and activity of AtxAH379D variants, and predicted structural changes associated with phosphorylation support a model for control of AtxA function. We propose that (1) in the AtxA dimer, phosphorylation of H199 in PRD1 affects HTH2 positioning, influencing DNA-binding; and (2) phosphorylation of H379 in PRD2 disrupts dimer formation. The AtxA structure is the first reported high-resolution full-length structure of a PRD-containing regulator and can serve as a model for proteins of this family, especially those that link virulence to bacterial metabolism. PMID:25402841

Crystal structure of Bacillus anthracis virulence regulator AtxA and effects of phosphorylated histidines on multimerization and activity.

PubMed

Hammerstrom, Troy G; Horton, Lori B; Swick, Michelle C; Joachimiak, Andrzej; Osipiuk, Jerzy; Koehler, Theresa M

2015-02-01

The Bacillus anthracis virulence regulator AtxA controls transcription of the anthrax toxin genes and capsule biosynthetic operon. AtxA activity is elevated during growth in media containing glucose and CO(2)/bicarbonate, and there is a positive correlation between the CO(2)/bicarbonate signal, AtxA activity and homomultimerization. AtxA activity is also affected by phosphorylation at specific histidines. We show that AtxA crystallizes as a dimer. Distinct folds associated with predicted DNA-binding domains (HTH1 and HTH2) and phosphoenolpyruvate: carbohydrate phosphotransferase system-regulated domains (PRD1 and PRD2) are apparent. We tested AtxA variants containing single and double phosphomimetic (His→Asp) and phosphoablative (His→Ala) amino acid changes for activity in B. anthracis cultures and for protein-protein interactions in cell lysates. Reduced activity of AtxA H199A, lack of multimerization and activity of AtxAH379D variants, and predicted structural changes associated with phosphorylation support a model for control of AtxA function. We propose that (i) in the AtxA dimer, phosphorylation of H199 in PRD1 affects HTH2 positioning, influencing DNA-binding; and (ii) phosphorylation of H379 in PRD2 disrupts dimer formation. The AtxA structure is the first reported high-resolution full-length structure of a PRD-containing regulator, and can serve as a model for proteins of this family, especially those that link virulence to bacterial metabolism. © 2014 John Wiley & Sons Ltd.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

PubMed

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

A systems approach to hemostasis: 3. Thrombus consolidation regulates intrathrombus solute transport and local thrombin activity

PubMed Central

Welsh, John D.; Tomaiuolo, Maurizio; Wu, Jie; Colace, Thomas V.; Diamond, Scott L.

2014-01-01

Hemostatic thrombi formed after a penetrating injury have a distinctive structure in which a core of highly activated, closely packed platelets is covered by a shell of less-activated, loosely packed platelets. We have shown that differences in intrathrombus molecular transport emerge in parallel with regional differences in platelet packing density and predicted that these differences affect thrombus growth and stability. Here we test that prediction in a mouse vascular injury model. The studies use a novel method for measuring thrombus contraction in vivo and a previously characterized mouse line with a defect in integrin αIIbβ3 outside-in signaling that affects clot retraction ex vivo. The results show that the mutant mice have a defect in thrombus consolidation following vascular injury, resulting in an increase in intrathrombus transport rates and, as predicted by computational modeling, a decrease in thrombin activity and platelet activation in the thrombus core. Collectively, these data (1) demonstrate that in addition to the activation state of individual platelets, the physical properties of the accumulated mass of adherent platelets is critical in determining intrathrombus agonist distribution and platelet activation and (2) define a novel role for integrin signaling in the regulation of intrathrombus transport rates and localization of thrombin activity. PMID:24951426

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

NASA Astrophysics Data System (ADS)

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

A Novel Computational Model Predicts Key Regulators of Chemokine Gradient Formation in Lymph Nodes and Site-Specific Roles for CCL19 and ACKR4

PubMed Central

Brook, Bindi S.

2017-01-01

The chemokine receptor CCR7 drives leukocyte migration into and within lymph nodes (LNs). It is activated by chemokines CCL19 and CCL21, which are scavenged by the atypical chemokine receptor ACKR4. CCR7-dependent navigation is determined by the distribution of extracellular CCL19 and CCL21, which form concentration gradients at specific microanatomical locations. The mechanisms underpinning the establishment and regulation of these gradients are poorly understood. In this article, we have incorporated multiple biochemical processes describing the CCL19–CCL21–CCR7–ACKR4 network into our model of LN fluid flow to establish a computational model to investigate intranodal chemokine gradients. Importantly, the model recapitulates CCL21 gradients observed experimentally in B cell follicles and interfollicular regions, building confidence in its ability to accurately predict intranodal chemokine distribution. Parameter variation analysis indicates that the directionality of these gradients is robust, but their magnitude is sensitive to these key parameters: chemokine production, diffusivity, matrix binding site availability, and CCR7 abundance. The model indicates that lymph flow shapes intranodal CCL21 gradients, and that CCL19 is functionally important at the boundary between B cell follicles and the T cell area. It also predicts that ACKR4 in LNs prevents CCL19/CCL21 accumulation in efferent lymph, but does not control intranodal gradients. Instead, it attributes the disrupted interfollicular CCL21 gradients observed in Ackr4-deficient LNs to ACKR4 loss upstream. Our novel approach has therefore generated new testable hypotheses and alternative interpretations of experimental data. Moreover, it acts as a framework to investigate gradients at other locations, including those that cannot be visualized experimentally or involve other chemokines. PMID:28807994

Autophagy in Dictyostelium: Mechanisms, regulation and disease in a simple biomedical model.

PubMed

Mesquita, Ana; Cardenal-Muñoz, Elena; Dominguez, Eunice; Muñoz-Braceras, Sandra; Nuñez-Corcuera, Beatriz; Phillips, Ben A; Tábara, Luis C; Xiong, Qiuhong; Coria, Roberto; Eichinger, Ludwig; Golstein, Pierre; King, Jason S; Soldati, Thierry; Vincent, Olivier; Escalante, Ricardo

2017-01-02

Autophagy is a fast-moving field with an enormous impact on human health and disease. Understanding the complexity of the mechanism and regulation of this process often benefits from the use of simple experimental models such as the social amoeba Dictyostelium discoideum. Since the publication of the first review describing the potential of D. discoideum in autophagy, significant advances have been made that demonstrate both the experimental advantages and interest in using this model. Since our previous review, research in D. discoideum has shed light on the mechanisms that regulate autophagosome formation and contributed significantly to the study of autophagy-related pathologies. Here, we review these advances, as well as the current techniques to monitor autophagy in D. discoideum. The comprehensive bioinformatics search of autophagic proteins that was a substantial part of the previous review has not been revisited here except for those aspects that challenged previous predictions such as the composition of the Atg1 complex. In recent years our understanding of, and ability to investigate, autophagy in D. discoideum has evolved significantly and will surely enable and accelerate future research using this model.

Physics of lumen growth.

PubMed

Dasgupta, Sabyasachi; Gupta, Kapish; Zhang, Yue; Viasnoff, Virgile; Prost, Jacques

2018-05-22

We model the dynamics of formation of intercellular secretory lumens. Using conservation laws, we quantitatively study the balance between paracellular leaks and the build-up of osmotic pressure in the lumen. Our model predicts a critical pumping threshold to expand stable lumens. Consistently with experimental observations in bile canaliculi, the model also describes a transition between a monotonous and oscillatory regime during luminogenesis as a function of ion and water transport parameters. We finally discuss the possible importance of regulation of paracellular leaks in intercellular tubulogenesis.

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

PubMed

Jain, Dharm Skandh; Gupte, Sanket Rajan; Aduri, Raviprasad

2018-06-22

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

Analysis of a minimal Rho-GTPase circuit regulating cell shape

NASA Astrophysics Data System (ADS)

Holmes, William R.; Edelstein-Keshet, Leah

2016-08-01

Networks of Rho-family GTPases regulate eukaryotic cell polarization and motility by controlling assembly and contraction of the cytoskeleton. The mutually inhibitory Rac-Rho circuit is emerging as a central, regulatory hub that can affect the shape and motility phenotype of eukaryotic cells. Recent experimental manipulation of the amounts of Rac and Rho or their regulators (guanine nucleotide-exchange factors, GTPase-activating proteins, guanine nucleotide dissociation inhibitors) have been shown to bias the prevalence of these different states and promote transitions between them. Here we show that part of this data can be understood in terms of inherent Rac-Rho mutually inhibitory dynamics. We analyze a spatio-temporal mathematical model of Rac-Rho dynamics to produce a detailed set of predictions of how parameters such as GTPase rates of activation and total amounts affect cell decisions (such as Rho-dominated contraction, Rac-dominated spreading, and spatially segregated Rac-Rho polarization). We find that in some parameter regimes, a cell can take on any of these three fates depending on its environment or stimuli. We also predict how experimental manipulations (corresponding to parameter variations) can affect cell shapes observed. Our methods are based on local perturbation analysis (a kind of nonlinear stability analysis), and an approximation of nonlinear feedback by sharp switches. We compare the Rac-Rho model to an even simpler single-GTPase (‘wave-pinning’) model and demonstrate that the overall behavior is inherent to GTPase properties, rather than stemming solely from network topology.

Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations.

PubMed

Ruiz, Montse C; Haapanen, Saara; Tolvanen, Asko; Robazza, Claudio; Duda, Joan L

2017-08-01

This study examined the relationships between perceptions of the motivational climate, motivation regulations, and the intensity and functionality levels of athletes' pleasant and unpleasant emotional states. Specifically, we examined the hypothesised mediational role of motivation regulations in the climate-emotion relationship. We also tested a sequence in which emotions were assumed to be predicted by the motivational climate dimensions and then served as antecedents to variability in motivation regulations. Participants (N = 494) completed a multi-section questionnaire assessing targeted variables. Structural equation modelling (SEM) revealed that a perceived task-involving climate was a positive predictor of autonomous motivation and of the impact of functional anger, and a negative predictor of the intensity of anxiety and dysfunctional anger. Autonomous motivation was a partial mediator of perceptions of a task-involving climate and the impact of functional anger. An ego-involving climate was a positive predictor of controlled motivation, and of the intensity and impact of functional anger and the intensity of dysfunctional anger. Controlled motivation partially mediated the relationship between an ego-involving climate and the intensity of dysfunctional anger. Good fit to the data also emerged for the motivational climate, emotional states, and motivation regulations sequence. Findings provide support for the consideration of hedonic tone and functionality distinctions in the assessment of athletes' emotional states.

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains.

PubMed

Pichon, Christophe; du Merle, Laurence; Caliot, Marie Elise; Trieu-Cuot, Patrick; Le Bouguénec, Chantal

2012-04-01

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

PubMed Central

Pichon, Christophe; du Merle, Laurence; Caliot, Marie Elise; Trieu-Cuot, Patrick; Le Bouguénec, Chantal

2012-01-01

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli. PMID:22139924

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling.

PubMed

Wen, Shi; Zhan, Bohan; Feng, Jianghua; Hu, Weize; Lin, Xianchao; Bai, Jianxi; Huang, Heguang

2017-11-02

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy. By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1 H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight. An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation. The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Thermal history regulates methylbutenol basal emission rate in Pinus ponderosa.

PubMed

Gray, Dennis W; Goldstein, Allen H; Lerdau, Manuel T

2006-07-01

Methylbutenol (MBO) is a 5-carbon alcohol that is emitted by many pines in western North America, which may have important impacts on the tropospheric chemistry of this region. In this study, we document seasonal changes in basal MBO emission rates and test several models predicting these changes based on thermal history. These models represent extensions of the ISO G93 model that add a correction factor C(basal), allowing MBO basal emission rates to change as a function of thermal history. These models also allow the calculation of a new emission parameter E(standard30), which represents the inherent capacity of a plant to produce MBO, independent of current or past environmental conditions. Most single-component models exhibited large departures in early and late season, and predicted day-to-day changes in basal emission rate with temporal offsets of up to 3 d relative to measured basal emission rates. Adding a second variable describing thermal history at a longer time scale improved early and late season model performance while retaining the day-to-day performance of the parent single-component model. Out of the models tested, the T(amb),T(max7) model exhibited the best combination of day-to-day and seasonal predictions of basal MBO emission rates.

Photoionization-regulated star formation and the structure of molecular clouds

NASA Technical Reports Server (NTRS)

Mckee, Christopher F.

1989-01-01

A model for the rate of low-mass star formation in Galactic molecular clouds and for the influence of this star formation on the structure and evolution of the clouds is presented. The rate of energy injection by newly formed stars is estimated, and the effect of this energy injection on the size of the cloud is determined. It is shown that the observed rate of star formation appears adequate to support the observed clouds against gravitational collapse. The rate of photoionization-regulated star formation is estimated and it is shown to be in agreement with estimates of the observed rate of star formation if the observed molecular cloud parameters are used. The mean cloud extinction and the Galactic star formation rate per unit mass of molecular gas are predicted theoretically from the condition that photionization-regulated star formation be in equilibrium. A simple model for the evolution of isolated molecular clouds is developed.

A tale of two tasks: reversing the self-regulatory resource depletion effect.

PubMed

Converse, Patrick D; Deshon, Richard P

2009-09-01

This research examined the self-regulatory depletion model (e.g., M. Muraven & R. F. Baumeister, 2000). Although numerous studies support this model's prediction of decrements in self-regulation across tasks, the majority of this research has relied on a single paradigm in which two tasks are performed in succession. Other work related to learned industriousness (R. Eisenberger, 1992) and adaptation-level theory (H. Helson, 1964) indicates that self-regulatory behavior may remain stable or even improve as a result of prior self-regulatory activities in situations involving additional tasks. Three studies examined these differing perspectives with 2- and 3-task designs. Results indicated that, relative to low initial self-regulatory exertion, high exertion can lead to poorer or better subsequent self-regulation. These findings are consistent with an adaptation view of self-regulation, suggesting that the depletion effect may be only part of the picture of self-regulatory behavior over time.

Transepithelial glucose transport and Na+/K+ homeostasis in enterocytes: an integrative model

PubMed Central

Drengstig, Tormod; Ruoff, Peter

2014-01-01

The uptake of glucose and the nutrient coupled transcellular sodium traffic across epithelial cells in the small intestine has been an ongoing topic in physiological research for over half a century. Driving the uptake of nutrients like glucose, enterocytes must have regulatory mechanisms that respond to the considerable changes in the inflow of sodium during absorption. The Na-K-ATPase membrane protein plays a major role in this regulation. We propose the hypothesis that the amount of active Na-K-ATPase in enterocytes is directly regulated by the concentration of intracellular Na+ and that this regulation together with a regulation of basolateral K permeability by intracellular ATP gives the enterocyte the ability to maintain ionic Na+/K+ homeostasis. To explore these regulatory mechanisms, we present a mathematical model of the sodium coupled uptake of glucose in epithelial enterocytes. Our model integrates knowledge about individual transporter proteins including apical SGLT1, basolateral Na-K-ATPase, and GLUT2, together with diffusion and membrane potentials. The intracellular concentrations of glucose, sodium, potassium, and chloride are modeled by nonlinear differential equations, and molecular flows are calculated based on experimental kinetic data from the literature, including substrate saturation, product inhibition, and modulation by membrane potential. Simulation results of the model without the addition of regulatory mechanisms fit well with published short-term observations, including cell depolarization and increased concentration of intracellular glucose and sodium during increased concentration of luminal glucose/sodium. Adding regulatory mechanisms for regulation of Na-K-ATPase and K permeability to the model show that our hypothesis predicts observed long-term ionic homeostasis. PMID:24898586

Mathematical modeling of the dynamic storage of iron in ferritin

PubMed Central

2010-01-01

Background Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content. Results Simulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded. Conclusions The results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption. PMID:21047430

Mathematical modeling of the dynamic storage of iron in ferritin.

PubMed

Salgado, J Cristian; Olivera-Nappa, Alvaro; Gerdtzen, Ziomara P; Tapia, Victoria; Theil, Elizabeth C; Conca, Carlos; Nuñez, Marco T

2010-11-03

Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content. Simulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded. The results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.

Predictive models of safety based on audit findings: Part 1: Model development and reliability.

PubMed

Hsiao, Yu-Lin; Drury, Colin; Wu, Changxu; Paquet, Victor

2013-03-01

This consecutive study was aimed at the quantitative validation of safety audit tools as predictors of safety performance, as we were unable to find prior studies that tested audit validity against safety outcomes. An aviation maintenance domain was chosen for this work as both audits and safety outcomes are currently prescribed and regulated. In Part 1, we developed a Human Factors/Ergonomics classification framework based on HFACS model (Shappell and Wiegmann, 2001a,b), for the human errors detected by audits, because merely counting audit findings did not predict future safety. The framework was tested for measurement reliability using four participants, two of whom classified errors on 1238 audit reports. Kappa values leveled out after about 200 audits at between 0.5 and 0.8 for different tiers of errors categories. This showed sufficient reliability to proceed with prediction validity testing in Part 2. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

PubMed

Majoros, William H; Ohler, Uwe

2010-12-16

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.

Embarked electrical network robust control based on singular perturbation model.

PubMed

Abdeljalil Belhaj, Lamya; Ait-Ahmed, Mourad; Benkhoris, Mohamed Fouad

2014-07-01

This paper deals with an approach of modelling in view of control for embarked networks which can be described as strongly coupled multi-sources, multi-loads systems with nonlinear and badly known characteristics. This model has to be representative of the system behaviour and easy to handle for easy regulators synthesis. As a first step, each alternator is modelled and linearized around an operating point and then it is subdivided into two lower order systems according to the singular perturbation theory. RST regulators are designed for each subsystem and tested by means of a software test-bench which allows predicting network behaviour in both steady and transient states. Finally, the designed controllers are implanted on an experimental benchmark constituted by two alternators supplying loads in order to test the dynamic performances in realistic conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Fifty-sixth Christmas Bird Count. 147. Southern Dorchester County, Md

USGS Publications Warehouse

Johnson, F.A.; Williams, B.K.; Nichols, J.D.; Hines, J.E.; Kendall, W.L.; Smith, G.W.; Caithamer, David F.

1956-01-01

Summary and Recommendations: We suggest that managers are approaching the limits of their ability to improve waterfowl harvest management, primarily because the information needed to make better decisions is being sacrificed by the current approach to setting regulations. We propose an actively adaptive management strategy in which regulatory decisions play a dominant role in reducing uncertainty about population dynamics. The proposed strategy recognizes 'value' in acquiring knowledge only to the extent that it contributes to the objective of optimizing harvests. To implement this strategy, managers will need: (1) a set of regulatory options, with possible constraints on their use; (2) quantifiable harvest management objectives; (3) a set of models that represent an array of meaningful hypotheses about the effects of regulations on populations; and (4) a measure of credibility (or likelihood) for each model, which can be updated regularly using information from waterfowl monitoring programs. Adaptive optimization is an iterative process in which the harvest-management policy converges over time to one that maximizes harvest under the most appropriate model. At each time step, an optimal regulatory decision is identified based on the state of the system and the model likelihoods. In the next time step, predicted population changes from the alternative models are compared with the actual changes provided by the monitoring program, The likelihoods are increased or decreased to the extent that predicted and actual population changes correspond. These updated likelihoods then are used in setting regulations in the next cycle and the process begins again. This iterative process produces the most informative regulations when uncertainty is prevalent and produces maximum sustainable yields as uncertainty is eliminated. We see no major obstacles to implementing this adaptive strategy, although there are a number of practical considerations. First and foremost, managers should assess the 'value' of learning. Only when there is a high degree of uncertainty about the effects of hunting regulations on population dynamics will the merit of our proposed strategy be evident. We suggest that this almost always will be true given our current understanding of the relationship between annual regulations, survival and population growth in waterfowl. Nonetheless, careful consideration should be given to formulating the set of alternative models. There is no value in distinguishing between models which differ in their mathematical formulation or biological realism, but which suggest similar harvest strategies. We suspect that 'mechanistic' models (i.e., those that attempt to capture the essence of biological processes) will make better candidates for model sets than so-called 'phenomenological' models. Assuming that all model sets include a good approximation of reality, learning rates will be dependent on the quality of monitoring programs. Fortunately, a variety of high-quality monitoring plans for many duck and goose populations of North America, when used with our adaptive approach, should provide new knowledge about population dynamics and response to hunting, and, thus, lead to improved management.

Genomic signal processing: from matrix algebra to genetic networks.

PubMed

Alter, Orly

2007-01-01

DNA microarrays make it possible, for the first time, to record the complete genomic signals that guide the progression of cellular processes. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment, and drug development. This chapter reviews the first data-driven models that were created from these genome-scale data, through adaptations and generalizations of mathematical frameworks from matrix algebra that have proven successful in describing the physical world, in such diverse areas as mechanics and perception: the singular value decomposition model, the generalized singular value decomposition model comparative model, and the pseudoinverse projection integrative model. These models provide mathematical descriptions of the genetic networks that generate and sense the measured data, where the mathematical variables and operations represent biological reality. The variables, patterns uncovered in the data, correlate with activities of cellular elements such as regulators or transcription factors that drive the measured signals and cellular states where these elements are active. The operations, such as data reconstruction, rotation, and classification in subspaces of selected patterns, simulate experimental observation of only the cellular programs that these patterns represent. These models are illustrated in the analyses of RNA expression data from yeast and human during their cell cycle programs and DNA-binding data from yeast cell cycle transcription factors and replication initiation proteins. Two alternative pictures of RNA expression oscillations during the cell cycle that emerge from these analyses, which parallel well-known designs of physical oscillators, convey the capacity of the models to elucidate the design principles of cellular systems, as well as guide the design of synthetic ones. In these analyses, the power of the models to predict previously unknown biological principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with cell cycle-regulated RNA transcription in yeast. These models may become the foundation of a future in which biological systems are modeled as physical systems are today.

Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica serovar Typhimurium

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

Yoon, Hyunjin; McDermott, Jason E.; Porwollik, Steffen

Salmonella must respond to a myriad of environmental cues during infection of a mouse and express specific subsets of genes in a temporal and spatial manner to subvert the host defense mechanisms but these regulatory pathways are poorly established. To unravel how micro-environmental signals are processed and integrated into coordinated action, we constructed in-frame non-polar deletions of 84 regulators inferred to play a role in Salmonella typhimurium virulence and tested them in three virulence assays (intraperitoneal (i.p.), and intragastric (i.g.) infection in BALB/c mice, and persistence in SvJ129 mice). Overall 36 regulators were identified that were less virulent in atmore » least one assay, and of those, 15 regulators were required for systemic mouse infection in an acute infection model. As a first step towards understanding the interplay between a pathogen and its host from a systems biology standpoint we focused on these 15 genes. Transcriptional profiles were obtained for each of these 15 regulators from strains grown under four different environmental conditions. These results as well as publicly available transcriptional profiles were analyzed using both network inference and cluster analysis algorithms. The analysis predicts a regulatory network in which all 15 regulators control a specific set of genes necessary for Salmonella to cause systemic infection. We tested the regulatory model by expressing a subset of the regulators in trans and monitoring transcription of 7 known virulence factors located within Salmonella pathogenicity island 2 (SPI-2). These experiments validated the regulatory model and showed that, for these 7 genes, the response regulator SsrB and the marR type regulator SlyA co-regulate in a regulatory cascade by integrating multiple signals.« less

Genomic and proteomic studies on the effects of the insect growth regulator diflubenzuron in the model beetle species Tribolium castaneum.

PubMed

Merzendorfer, Hans; Kim, Hee Shin; Chaudhari, Sujata S; Kumari, Meera; Specht, Charles A; Butcher, Stephen; Brown, Susan J; Manak, J Robert; Beeman, Richard W; Kramer, Karl J; Muthukrishnan, Subbaratnam

2012-04-01

Several benzoylphenyl urea-derived insecticides such as diflubenzuron (DFB, Dimilin) are in wide use to control various insect pests. Although this class of compounds is known to disrupt molting and to affect chitin content, their precise mode of action is still not understood. To gain a broader insight into the mechanism underlying the insecticidal effects of benzoylphenyl urea compounds, we conducted a comprehensive study with the model beetle species and stored product pest Tribolium castaneum (red flour beetle) utilizing genomic and proteomic approaches. DFB was added to a wheat flour-based diet at various concentrations and fed to larvae and adults. We observed abortive molting, hatching defects and reduced chitin amounts in the larval cuticle, the peritrophic matrix and eggs. Electron microscopic examination of the larval cuticle revealed major structural changes and a loss of lamellate structure of the procuticle. We used a genomic tiling array for determining relative expression levels of about 11,000 genes predicted by the GLEAN algorithm. About 6% of all predicted genes were more than 2-fold up- or down-regulated in response to DFB treatment. Genes encoding enzymes involved in chitin metabolism were unexpectedly unaffected, but many genes encoding cuticle proteins were affected. In addition, several genes presumably involved in detoxification pathways were up-regulated. Comparative 2D gel electrophoresis of proteins extracted from the midgut revealed 388 protein spots, of which 7% were significantly affected in their levels by DFB treatment as determined by laser densitometry. Mass spectrometric identification revealed that UDP-N-acetylglucosamine pyrophosphorylase and glutathione synthetase were up-regulated. In summary, the red flour beetle turned out to be a good model organism for investigating the global effects of bioactive materials such as insect growth regulators and other insecticides. The results of this study recapitulate all of the different DFB-induced symptoms in a single model insect, which have been previously found in several different insect species, and further illustrate that DFB treatment causes a wide range of effects at the molecular level. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

DOE PAGES

Erbilgin, Onur; Bowen, Benjamin P.; Kosina, Suzanne M.; ...

2017-01-23

Mixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same t ask. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters,more » creating an opportunity to integrate the data into a predictive model of resource use by a mixed community. Here we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster. Our results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.« less

Emotion regulation predicts marital satisfaction: more than a wives' tale.

PubMed

Bloch, Lian; Haase, Claudia M; Levenson, Robert W

2014-02-01

Emotion regulation is generally thought to be a critical ingredient for successful interpersonal relationships. Ironically, few studies have investigated the link between how well spouses regulate emotion and how satisfied they are with their marriages. We utilized data from a 13-year, 3-wave longitudinal study of middle-aged (40-50 years old) and older (60-70 years old) long-term married couples, focusing on the associations between downregulation of negative emotion (measured during discussions of an area of marital conflict at Wave 1) and marital satisfaction (measured at all 3 waves). Downregulation of negative emotion was assessed by determining how quickly spouses reduced signs of negative emotion (in emotional experience, emotional behavior, and physiological arousal) after negative emotion events. Data were analyzed using actor-partner interdependence modeling. Findings showed that (a) greater downregulation of wives' negative experience and behavior predicted greater marital satisfaction for wives and husbands concurrently and (b) greater downregulation of wives' negative behavior predicted increases in wives' marital satisfaction longitudinally. Wives' use of constructive communication (measured between Waves 1 and 2) mediated the longitudinal associations. These results show the benefits of wives' downregulation of negative emotion during conflict for marital satisfaction and point to wives' constructive communication as a mediating pathway. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Emotion regulation predicts marital satisfaction: More than a wives’ tale

PubMed Central

Bloch, Lian; Haase, Claudia M.; Levenson, Robert W.

2014-01-01

Emotion regulation is generally thought to be a critical ingredient for successful interpersonal relationships. Ironically, few studies have investigated the link between how well spouses regulate emotion and how satisfied they are with their marriages. We utilized data from a 13-year, 3-wave longitudinal study of middle-aged (40–50 years old) and older (60–70 years old) long-term married couples, focusing on the associations between downregulation of negative emotion (measured during discussions of an area of marital conflict at Wave 1) and marital satisfaction (measured at all three waves). Downregulation of negative emotion was assessed by determining how quickly spouses reduced signs of negative emotion (in emotional experience, emotional behavior, and physiological arousal) after negative emotion events. Data were analyzed using actor-partner interdependence modeling. Findings showed that (a) greater downregulation of wives’ negative experience and behavior predicted greater marital satisfaction for wives and husbands concurrently and (b) greater downregulation of wives’ negative behavior predicted increases in wives’ marital satisfaction longitudinally. Wives’ use of constructive communication (measured between Waves 1 and 2) mediated the longitudinal associations. These results show the benefits of wives’ downregulation of negative emotion during conflict for marital satisfaction and point to wives’ constructive communication as a mediating pathway. PMID:24188061

What plant hydraulics can tell us about responses to climate-change droughts.

PubMed

Sperry, John S; Love, David M

2015-07-01

Climate change exposes vegetation to unusual drought, causing declines in productivity and increased mortality. Drought responses are hard to anticipate because canopy transpiration and diffusive conductance (G) respond to drying soil and vapor pressure deficit (D) in complex ways. A growing database of hydraulic traits, combined with a parsimonious theory of tree water transport and its regulation, may improve predictions of at-risk vegetation. The theory uses the physics of flow through soil and xylem to quantify how canopy water supply declines with drought and ceases by hydraulic failure. This transpiration 'supply function' is used to predict a water 'loss function' by assuming that stomatal regulation exploits transport capacity while avoiding failure. Supply-loss theory incorporates root distribution, hydraulic redistribution, cavitation vulnerability, and cavitation reversal. The theory efficiently defines stomatal responses to D, drying soil, and hydraulic vulnerability. Driving the theory with climate predicts drought-induced loss of plant hydraulic conductance (k), canopy G, carbon assimilation, and productivity. Data lead to the 'chronic stress hypothesis' wherein > 60% loss of k increases mortality by multiple mechanisms. Supply-loss theory predicts the climatic conditions that push vegetation over this risk threshold. The theory's simplicity and predictive power encourage testing and application in large-scale modeling. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

A mathematical model of tumour and blood pHe regulation: The HCO3-/CO2 buffering system.

PubMed

Martin, Natasha K; Gaffney, Eamonn A; Gatenby, Robert A; Gillies, Robert J; Robey, Ian F; Maini, Philip K

2011-03-01

Malignant tumours are characterised by a low, acidic extracellular pH (pHe) which facilitates invasion and metastasis. Previous research has proposed the potential benefits of manipulating systemic pHe, and recent experiments have highlighted the potential for buffer therapy to raise tumour pHe, prevent metastases, and prolong survival in laboratory mice. To examine the physiological regulation of tumour buffering and investigate how perturbations of the buffering system (via metabolic/respiratory disorders or changes in parameters) can alter tumour and blood pHe, we develop a simple compartmentalised ordinary differential equation model of pHe regulation by the HCO3-/CO2 buffering system. An approximate analytical solution is constructed and used to carry out a sensitivity analysis, where we identify key parameters that regulate tumour pHe in both humans and mice. From this analysis, we suggest promising alternative and combination therapies, and identify specific patient groups which may show an enhanced response to buffer therapy. In addition, numerical simulations are performed, validating the model against well-known metabolic/respiratory disorders and predicting how these disorders could change tumour pHe. Copyright © 2010 Elsevier Inc. All rights reserved.

Using beta binomials to estimate classification uncertainty for ensemble models.

PubMed

Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

2014-01-01

Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

An investigation of the performance of an electronic in-line pump system for diesel engines

NASA Astrophysics Data System (ADS)

Fan, Li-Yun; Zhu, Yuan-Xian; Long, Wu-Qiang; Ma, Xiu-Zhen; Xue, Ying-Ying

2008-12-01

WIT Electronic Fuel System Co., Ltd. has developed a new fuel injector, the Electronic In-line Pump (EIP) system, designed to meet China’s diesel engine emission and fuel economy regulations. It can be used on marine diesel engines and commercial vehicle engines through different EIP systems. A numerical model of the EIP system was built in the AMESim environment for the purpose of creating a design tool for engine application and system optimization. The model was used to predict key injection characteristics under different operating conditions, such as injection pressure, injection rate, and injection duration. To validate these predictions, experimental tests were conducted under the conditions that were modeled. The results were quite encouraging and in agreement with model predictions. Additional experiments were conducted to study the injection characteristics of the EIP system. These results show that injection pressure and injection quantity are insensitive to injection timing variations, this is due to the design of the constant velocity cam profile. Finally, injection quantity and pressure vs. pulse width at different cam speeds are presented, an important injection characteristic for EIP system calibration.

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

A mathematical model for regulating monomer composition of the microbially synthesized polyhydroxyalkanoate copolymers.

PubMed

Xu, Jun; Guo, Baohua; Zhang, Zengmin; Wu, Qiong; Zhou, Quan; Chen, Jinchun; Chen, Guoqiang; Li, Guodong

2005-06-30

A mathematical model is proposed for predicting the copolymer composition of the microbially synthesized polyhydroxyalkanoate (PHA) copolymers. Based on the biochemical reactions involved in the precursor formation and polymerization pathways, the model correlates the copolymer composition with the cultivation conditions, the enzyme levels and selectivity, and the metabolic pathways. It suggests the following points: (1) in the case of a sole carbon source, the copolymer composition depends mainly on the topology of the metabolic pathways and the selectivity of both the enzymes involved in the precursor formation and the polymerization route; (2) the copolymer composition can be varied in a wide range via alteration of the flux ratio of different types of monomers channeled from two or more independent and simultaneous pathways; (3) the enzymes which should be over-expressed or inhibited to obtain the desired copolymer composition can be predicted. For example, inhibition of the beta-oxidation pathway will increase the content of the monomer units with longer chain length. To test the model, various experiments were envisaged by varying cultivation time, concentration and chain length of the sole carbon source, and molar ratio of the cosubstrates. The predictions from the model agree well with the experimental results. Therefore, the proposed model will be useful in predicting the PHA copolymer composition under different biochemical reaction conditions. In other words, it can provide a guide for the synthesis of desired PHA copolymers.

MODELING PROCESSES CONTROLLING MERCURY FATE IN WATERSHEDS RECEIVING ATMOSPHERIC DEPOSITION - COMPARISON OF FIELD SCALE GLEAMS AND WATERSHED SCALE WCS-GBMM

EPA Science Inventory

Long-term simulations of mercury fate in watersheds are needed to support regulations such as TMDLs and to predict the effectiveness of regulatory proposals, such as the Clean Air Mercury Rule (CAMR). Scientific uncertainties in mercury fate process descriptions combined with in...

Use of QSAR validation principles to enhance predictive approaches in the US EPA ECOSAR model

EPA Science Inventory

The US EPA Office of Pollution Prevention and Toxics (OPPT) is responsible for implementing the Toxic Substances Control Act (TSCA). TSCA is the US law that regulates industrial chemicals in the US and OPPT evaluates both new chemicals entering commerce, as well as those chemica...

Predicting Sentencing for Low-Level Crimes: Comparing Models of Human Judgment

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

Laws and guidelines regulating legal decision making are often imposed without taking the cognitive processes of the legal decision maker into account. In the case of sentencing, this raises the question of whether the sentencing decisions of prosecutors and judges are consistent with legal policy. Especially in handling low-level crimes, legal…

Identification and Functional Prediction of Large Intergenic Noncoding RNAs (lincRNAs) in Rainbow Trout (Oncorhynchus mykiss)

USDA-ARS?s Scientific Manuscript database

Long noncoding RNAs (lncRNAs) have been recognized in recent years as key regulators of diverse cellular processes. Genome-wide large-scale projects have uncovered thousands of lncRNAs in many model organisms. Large intergenic noncoding RNAs (lincRNAs) are lncRNAs that are transcribed from intergeni...

Application of indoor noise prediction in the real world

NASA Astrophysics Data System (ADS)

Lewis, David N.

2002-11-01

Predicting indoor noise in industrial workrooms is an important part of the process of designing industrial plants. Predicted levels are used in the design process to determine compliance with occupational-noise regulations, and to estimate levels inside the walls in order to predict community noise radiated from the building. Once predicted levels are known, noise-control strategies can be developed. In this paper an overview of over 20 years of experience is given with the use of various prediction approaches to manage noise in Unilever plants. This work has applied empirical and ray-tracing approaches separately, and in combination, to design various packaging and production plants and other facilities. The advantages of prediction methods in general, and of the various approaches in particular, will be discussed. A case-study application of prediction methods to the optimization of noise-control measures in a food-packaging plant will be presented. Plans to acquire a simplified prediction model for use as a company noise-screening tool will be discussed.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

A Graph Based Approach to Nonlinear Model Predictive Control with Application to Combustion Control and Flow Control

DTIC Science & Technology

2015-08-21

plants (200 MW and above) produce the majority of the nation’s energy demands, and these are the most heavily regulated by the EPA . The automotive...existing engines are not achieving the best possible efficiency. As in the electric power industry, EPA regulation is a major factor in the US...automotive engine market. Cummins, for example, was the only company in the market to meet the 2010 EPA standards for NOx emissions with their release of a 6.7

iCLIP Predicts the Dual Splicing Effects of TIA-RNA Interactions

PubMed Central

Briese, Michael; Zarnack, Kathi; Luscombe, Nicholas M.; Rot, Gregor; Zupan, Blaž; Curk, Tomaž; Ule, Jernej

2010-01-01

The regulation of alternative splicing involves interactions between RNA-binding proteins and pre-mRNA positions close to the splice sites. T-cell intracellular antigen 1 (TIA1) and TIA1-like 1 (TIAL1) locally enhance exon inclusion by recruiting U1 snRNP to 5′ splice sites. However, effects of TIA proteins on splicing of distal exons have not yet been explored. We used UV-crosslinking and immunoprecipitation (iCLIP) to find that TIA1 and TIAL1 bind at the same positions on human RNAs. Binding downstream of 5′ splice sites was used to predict the effects of TIA proteins in enhancing inclusion of proximal exons and silencing inclusion of distal exons. The predictions were validated in an unbiased manner using splice-junction microarrays, RT-PCR, and minigene constructs, which showed that TIA proteins maintain splicing fidelity and regulate alternative splicing by binding exclusively downstream of 5′ splice sites. Surprisingly, TIA binding at 5′ splice sites silenced distal cassette and variable-length exons without binding in proximity to the regulated alternative 3′ splice sites. Using transcriptome-wide high-resolution mapping of TIA-RNA interactions we evaluated the distal splicing effects of TIA proteins. These data are consistent with a model where TIA proteins shorten the time available for definition of an alternative exon by enhancing recognition of the preceding 5′ splice site. Thus, our findings indicate that changes in splicing kinetics could mediate the distal regulation of alternative splicing. PMID:21048981