Seven lessons from manyfield inflation in random potentials

NASA Astrophysics Data System (ADS)

Dias, Mafalda; Frazer, Jonathan; Marsh, M. C. David

2018-01-01

We study inflation in models with many interacting fields subject to randomly generated scalar potentials. We use methods from non-equilibrium random matrix theory to construct the potentials and an adaption of the `transport method' to evolve the two-point correlators during inflation. This construction allows, for the first time, for an explicit study of models with up to 100 interacting fields supporting a period of `approximately saddle-point' inflation. We determine the statistical predictions for observables by generating over 30,000 models with 2–100 fields supporting at least 60 efolds of inflation. These studies lead us to seven lessons: i) Manyfield inflation is not single-field inflation, ii) The larger the number of fields, the simpler and sharper the predictions, iii) Planck compatibility is not rare, but future experiments may rule out this class of models, iv) The smoother the potentials, the sharper the predictions, v) Hyperparameters can transition from stiff to sloppy, vi) Despite tachyons, isocurvature can decay, vii) Eigenvalue repulsion drives the predictions. We conclude that many of the `generic predictions' of single-field inflation can be emergent features of complex inflation models.

Interaction potential between a helium atom and metal surfaces

NASA Technical Reports Server (NTRS)

Takada, Y.; Kohn, W.

1985-01-01

By employing an S-matrix theory for evanescent waves, the repulsive potential between a helium atom and corrugated metal surfaces has been calculated. P-wave interactions and intra-atomic correlation effects were found to be very important. The corrugation part of the interaction potential is much weaker than predicted by the effective-medium theory. Application to Cu, Ni, and Ag (110) surfaces gives good agreement with experiment without any adjustable parameters.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

Predicting rates of interspecific interaction from phylogenetic trees.

PubMed

Nuismer, Scott L; Harmon, Luke J

2015-01-01

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

PubMed

Zhou, D; Bui, K; Sostek, M; Al-Huniti, N

2016-05-01

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Semimicroscopic, Lane-consistent nucleon-nucleus optical model potential up to 200 MeV

NASA Astrophysics Data System (ADS)

Bauge, Eric; Delaroche, Jean-Paul; Girod, Michel

2000-10-01

Our semimicroscopic optical model potential (E. Bauge et al., Phys. Rev. C 58), 1118 (1998). is re-evaluated in order to obtain a Lane-consistent description of (p,p), (n,n) and (p,n IAS) elastic scattering and reaction observables. The re-assessed nuclear matter interaction (which includes sizable renormalizations of the isovector potentials) is folded with microscopic HFB nuclear densities, producing OMPs that are free of adjustable parameters for nuclei with A >= 40. With Lane-consistency of the interaction, and the predictive nature of our HFB calculations, this scheme can be used to calculate observables for nuclei far from the stability line with good predictivity.

Co-evolutionary Analysis of Domains in Interacting Proteins Reveals Insights into Domain–Domain Interactions Mediating Protein–Protein Interactions

PubMed Central

Jothi, Raja; Cherukuri, Praveen F.; Tasneem, Asba; Przytycka, Teresa M.

2006-01-01

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the noninteracting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. PMID:16949097

Predicting Drug-Target Interactions Based on Small Positive Samples.

PubMed

Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

2018-01-01

A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance evaluation of ODT shows that it can be potentially useful. It confirms that predicting potential or missing DTIs based on the known interactions is a promising direction to solve problems related to the use of uncertain and unreliable negative samples and those related to the great demand in computational resources. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Interacting stressors and the potential for adaptation in a changing world: responses of populations and individuals

PubMed Central

French, Susannah S.; Brodie, Edmund D.

2017-01-01

To accurately predict the impact of environmental change, it is necessary to assay effects of key interacting stressors on vulnerable organisms, and the potential resiliency of their populations. Yet, for the most part, these critical data are missing. We examined the effects of two common abiotic stressors predicted to interact with climate change, salinity and temperature, on the embryonic survival and development of a model freshwater vertebrate, the rough-skinned newt (Taricha granulosa) from different populations. We found that salinity and temperature significantly interacted to affect newt embryonic survival and development, with the negative effects of salinity most pronounced at temperature extremes. We also found significant variation among, and especially within, populations, with different females varying in the performance of their eggs at different salinity–temperature combinations, possibly providing the raw material for future natural selection. Our results highlight the complex nature of predicting responses to climate change in space and time, and provide critical data towards that aim. PMID:28680662

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra

2012-01-01

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed. PMID:22539940

Biomedical Informatics Approaches to Identifying Drug-Drug Interactions: Application to Insulin Secretagogues

PubMed Central

Han, Xu; Chiang, ChienWei; Leonard, Charles E.; Bilker, Warren B.; Brensinger, Colleen M.; Li, Lang; Hennessy, Sean

2017-01-01

Background Drug-drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues―glipizide, glyburide, glimepiride, repaglinide, and nateglinide―to cause serious hypoglycemia. Methods We screened 400 drugs frequently co-prescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug–drug interaction potential based on the pharmacokinetics of each secretagogue–precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug–drug interaction. Results We predicted 34 pharmacokinetic drug–drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue–precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening. Conclusions The self-controlled case series design has the potential to be widely applicable to screening for drug–drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug–drug interactions in this case. PMID:28169935

Effects of anticipated emotional category and temporal predictability on the startle reflex.

PubMed

Parisi, Elizabeth A; Hajcak, Greg; Aneziris, Eleni; Nelson, Brady D

2017-09-01

Anticipated emotional category and temporal predictability are key characteristics that have both been shown to impact psychophysiological indices of defensive motivation (e.g., the startle reflex). To date, research has primarily examined these features in isolation, and it is unclear whether they have additive or interactive effects on defensive motivation. In the present study, the startle reflex was measured in anticipation of low arousal neutral, moderate arousal pleasant, and high arousal unpleasant pictures that were presented with either predictable or unpredictable timing. Linear mixed-effects modeling was conducted to examine startle magnitude across time, and the intercept at the beginning and end of the task. Across the entire task, the anticipation of temporally unpredictable (relative to predictable) pictures and emotional (relative to neutral) pictures potentiated startle magnitude, but there was no interaction between the two features. However, examination of the intercept at the beginning of the task indicated a Predictability by Emotional Category interaction, such that temporal unpredictability enhanced startle potentiation in anticipation of unpleasant pictures only. Examination of the intercept at the end of the task indicated that the effects of predictability and emotional category on startle magnitude were largely diminished. The present study replicates previous reports demonstrating that emotional category and temporal predictability impact the startle reflex, and provides novel evidence suggesting an interactive effect on defensive motivation at the beginning of the task. This study also highlights the importance of examining the time course of the startle reflex. Copyright © 2017 Elsevier B.V. All rights reserved.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

PubMed Central

2013-01-01

Background Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs. Results A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset. Conclusions Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html PMID:23815072

Ghosts, UFOs, and magic: positive affect and the experiential system.

PubMed

King, Laura A; Burton, Chad M; Hicks, Joshua A; Drigotas, Stephen M

2007-05-01

Three studies examined the potential interactions of the experiential system and positive affect (PA) in predicting superstitious beliefs and sympathetic magic. In Study 1, experientiality and induced positive mood interacted to predict the emergence of belief in videos purporting to show unidentified flying objects or ghosts. In Study 2, naturally occurring PA interacted with experientiality to predict susceptibility to sympathetic magic, specifically difficulty in throwing darts at a picture of a baby (demonstrating the law of similarity). In Study 3, induced mood interacted with experientiality to predict sitting farther away from, and expressing less liking for, a partner who had stepped in excrement (demonstrating the law of contagion). Results are interpreted as indicating that PA promotes experiential processing. Implications for the psychology of nonrational beliefs and behaviors are discussed. ((c) 2007 APA, all rights reserved).

Three-dimensional viscous rotor flow calculations using a viscous-inviscid interaction approach

NASA Technical Reports Server (NTRS)

Chen, Ching S.; Bridgeman, John O.

1990-01-01

A three-dimensional viscous-inviscid interaction analysis was developed to predict the performance of rotors in hover and in forward flight at subsonic and transonic tip speeds. The analysis solves the full-potential and boundary-layer equations by finite-difference numerical procedures. Calculations were made for several different model rotor configurations. The results were compared with predictions from a two-dimensional integral method and with experimental data. The comparisons show good agreement between predictions and test data.

Systematic identification of phosphorylation-mediated protein interaction switches

PubMed Central

Wichmann, Oliver; Utz, Mathias; Andre, Timon; Minguez, Pablo; Parca, Luca; Roth, Frederick P.; Gavin, Anne-Claude; Bork, Peer; Russell, Robert B.

2017-01-01

Proteomics techniques can identify thousands of phosphorylation sites in a single experiment, the majority of which are new and lack precise information about function or molecular mechanism. Here we present a fast method to predict potential phosphorylation switches by mapping phosphorylation sites to protein-protein interactions of known structure and analysing the properties of the protein interface. We predict 1024 sites that could potentially enable or disable particular interactions. We tested a selection of these switches and showed that phosphomimetic mutations indeed affect interactions. We estimate that there are likely thousands of phosphorylation mediated switches yet to be uncovered, even among existing phosphorylation datasets. The results suggest that phosphorylation sites on globular, as distinct from disordered, parts of the proteome frequently function as switches, which might be one of the ancient roles for kinase phosphorylation. PMID:28346509

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

PubMed

Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

2016-09-01

Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Differential C3NET reveals disease networks of direct physical interactions

PubMed Central

2011-01-01

Background Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. Results C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. Conclusions Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network. PMID:21777411

On their best behavior: how animal behavior can help determine the combined effects of species interactions and climate change.

PubMed

Harmon, Jason P; Barton, Brandon T

2013-09-01

The increasingly appreciated link between climate change and species interactions has the potential to help us understand and predict how organisms respond to a changing environment. As this connection grows, it becomes even more important to appreciate the mechanisms that create and control the combined effect of these factors. However, we believe one such important set of mechanisms comes from species' behavior and the subsequent trait-mediated interactions, as opposed to the more often studied density-mediated effects. Behavioral mechanisms are already well appreciated for mitigating the separate effects of the environment and species interactions. Thus, they could be at the forefront for understanding the combined effects. In this review, we (1) show some of the known behaviors that influence the individual and combined effects of climate change and species interactions; (2) conceptualize general ways behavior may mediate these combined effects; and (3) illustrate the potential importance of including behavior in our current tools for predicting climate change effects. In doing so, we hope to promote more research on behavior and other mechanistic factors that may increase our ability to accurately predict climate change effects. © 2013 New York Academy of Sciences.

Bioinformatic prediction and in vivo validation of residue-residue interactions in human proteins

NASA Astrophysics Data System (ADS)

Jordan, Daniel; Davis, Erica; Katsanis, Nicholas; Sunyaev, Shamil

2014-03-01

Identifying residue-residue interactions in protein molecules is important for understanding both protein structure and function in the context of evolutionary dynamics and medical genetics. Such interactions can be difficult to predict using existing empirical or physical potentials, especially when residues are far from each other in sequence space. Using a multiple sequence alignment of 46 diverse vertebrate species we explore the space of allowed sequences for orthologous protein families. Amino acid changes that are known to damage protein function allow us to identify specific changes that are likely to have interacting partners. We fit the parameters of the continuous-time Markov process used in the alignment to conclude that these interactions are primarily pairwise, rather than higher order. Candidates for sites under pairwise epistasis are predicted, which can then be tested by experiment. We report the results of an initial round of in vivo experiments in a zebrafish model that verify the presence of multiple pairwise interactions predicted by our model. These experimentally validated interactions are novel, distant in sequence, and are not readily explained by known biochemical or biophysical features.

Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

PubMed

Yan, Yumeng; Wen, Zeyu; Zhang, Di; Huang, Sheng-You

2018-05-18

RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.

Theoretical study of solvent effects on the coil-globule transition

NASA Astrophysics Data System (ADS)

Polson, James M.; Opps, Sheldon B.; Abou Risk, Nicholas

2009-06-01

The coil-globule transition of a polymer in a solvent has been studied using Monte Carlo simulations of a single chain subject to intramolecular interactions as well as a solvent-mediated effective potential. This solvation potential was calculated using several different theoretical approaches for two simple polymer/solvent models, each employing hard-sphere chains and hard-sphere solvent particles as well as attractive square-well potentials between some interaction sites. For each model, collapse is driven by variation in a parameter which changes the energy mismatch between monomers and solvent particles. The solvation potentials were calculated using two fundamentally different methodologies, each designed to predict the conformational behavior of polymers in solution: (1) the polymer reference interaction site model (PRISM) theory and (2) a many-body solvation potential (MBSP) based on scaled particle theory introduced by Grayce [J. Chem. Phys. 106, 5171 (1997)]. For the PRISM calculations, two well-studied solvation monomer-monomer pair potentials were employed, each distinguished by the closure relation used in its derivation: (i) a hypernetted-chain (HNC)-type potential and (ii) a Percus-Yevick (PY)-type potential. The theoretical predictions were each compared to results obtained from explicit-solvent discontinuous molecular dynamics simulations on the same polymer/solvent model systems [J. Chem. Phys. 125, 194904 (2006)]. In each case, the variation in the coil-globule transition properties with solvent density is mostly qualitatively correct, though the quantitative agreement between the theory and prediction is typically poor. The HNC-type potential yields results that are more qualitatively consistent with simulation. The conformational behavior of the polymer upon collapse predicted by the MBSP approach is quantitatively correct for low and moderate solvent densities but is increasingly less accurate for higher densities. At high solvent densities, the PRISM-HNC and MBSP approaches tend to overestimate, while the PRISM-PY approach underestimates the tendency of the solvent to drive polymer collapse.

DLVO Approximation Methods for Predicting the Attachment of Silver Nanoparticles to Ceramic Membranes.

PubMed

Mikelonis, Anne M; Youn, Sungmin; Lawler, Desmond F

2016-02-23

This article examines the influence of three common stabilizing agents (citrate, poly(vinylpyrrolidone) (PVP), and branched poly(ethylenimine) (BPEI)) on the attachment affinity of silver nanoparticles to ceramic water filters. Citrate-stabilized silver nanoparticles were found to have the highest attachment affinity (under conditions in which the surface potential was of opposite sign to the filter). This work demonstrates that the interaction between the electrical double layers plays a critical role in the attachment of nanoparticles to flat surfaces and, in particular, that predictions of double-layer interactions are sensitive to boundary condition assumptions (constant charge vs constant potential). The experimental deposition results can be explained when using different boundary condition assumptions for different stabilizing molecules but not when the same assumption was assumed for all three types of particles. The integration of steric interactions can also explain the experimental deposition results. Particle size was demonstrated to have an effect on the predicted deposition for BPEI-stabilized particles but not for PVP.

Genetic Influences Can Protect Against Unresponsive Parenting in the Prediction of Child Social Competence

PubMed Central

Van Ryzin, Mark J.; Leve, Leslie D.; Neiderhiser, Jenae M.; Shaw, Daniel S.; Natsuaki, Misaki N.; Reiss, David

2014-01-01

Although social competence in children has been linked to the quality of parenting, prior research has typically not accounted for genetic similarities between parents and children, or for interactions between environmental (i.e., parental) and genetic influences. In this paper, we evaluate the possibility of a gene-by-environment (GxE) interaction in the prediction of social competence in school-age children. Using a longitudinal, multi-method dataset from a sample of children adopted at birth (N = 361), we found a significant interaction between birth parent sociability and sensitive, responsive adoptive parenting when predicting child social competence at school entry (age 6), even when controlling for potential confounds. An analysis of the interaction revealed that genetic strengths can buffer the effects of unresponsive parenting. PMID:25581124

Rotor/Wing Interactions in Hover

NASA Technical Reports Server (NTRS)

Young, Larry A.; Derby, Michael R.

2002-01-01

Hover predictions of tiltrotor aircraft are hampered by the lack of accurate and computationally efficient models for rotor/wing interactional aerodynamics. This paper summarizes the development of an approximate, potential flow solution for the rotor-on-rotor and wing-on-rotor interactions. This analysis is based on actuator disk and vortex theory and the method of images. The analysis is applicable for out-of-ground-effect predictions. The analysis is particularly suited for aircraft preliminary design studies. Flow field predictions from this simple analytical model are validated against experimental data from previous studies. The paper concludes with an analytical assessment of the influence of rotor-on-rotor and wing-on-rotor interactions. This assessment examines the effect of rotor-to-wing offset distance, wing sweep, wing span, and flaperon incidence angle on tiltrotor inflow and performance.

Interaction potential for indium phosphide: a molecular dynamics and first-principles study of the elastic constants, generalized stacking fault and surface energies.

PubMed

Branicio, Paulo Sergio; Rino, José Pedro; Gan, Chee Kwan; Tsuzuki, Hélio

2009-03-04

Indium phosphide is investigated using molecular dynamics (MD) simulations and density-functional theory calculations. MD simulations use a proposed effective interaction potential for InP fitted to a selected experimental dataset of properties. The potential consists of two- and three-body terms that represent atomic-size effects, charge-charge, charge-dipole and dipole-dipole interactions as well as covalent bond bending and stretching. Predictions are made for the elastic constants as a function of density and temperature, the generalized stacking fault energy and the low-index surface energies.

Nucleon-deuteron scattering with the JISP16 potential

NASA Astrophysics Data System (ADS)

Skibiński, R.; Golak, J.; Topolnicki, K.; Witała, H.; Volkotrub, Yu.; Kamada, H.; Shirokov, A. M.; Okamoto, R.; Suzuki, K.; Vary, J. P.

2018-01-01

The nucleon-nucleon J -matrix inverse scattering potential JISP16 is applied to elastic nucleon-deuteron scattering and the deuteron breakup process at the laboratory nucleon energies up to 135 MeV. The formalism of the Faddeev equations is used to obtain three-nucleon scattering states. We compare predictions based on the JISP16 force with data and with results based on various two-body interactions, including the CD Bonn, the Argonne AV18, the chiral force with the semilocal regularization at the fifth order of the chiral expansion and with low-momentum interactions obtained from the CD Bonn force as well as with the predictions from the combination of the AV18 NN interaction and the Urbana IX 3 N force. JISP16 provides a satisfactory description of some observables at low energies but strong deviations from data as well as from standard and chiral potential predictions with increasing energy. However, there are also polarization observables at low energies for which the JISP16 predictions differ from those based on the other forces by a factor of two. The reason for such a behavior can be traced back to the P -wave components of the JISP16 force. At higher energies the deviations can be enhanced by an interference with higher partial waves and by the properties of the JISP16 deuteron wave function. In addition, we compare the energy and angular dependence of predictions based on the JISP16 force with the results of the low-momentum interactions obtained with different values of the momentum cutoff parameter. We found that such low-momentum forces can be employed to interpret the nucleon-deuteron elastic scattering data only below some specific energy which depends on the cutoff parameter. Since JISP16 is defined in a finite oscillator basis, it has properties similar to low momentum interactions and its application to the description of nucleon-deuteron scattering data is limited to a low momentum transfer region.

Computational prediction of host-pathogen protein-protein interactions.

PubMed

Dyer, Matthew D; Murali, T M; Sobral, Bruno W

2007-07-01

Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. Supplementary data are available at Bioinformatics online.

Efficient prediction of human protein-protein interactions at a global scale.

PubMed

Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

2014-12-10

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

High-temperature fatigue in metals - A brief review of life prediction methods developed at the Lewis Research Center of NASA

NASA Technical Reports Server (NTRS)

Halford, G. R.

1983-01-01

The presentation focuses primarily on the progress we at NASA Lewis Research Center have made. The understanding of the phenomenological processes of high temperature fatigue of metals for the purpose of calculating lives of turbine engine hot section components is discussed. Improved understanding resulted in the development of accurate and physically correct life prediction methods such as Strain-Range partitioning for calculating creep fatigue interactions and the Double Linear Damage Rule for predicting potentially severe interactions between high and low cycle fatigue. Examples of other life prediction methods are also discussed. Previously announced in STAR as A83-12159

Maneuvering a reentry body via magneto-gasdynamic forces

NASA Astrophysics Data System (ADS)

Ohare, Leo Patrick

1992-04-01

Some of the characteristics of the interaction of an electrically conducting fluid with a non-uniform applied magnetic field and a potential magnetogasdynamic control system which may be used on future aerospace vehicles are presented. The flow through a two dimensional channel is predicted by numerically solving the magnetogasdynamic equations using a time marching technique. The fluid was modeled as a compressible, inviscid, supersonic gas with finite electrical conductivity. Development of the algorithm provided a means to predict and analyze phenomena associated with magnetogasdynamic flows which had not been previously explored using numerical methods. One such phenomena was the prediction of oblique waves resulting from the interaction of an electrically conducting fluid with a non-uniform applied magnetic field. Development of this tool provided a means to explore an application which might have potential use for future aerospace vehicle missions. In order to appreciate the significance of this technology, predictions were made of the pitching moment about a slender blunted cone, generated by a system relying on the fluid-magnetic interaction. These moments were compared to predictions of a pitching moment generated by a deflecting control surface on the same vehicle. It was shown that the proposed magnetogasdynamic system could produce moments which were on the same order as the moments produced by the flap systems at low deflection angles.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

PubMed

Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques; Ferreira, Rafaela Salgado; Silva, Artur; Gromiha, Michael; Ghosh, Preetam; Barh, Debmalya; Azevedo, Vasco; Röttger, Richard

2016-11-04

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Metabolome of human gut microbiome is predictive of host dysbiosis.

PubMed

Larsen, Peter E; Dai, Yang

2015-01-01

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

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

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

Metabolome of human gut microbiome is predictive of host dysbiosis

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

Larsen, Peter E.; Dai, Yang

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

Metabolome of human gut microbiome is predictive of host dysbiosis

DOE PAGES

Larsen, Peter E.; Dai, Yang

2015-09-14

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

Quantifying Additive Interactions of the Osmolyte Proline with Individual Functional Groups of Proteins: Comparisons with Urea and Glycine Betaine, Interpretation of m-Values

PubMed Central

Diehl, Roger C.; Guinn, Emily J.; Capp, Michael W.; Tsodikov, Oleg V.; Record, M. Thomas

2013-01-01

To quantify interactions of the osmolyte L-proline with protein functional groups and predict its effects on protein processes, we use vapor pressure osmometry to determine chemical potential derivatives dµ2/dm3 = µ23 quantifying preferential interactions of proline (component 3) with 21 solutes (component 2) selected to display different combinations of aliphatic or aromatic C, amide, carboxylate, phosphate or hydroxyl O, and/or amide or cationic N surface. Solubility data yield µ23 values for 4 less-soluble solutes. Values of µ23 are dissected using an ASA-based analysis to test the hypothesis of additivity and obtain α-values (proline interaction potentials) for these eight surface types and three inorganic ions. Values of µ23 predicted from these α-values agree with experiment, demonstrating additivity. Molecular interpretation of α-values using the solute partitioning model yields partition coefficients (Kp) quantifying the local accumulation or exclusion of proline in the hydration water of each functional group. Interactions of proline with native protein surface and effects of proline on protein unfolding are predicted from α-values and ASA information and compared with experimental data, with results for glycine betaine and urea, and with predictions from transfer free energy analysis. We conclude that proline stabilizes proteins because of its unfavorable interactions with (exclusion from) amide oxygens and aliphatic hydrocarbon surface exposed in unfolding, and that proline is an effective in vivo osmolyte because of the osmolality increase resulting from its unfavorable interactions with anionic (carboxylate and phosphate) and amide oxygens and aliphatic hydrocarbon groups on the surface of cytoplasmic proteins and nucleic acids. PMID:23909383

Direct measurements of protein-stabilized gold nanoparticle interactions.

PubMed

Eichmann, Shannon L; Bevan, Michael A

2010-09-21

We report integrated video and total internal reflection microscopy measurements of protein stabilized 110 nm Au nanoparticles confined in 280 nm gaps in physiological media. Measured potential energy profiles display quantitative agreement with Brownian dynamic simulations that include hydrodynamic interactions and camera exposure time and noise effects. Our results demonstrate agreement between measured nonspecific van der Waals and adsorbed protein interactions with theoretical potentials. Confined, lateral nanoparticle diffusivity measurements also display excellent agreement with predictions. These findings provide a basis to interrogate specific biomacromolecular interactions in similar experimental configurations and to design future improved measurement methods.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Biokinetics of zinc oxide nanoparticles: toxicokinetics, biological fates, and protein interaction

PubMed Central

Choi, Soo-Jin; Choy, Jin-Ho

2014-01-01

Biokinetic studies of zinc oxide (ZnO) nanoparticles involve systematic and quantitative analyses of absorption, distribution, metabolism, and excretion in plasma and tissues of whole animals after exposure. A full understanding of the biokinetics provides basic information about nanoparticle entry into systemic circulation, target organs of accumulation and toxicity, and elimination time, which is important for predicting the long-term toxic potential of nanoparticles. Biokinetic behaviors can be dependent on physicochemical properties, dissolution property in biological fluids, and nanoparticle–protein interaction. Moreover, the determination of biological fates of ZnO nanoparticles in the systemic circulation and tissues is critical in interpreting biokinetic behaviors and predicting toxicity potential as well as mechanism. This review focuses on physicochemical factors affecting the biokinetics of ZnO nanoparticles, in concert with understanding bioavailable fates and their interaction with proteins. PMID:25565844

Ab initio interatomic potentials and the thermodynamic properties of fluids

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-07-01

Monte Carlo simulations with accurate ab initio interatomic potentials are used to investigate the key thermodynamic properties of argon and krypton in both vapor and liquid phases. Data are reported for the isochoric and isobaric heat capacities, the Joule-Thomson coefficient, and the speed of sound calculated using various two-body interatomic potentials and different combinations of two-body plus three-body terms. The results are compared to either experimental or reference data at state points between the triple and critical points. Using accurate two-body ab initio potentials, combined with three-body interaction terms such as the Axilrod-Teller-Muto and Marcelli-Wang-Sadus potentials, yields systematic improvements to the accuracy of thermodynamic predictions. The effect of three-body interactions is to lower the isochoric and isobaric heat capacities and increase both the Joule-Thomson coefficient and speed of sound. The Marcelli-Wang-Sadus potential is a computationally inexpensive way to utilize accurate two-body ab initio potentials for the prediction of thermodynamic properties. In particular, it provides a very effective way of extending two-body ab initio potentials to liquid phase properties.

Ab initio interatomic potentials and the thermodynamic properties of fluids.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-07-14

Monte Carlo simulations with accurate ab initio interatomic potentials are used to investigate the key thermodynamic properties of argon and krypton in both vapor and liquid phases. Data are reported for the isochoric and isobaric heat capacities, the Joule-Thomson coefficient, and the speed of sound calculated using various two-body interatomic potentials and different combinations of two-body plus three-body terms. The results are compared to either experimental or reference data at state points between the triple and critical points. Using accurate two-body ab initio potentials, combined with three-body interaction terms such as the Axilrod-Teller-Muto and Marcelli-Wang-Sadus potentials, yields systematic improvements to the accuracy of thermodynamic predictions. The effect of three-body interactions is to lower the isochoric and isobaric heat capacities and increase both the Joule-Thomson coefficient and speed of sound. The Marcelli-Wang-Sadus potential is a computationally inexpensive way to utilize accurate two-body ab initio potentials for the prediction of thermodynamic properties. In particular, it provides a very effective way of extending two-body ab initio potentials to liquid phase properties.

Utilization of Integrated Assessment Modeling for determining geologic CO2 storage security

NASA Astrophysics Data System (ADS)

Pawar, R.

2017-12-01

Geologic storage of carbon dioxide (CO2) has been extensively studied as a potential technology to mitigate atmospheric concentration of CO2. Multiple international research & development efforts, large-scale demonstration and commercial projects are helping advance the technology. One of the critical areas of active investigation is prediction of long-term CO2 storage security and risks. A quantitative methodology for predicting a storage site's long-term performance is critical for making key decisions necessary for successful deployment of commercial scale projects where projects will require quantitative assessments of potential long-term liabilities. These predictions are challenging given that they require simulating CO2 and in-situ fluid movements as well as interactions through the primary storage reservoir, potential leakage pathways (such as wellbores, faults, etc.) and shallow resources such as groundwater aquifers. They need to take into account the inherent variability and uncertainties at geologic sites. This talk will provide an overview of an approach based on integrated assessment modeling (IAM) to predict long-term performance of a geologic storage site including, storage reservoir, potential leakage pathways and shallow groundwater aquifers. The approach utilizes reduced order models (ROMs) to capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. Applicability of the approach will be demonstrated through examples that are focused on key storage security questions such as what is the probability of leakage of CO2 from a storage reservoir? how does storage security vary for different geologic environments and operational conditions? how site parameter variability and uncertainties affect storage security, etc.

Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

PubMed

Bauer, Katharina Christin; Hämmerling, Frank; Kittelmann, Jörg; Dürr, Cathrin; Görlich, Fabian; Hubbuch, Jürgen

2017-04-01

Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Influence of Food on Paediatric Gastrointestinal Drug Absorption Following Oral Administration: A Review

PubMed Central

Batchelor, Hannah K.

2015-01-01

The objective of this paper was to review existing information regarding food effects on drug absorption within paediatric populations. Mechanisms that underpin food–drug interactions were examined to consider potential differences between adult and paediatric populations, to provide insights into how this may alter the pharmacokinetic profile in a child. Relevant literature was searched to retrieve information on food–drug interaction studies undertaken on: (i) paediatric oral drug formulations; and (ii) within paediatric populations. The applicability of existing methodology to predict food effects in adult populations was evaluated with respect to paediatric populations where clinical data was available. Several differences in physiology, anatomy and the composition of food consumed within a paediatric population are likely to lead to food–drug interactions that cannot be predicted based on adult studies. Existing methods to predict food effects cannot be directly extrapolated to allow predictions within paediatric populations. Development of systematic methods and guidelines is needed to address the general lack of information on examining food–drug interactions within paediatric populations. PMID:27417362

Mapping Protein Interactions between Dengue Virus and Its Human and Insect Hosts

PubMed Central

Doolittle, Janet M.; Gomez, Shawn M.

2011-01-01

Background Dengue fever is an increasingly significant arthropod-borne viral disease, with at least 50 million cases per year worldwide. As with other viral pathogens, dengue virus is dependent on its host to perform the bulk of functions necessary for viral survival and replication. To be successful, dengue must manipulate host cell biological processes towards its own ends, while avoiding elimination by the immune system. Protein-protein interactions between the virus and its host are one avenue through which dengue can connect and exploit these host cellular pathways and processes. Methodology/Principal Findings We implemented a computational approach to predict interactions between Dengue virus (DENV) and both of its hosts, Homo sapiens and the insect vector Aedes aegypti. Our approach is based on structural similarity between DENV and host proteins and incorporates knowledge from the literature to further support a subset of the predictions. We predict over 4,000 interactions between DENV and humans, as well as 176 interactions between DENV and A. aegypti. Additional filtering based on shared Gene Ontology cellular component annotation reduced the number of predictions to approximately 2,000 for humans and 18 for A. aegypti. Of 19 experimentally validated interactions between DENV and humans extracted from the literature, this method was able to predict nearly half (9). Additional predictions suggest specific interactions between virus and host proteins relevant to interferon signaling, transcriptional regulation, stress, and the unfolded protein response. Conclusions/Significance Dengue virus manipulates cellular processes to its advantage through specific interactions with the host's protein interaction network. The interaction networks presented here provide a set of hypothesis for further experimental investigation into the DENV life cycle as well as potential therapeutic targets. PMID:21358811

Using the surface panel method to predict the steady performance of ducted propellers

NASA Astrophysics Data System (ADS)

Cai, Hao-Peng; Su, Yu-Min; Li, Xin; Shen, Hai-Long

2009-12-01

A new numerical method was developed for predicting the steady hydrodynamic performance of ducted propellers. A potential based surface panel method was applied both to the duct and the propeller, and the interaction between them was solved by an induced velocity potential iterative method. Compared with the induced velocity iterative method, the method presented can save programming and calculating time. Numerical results for a JD simplified ducted propeller series showed that the method presented is effective for predicting the steady hydrodynamic performance of ducted propellers.

Design, Fabrication, and Testing of Emissive Probes to Determine the Plasma Potential of the Plumes of Various Electric Thrusters

NASA Technical Reports Server (NTRS)

Chen, Erinna M.

2005-01-01

A significant problem in the use of electric thrusters in spacecraft is the formation of low-energy ions in the thruster plume. Low-energy ions are formed in the plume via random collisions between high-velocity ions ejected from the thruster and slow-moving neutral atoms of propellant effusing from the engine. The sputtering of spacecraft materials due to interactions with low-energy ions may result in erosion or contamination of the spacecraft. The trajectory of these ions is determined primarily by the plasma potential of the plume. Thus, accurate characterization of the plasma potential is essential to predicting low-energy ion contamination. Emissive probes were utilized to determine the plasma potential. When the ion and electron currents to the probe are balanced, the potential of such probes float to the plasma potential. Two emissive probes were fabricated; one utilizing a DC power supply, another utilizing a rectified AC power source. Labview programs were written to coordinate and automate probe motion in the thruster plume. Employing handshaking interaction, these motion programs were synchronized to various data acquisition programs to ensure precision and accuracy of the measurements. Comparing these experimental values to values from theoretical models will allow for a more accurate prediction of low-energy ion interaction.

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients.

PubMed

Zorina, Olesya I; Haueis, Patrick; Semmler, Alexander; Marti, Isabelle; Gonzenbach, Roman R; Guzek, Markus; Kullak-Ublick, Gerd A; Weller, Michael; Russmann, Stefan

2012-08-01

The comparative evaluation of clinical decision support software (CDSS) programs regarding their sensitivity and positive predictive value for the identification of clinically relevant drug interactions. In this research, we used a cross-sectional study that identified potential drug interactions using the CDSS MediQ and the ID PHARMA CHECK in 484 neurological inpatients. Interactions were reclassified according to the Zurich Interaction System, a multidimensional classification that incorporates the Operational Classification of Drug Interactions. In 484 patients with 2812 prescriptions, MediQ and ID PHARMA CHECK generated a total of 1759 and 1082 alerts, respectively. MediQ identified 658 unique potentially interacting combinations, 8 classified as "high danger," 164 as "average danger," and 486 as "low danger." ID PHARMA CHECK detected 336 combinations assigned to one or several of 12 risk and management categories. Altogether, both CDSS issued alerts relating to 808 unique potentially interacting combinations. According to the Zurich Interaction System, 6 of these were contraindicated, 25 were provisionally contraindicated, 190 carried a conditional risk, and 587 had a minimal risk of adverse events. The positive predictive value for alerts having at least a conditional risk was 0.24 for MediQ and 0.48 for ID PHARMA CHECK. CDSS showed major differences in the identification and grading of interactions, and many interactions were only identified by one of the two CDSS. For both programs, only a small proportion of all identified interactions appeared clinically relevant, and the selected display of alerts that imply management changes is a key issue in the further development and local setup of such programs. Copyright © 2012 John Wiley & Sons, Ltd.

Maize transgenes containing zein promoters are regulated by opaque2

USDA-ARS?s Scientific Manuscript database

Transgenes have great potential in crop improvement, but relatively little is known about the epistatic interaction of transgenes with the native genes in the genome. Understanding these interactions is critical for predicting the response of transgenes to different genetic backgrounds and environm...

Valerian: no evidence for clinically relevant interactions.

PubMed

Kelber, Olaf; Nieber, Karen; Kraft, Karin

2014-01-01

In recent popular publications as well as in widely used information websites directed to cancer patients, valerian is claimed to have a potential of adverse interactions with anticancer drugs. This questions its use as a safe replacement for, for example, benzodiazepines. A review on the interaction potential of preparations from valerian root (Valeriana officinalis L. root) was therefore conducted. A data base search and search in a clinical drug interaction data base were conducted. Thereafter, a systematic assessment of publications was performed. Seven in vitro studies on six CYP 450 isoenzymes, on p-glycoprotein, and on two UGT isoenzymes were identified. However, the methodological assessment of these studies did not support their suitability for the prediction of clinically relevant interactions. In addition, clinical studies on various valerian preparations did not reveal any relevant interaction potential concerning CYP 1A2, 2D6, 2E1, and 3A4. Available animal and human pharmacodynamic studies did not verify any interaction potential. The interaction potential of valerian preparations therefore seems to be low and thereby without clinical relevance. We conclude that there is no specific evidence questioning their safety, also in cancer patients.

Valerian: No Evidence for Clinically Relevant Interactions

PubMed Central

Nieber, Karen; Kraft, Karin

2014-01-01

In recent popular publications as well as in widely used information websites directed to cancer patients, valerian is claimed to have a potential of adverse interactions with anticancer drugs. This questions its use as a safe replacement for, for example, benzodiazepines. A review on the interaction potential of preparations from valerian root (Valeriana officinalis L. root) was therefore conducted. A data base search and search in a clinical drug interaction data base were conducted. Thereafter, a systematic assessment of publications was performed. Seven in vitro studies on six CYP 450 isoenzymes, on p-glycoprotein, and on two UGT isoenzymes were identified. However, the methodological assessment of these studies did not support their suitability for the prediction of clinically relevant interactions. In addition, clinical studies on various valerian preparations did not reveal any relevant interaction potential concerning CYP 1A2, 2D6, 2E1, and 3A4. Available animal and human pharmacodynamic studies did not verify any interaction potential. The interaction potential of valerian preparations therefore seems to be low and thereby without clinical relevance. We conclude that there is no specific evidence questioning their safety, also in cancer patients. PMID:25093031

Experimental evidence of solitary wave interaction in Hertzian chains

NASA Astrophysics Data System (ADS)

Santibanez, Francisco; Munoz, Romina; Caussarieu, Aude; Job, Stéphane; Melo, Francisco

2011-08-01

We study experimentally the interaction between two solitary waves that approach one another in a linear chain of spheres interacting via the Hertz potential. When these counterpropagating waves collide, they cross each other and a phase shift in respect to the noninteracting waves is introduced as a result of the nonlinear interaction potential. This observation is well reproduced by our numerical simulations and is shown to be independent of viscoelastic dissipation at the bead contact. In addition, when the collision of equal amplitude and synchronized counterpropagating waves takes place, we observe that two secondary solitary waves emerge from the interacting region. The amplitude of the secondary solitary waves is proportional to the amplitude of incident waves. However, secondary solitary waves are stronger when the collision occurs at the middle contact in chains with an even number of beads. Although numerical simulations correctly predict the existence of these waves, experiments show that their respective amplitudes are significantly larger than predicted. We attribute this discrepancy to the rolling friction at the bead contact during solitary wave propagation.

Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model.

PubMed

Fernandes, Jose A; Cheung, William W L; Jennings, Simon; Butenschön, Momme; de Mora, Lee; Frölicher, Thomas L; Barange, Manuel; Grant, Alastair

2013-08-01

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment. © 2013 John Wiley & Sons Ltd.

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

PubMed

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

2014-09-01

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

Genetic influences can protect against unresponsive parenting in the prediction of child social competence.

PubMed

Van Ryzin, Mark J; Leve, Leslie D; Neiderhiser, Jenae M; Shaw, Daniel S; Natsuaki, Misaki N; Reiss, David

2015-01-01

Although social competence in children has been linked to the quality of parenting, prior research has typically not accounted for genetic similarities between parents and children, or for interactions between environmental (i.e., parental) and genetic influences. In this article, the possibility of a Gene x Environment (G × E) interaction in the prediction of social competence in school-age children is evaluated. Using a longitudinal, multimethod data set from a sample of children adopted at birth (N = 361), a significant interaction was found between birth parent sociability and sensitive, responsive adoptive parenting when predicting child social competence at school entry (age 6), even when controlling for potential confounds. An analysis of the interaction revealed that genetic strengths can buffer the effects of unresponsive parenting. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

PubMed Central

Huynen, Martijn; Snel, Berend; Lathe, Warren; Bork, Peer

2000-01-01

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes. PMID:10958638

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

Polymer adhesion predictions for oral dosage forms to enhance drug administration safety. Part 2: In vitro approach using mechanical force methods.

PubMed

Drumond, Nélio; Stegemann, Sven

2018-06-01

Predicting the potential for unintended adhesion of solid oral dosage forms (SODF) to mucosal tissue is an important aspect that should be considered during drug product development. Previous investigations into low strength mucoadhesion based on particle interactions methods provided evidence that rheological measurements could be used to obtain valid predictions for the development of SODF coatings that can be safely swallowed. The aim of this second work was to estimate the low mucoadhesive strength properties of different polymers using in vitro methods based on mechanical forces and to identify which methods are more precise when measuring reduced mucoadhesion. Another aim was to compare the obtained results to the ones achieved with in vitro particle interaction methods in order to evaluate which methodology can provide stronger predictions. The combined results correlate between particle interaction methods and mechanical force measurements. The polyethylene glycol grades (PEG) and carnauba wax showed the lowest adhesive potential and are predicted to support safe swallowing. Hydroxypropyl methylcellulose (HPMC) along with high molecular grades of polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) exhibited strong in vitro mucoadhesive strength. The combination of rheological and force tensiometer measurements should be considered when assessing the reduced mucoadhesion of polymer coatings to support safe swallowing of SODF. Copyright © 2018 Elsevier B.V. All rights reserved.

Seasonal resource value and male size influence male aggressive interactions in the leaf footed cactus bug, Narnia femorata.

PubMed

Nolen, Zachary J; Allen, Pablo E; Miller, Christine W

2017-05-01

In animal contests, resource value (the quality of a given resource) and resource holding potential (a male's absolute fighting ability) are two important factors determining the level of engagement and outcome of contests. Few studies have tested these factors simultaneously. Here, we investigated whether natural, seasonal differences in cactus phenology (fruit quality) influence interactions between males in the leaf-footed cactus bug, Narnia femorata (Hemiptera: Coreidae). We also considered whether males were more likely to interact when they were similar in size, as predicted by theory. Finally, we examined if male size relative to the size of an opponent predicted competitive success. We found that males have more interactions on cactus with high value ripe fruit, as we predicted. Further, we found that males that were closer in size were more likely to interact, and larger males were more likely to become dominant. Copyright © 2017 Elsevier B.V. All rights reserved.

A network approach to predict pathogenic genes for Fusarium graminearum.

PubMed

Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

2010-10-04

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-06-01

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-06-28

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Error correction in multi-fidelity molecular dynamics simulations using functional uncertainty quantification

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

Reeve, Samuel Temple; Strachan, Alejandro, E-mail: strachan@purdue.edu

We use functional, Fréchet, derivatives to quantify how thermodynamic outputs of a molecular dynamics (MD) simulation depend on the potential used to compute atomic interactions. Our approach quantifies the sensitivity of the quantities of interest with respect to the input functions as opposed to its parameters as is done in typical uncertainty quantification methods. We show that the functional sensitivity of the average potential energy and pressure in isothermal, isochoric MD simulations using Lennard–Jones two-body interactions can be used to accurately predict those properties for other interatomic potentials (with different functional forms) without re-running the simulations. This is demonstrated undermore » three different thermodynamic conditions, namely a crystal at room temperature, a liquid at ambient pressure, and a high pressure liquid. The method provides accurate predictions as long as the change in potential can be reasonably described to first order and does not significantly affect the region in phase space explored by the simulation. The functional uncertainty quantification approach can be used to estimate the uncertainties associated with constitutive models used in the simulation and to correct predictions if a more accurate representation becomes available.« less

Running non-minimal inflation with stabilized inflaton potential

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

Okada, Nobuchika; Raut, Digesh

In the context of the Higgs model involving gauge and Yukawa interactions with the spontaneous gauge symmetry breaking, we consider λφ4 inflation with non- minimal gravitational coupling, where the Higgs field is identified as the inflaton. Since the inflaton quartic coupling is very small, once quantum corrections through the gauge and Yukawa interactions are taken into account, the inflaton effective potential most likely becomes unstable. Furthermore, in order to avoid this problem, we need to impose stability conditions on the effective inflaton potential, which lead to not only non-trivial relations amongst the particle mass spectrum of the model, but alsomore » correlations between the inflationary predictions and the mass spectrum. For reasons of concrete discussion, we investigate the minimal B - L extension of the standard model with identification of the B - L Higgs field as the inflaton. The stability conditions for the inflaton effective potential fix the mass ratio amongst the B - L gauge boson, the right-handed neutrinos and the inflaton. This mass ratio also correlates with the inflationary predictions. So, if the B - L gauge boson and the right-handed neutrinos are discovered in the future, their observed mass ratio provides constraints on the inflationary predictions.« less

Running non-minimal inflation with stabilized inflaton potential

DOE PAGES

Okada, Nobuchika; Raut, Digesh

2017-04-18

In the context of the Higgs model involving gauge and Yukawa interactions with the spontaneous gauge symmetry breaking, we consider λφ4 inflation with non- minimal gravitational coupling, where the Higgs field is identified as the inflaton. Since the inflaton quartic coupling is very small, once quantum corrections through the gauge and Yukawa interactions are taken into account, the inflaton effective potential most likely becomes unstable. Furthermore, in order to avoid this problem, we need to impose stability conditions on the effective inflaton potential, which lead to not only non-trivial relations amongst the particle mass spectrum of the model, but alsomore » correlations between the inflationary predictions and the mass spectrum. For reasons of concrete discussion, we investigate the minimal B - L extension of the standard model with identification of the B - L Higgs field as the inflaton. The stability conditions for the inflaton effective potential fix the mass ratio amongst the B - L gauge boson, the right-handed neutrinos and the inflaton. This mass ratio also correlates with the inflationary predictions. So, if the B - L gauge boson and the right-handed neutrinos are discovered in the future, their observed mass ratio provides constraints on the inflationary predictions.« less

On the interaction of deaffrication and consonant harmony*

PubMed Central

Dinnsen, Daniel A.; Gierut, Judith A.; Morrisette, Michele L.; Green, Christopher R.; Farris-Trimble, Ashley W.

2010-01-01

Error patterns in children’s phonological development are often described as simplifying processes that can interact with one another with different consequences. Some interactions limit the applicability of an error pattern, and others extend it to more words. Theories predict that error patterns interact to their full potential. While specific interactions have been documented for certain pairs of processes, no developmental study has shown that the range of typologically predicted interactions occurs for those processes. To determine whether this anomaly is an accidental gap or a systematic peculiarity of particular error patterns, two commonly occurring processes were considered, namely Deaffrication and Consonant Harmony. Results are reported from a cross-sectional and longitudinal study of 12 children (age 3;0 – 5;0) with functional phonological delays. Three interaction types were attested to varying degrees. The longitudinal results further instantiated the typology and revealed a characteristic trajectory of change. Implications of these findings are explored. PMID:20513256

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

EPA Science Inventory

Humans potentially are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Many of these chemicals never have been tested for their ability to interact with the es...

Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

NASA Technical Reports Server (NTRS)

Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

1981-01-01

The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.

PubMed

Sariyar, Murat; Hoffmann, Isabell; Binder, Harald

2014-02-26

Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are established techniques that combine parameter estimation and variable selection. One big challenge is to incorporate interactions into such prediction models. In this feasibility study, we present building blocks for evaluating and incorporating interactions terms in high-dimensional time-to-event settings, especially for settings in which it is computationally too expensive to check all possible interactions. We use a boosting technique for estimation of effects and the following building blocks for pre-selecting interactions: (1) resampling, (2) random forests and (3) orthogonalization as a data pre-processing step. In a simulation study, the strategy that uses all building blocks is able to detect true main effects and interactions with high sensitivity in different kinds of scenarios. The main challenge are interactions composed of variables that do not represent main effects, but our findings are also promising in this regard. Results on real world data illustrate that effect sizes of interactions frequently may not be large enough to improve prediction performance, even though the interactions are potentially of biological relevance. Screening interactions through random forests is feasible and useful, when one is interested in finding relevant two-way interactions. The other building blocks also contribute considerably to an enhanced pre-selection of interactions. We determined the limits of interaction detection in terms of necessary effect sizes. Our study emphasizes the importance of making full use of existing methods in addition to establishing new ones.

Communication Accommodation between Chinese and Australian Students and Academic Staff.

ERIC Educational Resources Information Center

Gallois, Cynthia; And Others

A study tested paths predicted by Communication Accommodation Theory (CAT) in the context of interactions between 105 Chinese and 283 Anglo-Australian students and 98 academic staff in situations of potential conflict. Videotapes of student-lecturer interactions in which speakers accommodated, over-accommodated, or under-accommodated were rated by…

Drought and leaf herbivory influence floral volatiles and pollinator attraction

Treesearch

Laura A. Burkle; Justin B. Runyon

2016-01-01

The effects of climate change on species interactions are poorly understood. Investigating the mechanisms by which species interactions may shift under altered environmental conditions will help form a more predictive understanding of such shifts. In particular, components of climate change have the potential to strongly influence floral volatile organic...

Differential Susceptibility and the Early Development of Aggression: Interactive Effects of Respiratory Sinus Arrhythmia and Environmental Quality

PubMed Central

Eisenberg, Nancy; Sulik, Michael J.; Spinrad, Tracy L.; Edwards, Alison; Eggum, Natalie D.; Liew, Jeffrey; Sallquist, Julie; Popp, Tierney K.; Smith, Cynthia L.; Hart, Daniel

2012-01-01

The purpose of the current study was to predict the development of aggressive behavior from young children’s respiratory sinus arrhythmia (RSA) and environmental quality. In a longitudinal sample of 213 children, baseline RSA, RSA suppression in response to a film of crying babies, and a composite measure of environmental quality (incorporating socioeconomic status and marital adjustment) were measured, and parent-reported aggression was assessed from 18 to 54 months of age. Predictions based on biological sensitivity-to-context/differential susceptibility and diathesis-stress models, as well as potential moderation by child sex, were examined. The interaction of baseline RSA with environmental quality predicted the development (slope) and 54-month intercept of mothers’ reports of aggression. For girls only, the interaction between baseline RSA and environmental quality predicted the 18-month intercept of fathers’ reports. In general, significant negative relations between RSA and aggression were found primarily at high levels of environmental quality. In addition, we found a significant Sex × RSA interaction predicting the slope and 54-month intercept of fathers’ reports of aggression, such that RSA was negatively related to aggression for boys but not for girls. Contrary to predictions, no significant main effects or interactions were found for RSA suppression. The results provide mixed but not full support for differential susceptibility theory and provide little support for the diathesis-stress model. PMID:22182294

Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

PubMed Central

Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

2018-01-01

The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

Reference interaction site model and optimized perturbation theories of colloidal dumbbells with increasing anisotropy

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

Munaò, Gianmarco, E-mail: gmunao@unime.it; Costa, Dino; Caccamo, Carlo

We investigate thermodynamic properties of anisotropic colloidal dumbbells in the frameworks provided by the Reference Interaction Site Model (RISM) theory and an Optimized Perturbation Theory (OPT), this latter based on a fourth-order high-temperature perturbative expansion of the free energy, recently generalized to molecular fluids. Our model is constituted by two identical tangent hard spheres surrounded by square-well attractions with same widths and progressively different depths. Gas-liquid coexistence curves are obtained by predicting pressures, free energies, and chemical potentials. In comparison with previous simulation results, RISM and OPT agree in reproducing the progressive reduction of the gas-liquid phase separation as themore » anisotropy of the interaction potential becomes more pronounced; in particular, the RISM theory provides reasonable predictions for all coexistence curves, bar the strong anisotropy regime, whereas OPT performs generally less well. Both theories predict a linear dependence of the critical temperature on the interaction strength, reproducing in this way the mean-field behavior observed in simulations; the critical density—that drastically drops as the anisotropy increases—turns to be less accurate. Our results appear as a robust benchmark for further theoretical studies, in support to the simulation approach, of self-assembly in model colloidal systems.« less

Inhibition of cytochrome P450 3A by acetoxylated analogues of resveratrol in in vitro and in silico models

NASA Astrophysics Data System (ADS)

Basheer, Loai; Schultz, Keren; Kerem, Zohar

2016-08-01

Many dietary compounds, including resveratrol, are potent inhibitors of CYP3A4. Here we examined the potential to predict inhibition capacity of dietary polyphenolics using an in silico and in vitro approaches and synthetic model compounds. Mono, di, and tri-acetoxy resveratrol were synthesized, a cell line of human intestine origin and microsomes from rat liver served to determine their in vitro inhibition of CYP3A4, and compared to that of resveratrol. Docking simulation served to predict the affinity of the synthetic model compounds to the enzyme. Modelling of the enzyme’s binding site revealed three types of interaction: hydrophobic, electrostatic and H-bonding. The simulation revealed that each of the examined acetylations of resveratrol led to the loss of important interactions of all types. Tri-acetoxy resveratrol was the weakest inhibitor in vitro despite being the more lipophilic and having the highest affinity for the binding site. The simulation demonstrated exclusion of all interactions between tri-acetoxy resveratrol and the heme due to distal binding, highlighting the complexity of the CYP3A4 binding site, which may allow simultaneous accommodation of two molecules. Finally, the use of computational modelling may serve as a quick predictive tool to identify potential harmful interactions between dietary compounds and prescribed drugs.

An analysis for high Reynolds number inviscid/viscid interactions in cascades

NASA Technical Reports Server (NTRS)

Barnett, Mark; Verdon, Joseph M.; Ayer, Timothy C.

1993-01-01

An efficient steady analysis for predicting strong inviscid/viscid interaction phenomena such as viscous-layer separation, shock/boundary-layer interaction, and trailing-edge/near-wake interaction in turbomachinery blade passages is needed as part of a comprehensive analytical blade design prediction system. Such an analysis is described. It uses an inviscid/viscid interaction approach, in which the flow in the outer inviscid region is assumed to be potential, and that in the inner or viscous-layer region is governed by Prandtl's equations. The inviscid solution is determined using an implicit, least-squares, finite-difference approximation, the viscous-layer solution using an inverse, finite-difference, space-marching method which is applied along the blade surfaces and wake streamlines. The inviscid and viscid solutions are coupled using a semi-inverse global iteration procedure, which permits the prediction of boundary-layer separation and other strong-interaction phenomena. Results are presented for three cascades, with a range of inlet flow conditions considered for one of them, including conditions leading to large-scale flow separations. Comparisons with Navier-Stokes solutions and experimental data are also given.

Identifying Drug-Target Interactions with Decision Templates.

PubMed

Yan, Xiao-Ying; Zhang, Shao-Wu

2018-01-01

During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A first principles prediction of the crystal structure of C6Br2ClFH2

NASA Astrophysics Data System (ADS)

Misquitta, Alston J.; Welch, Gareth W. A.; Stone, Anthony J.; Price, Sarah L.

2008-04-01

We have constructed an intermolecular potential for the 1,3-dibromo-2-chloro-5-fluorobenzene molecule from first principles using SAPT(DFT) interaction energy calculations and the Williams-Stone-Misquitta method for obtaining molecular properties in distributed form. This molecule was included in the fourth Blind Test of crystal structure prediction organised by the Cambridge Crystallographic Data Centre. Using our potential, we have predicted the crystal structure of CBrClFH and found the lowest energy solution to be in excellent agreement with the experimentally observed crystal when it was subsequently revealed.

Evaluating and Predicting Patient Safety for Medical Devices With Integral Information Technology

DTIC Science & Technology

2005-01-01

have the potential to become solid tools for manufacturers, purchasers, and consumers to evaluate patient safety issues in various health related...323 Evaluating and Predicting Patient Safety for Medical Devices with Integral Information Technology Jiajie Zhang, Vimla L. Patel, Todd R...errors are due to inappropriate designs for user interactions, rather than mechanical failures. Evaluating and predicting patient safety in medical

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Effective equilibrium states in mixtures of active particles driven by colored noise

NASA Astrophysics Data System (ADS)

Wittmann, René; Brader, J. M.; Sharma, A.; Marconi, U. Marini Bettolo

2018-01-01

We consider the steady-state behavior of pairs of active particles having different persistence times and diffusivities. To this purpose we employ the active Ornstein-Uhlenbeck model, where the particles are driven by colored noises with exponential correlation functions whose intensities and correlation times vary from species to species. By extending Fox's theory to many components, we derive by functional calculus an approximate Fokker-Planck equation for the configurational distribution function of the system. After illustrating the predicted distribution in the solvable case of two particles interacting via a harmonic potential, we consider systems of particles repelling through inverse power-law potentials. We compare the analytic predictions to computer simulations for such soft-repulsive interactions in one dimension and show that at linear order in the persistence times the theory is satisfactory. This work provides the toolbox to qualitatively describe many-body phenomena, such as demixing and depletion, by means of effective pair potentials.

Predicting long-term performance of engineered geologic carbon dioxide storage systems to inform decisions amidst uncertainty

NASA Astrophysics Data System (ADS)

Pawar, R.

2016-12-01

Risk assessment and risk management of engineered geologic CO2 storage systems is an area of active investigation. The potential geologic CO2 storage systems currently under consideration are inherently heterogeneous and have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site's characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. A quantitative methodology for predicting a sequestration site's long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. An integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems can be used to predict long-term performance. The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers). CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc. Answers to such questions are critical in making key risk management decisions. A systematic uncertainty quantification approach can been used to understand how uncertain parameters associated with different subsystems (e.g., reservoir permeability, wellbore cement permeability, wellbore density, etc.) impact the overall site performance predictions.

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Explaining reaction mechanisms using the dual descriptor: a complementary tool to the molecular electrostatic potential.

PubMed

Martínez-Araya, Jorge Ignacio

2013-07-01

The intrinsic reactivity of cyanide when interacting with a silver cation was rationalized using the dual descriptor (DD) as a complement to the molecular electrostatic potential (MEP) in order to predict interactions at the local level. It was found that DD accurately explains covalent interactions that cannot be explained by MEP, which focuses on essentially ionic interactions. This allowed the rationalization of the reaction mechanism that yields silver cyanide in the gas phase. Other similar reaction mechanisms involving a silver cation interacting with water, ammonia, and thiosulfate were also explained by the combination of MEP and DD. This analysis provides another example of the usefulness of DD as a tool for gaining a deeper understanding of any reaction mechanism that is mainly governed by covalent interactions.

An equivalent potential vorticity theory applied to the analysis and prediction of severe storm dynamics

NASA Technical Reports Server (NTRS)

Paine, D. A.; Kaplan, M. L.

1976-01-01

Potential vorticity theory is developed in a description of an equivalent potential temperature topography, and a new theory suited to the description of scale interaction is elaborated. Macroscale triggering of ageostrophic flow fields at the mesoscale, in turn leading to release of convective instability along narrow zones at the microscale, is examined. Correlation of appreciable decrease in potential vorticity with such phenomena as cumulonimbi, tornados, and duststorms is examined. The relevance of a multiscale energy-momentum cascade in numerical prediction of severe mesoscale and microscale phenomena from radiosonde data is reviewed. Hypotheses for mesoscale dynamics are constructed.

Maternal rank influences the outcome of aggressive interactions between immature chimpanzees

PubMed Central

Markham, A. Catherine; Lonsdorf, Elizabeth V.; Pusey, Anne E.; Murray, Carson M.

2015-01-01

For many long-lived mammalian species, extended maternal investment has a profound effect on offspring integration in complex social environments. One component of this investment may be aiding young in aggressive interactions, which can set the stage for offspring social position later in life. Here we examined maternal effects on dyadic aggressive interactions between immature (<12 years) chimpanzees. Specifically, we tested whether relative maternal rank predicted the probability of winning an aggressive interaction. We also examined maternal responses to aggressive interactions to determine whether maternal interventions explain interaction outcomes. Using a 12-year behavioural data set (2000–2011) from Gombe National Park, Tanzania, we found that relative maternal rank predicted the probability of winning aggressive interactions in male–male and male–female aggressive interactions: offspring were more likely to win if their mother outranked their opponent’s mother. Female–female aggressive interactions occurred infrequently (two interactions), so could not be analysed. The probability of winning was also higher for relatively older individuals in male–male interactions, and for males in male–female interactions. Maternal interventions were rare (7.3% of 137 interactions), suggesting that direct involvement does not explain the outcome for the vast majority of aggressive interactions. These findings provide important insight into the ontogeny of aggressive behaviour and early dominance relationships in wild apes and highlight a potential social advantage for offspring of higher-ranking mothers. This advantage may be particularly pronounced for sons, given male philopatry in chimpanzees and the potential for social status early in life to translate more directly to adult rank. PMID:25624528

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

PubMed Central

Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

2013-01-01

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. PMID:24204854

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

PubMed

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Progesterone at Encoding Predicts Subsequent Emotional Memory

ERIC Educational Resources Information Center

Ertman, Nicole; Andreano, Joseph M.; Cahill, Larry

2011-01-01

Significant sex differences in the well-documented relationship between stress hormones and memory have emerged in recent studies. The potentiating effects of glucocorticoids on memory vary across the menstrual cycle, suggesting a potential interaction between these stress hormones and endogenously cycling sex hormones. Here, we show that memory…

Predicting radiotherapy outcomes using statistical learning techniques

NASA Astrophysics Data System (ADS)

El Naqa, Issam; Bradley, Jeffrey D.; Lindsay, Patricia E.; Hope, Andrew J.; Deasy, Joseph O.

2009-09-01

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data. Part of this work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

Thermodynamic and Structural Properties of Methanol-Water Solutions Using Non-Additive Interaction Models

PubMed Central

Zhong, Yang; Warren, G. Lee; Patel, Sandeep

2014-01-01

We study bulk structural and thermodynamic properties of methanol-water solutions via molecular dynamics simulations using novel interaction potentials based on the charge equilibration (fluctuating charge) formalism to explicitly account for molecular polarization at the atomic level. The study uses the TIP4P-FQ potential for water-water interactions, and the CHARMM-based (Chemistry at HARvard Molecular Mechanics) fluctuating charge potential for methanol-methanol and methanol-water interactions. In terms of bulk solution properties, we discuss liquid densities, enthalpies of mixing, dielectric constants, self-diffusion constants, as well as structural properties related to local hydrogen bonding structure as manifested in radial distribution functions and cluster analysis. We further explore the electronic response of water and methanol in the differing local environments established by the interaction of each species predominantly with molecules of the other species. The current force field for the alcohol-water interaction performs reasonably well for most properties, with the greatest deviation from experiment observed for the excess mixing enthalpies, which are predicted to be too favorable. This is qualitatively consistent with the overestimation of the methanol-water gas-phase interaction energy for the lowest-energy conformer (methanol as proton donor). Hydration free energies for methanol in TIP4P-FQ water are predicted to be −5.6±0.2 kcal/mole, in respectable agreement with the experimental value of −5.1 kcal/mole. With respect to solution micro-structure, the present cluster analysis suggests that the micro-scale environment for concentrations where select thermodynamic quantities reach extremal values is described by a bi-percolating network structure. PMID:18074339

Restricted mobility of side chains on concave surfaces of solenoid proteins may impart heightened potential for intermolecular interactions.

PubMed

Ramya, L; Gautham, N; Chaloin, Laurent; Kajava, Andrey V

2015-09-01

Significant progress has been made in the determination of the protein structures with their number today passing over a hundred thousand structures. The next challenge is the understanding and prediction of protein-protein and protein-ligand interactions. In this work we address this problem by analyzing curved solenoid proteins. Many of these proteins are considered as "hub molecules" for their high potential to interact with many different molecules and to be a scaffold for multisubunit protein machineries. Our analysis of these structures through molecular dynamics simulations reveals that the mobility of the side-chains on the concave surfaces of the solenoids is lower than on the convex ones. This result provides an explanation to the observed preferential binding of the ligands, including small and flexible ligands, to the concave surface of the curved solenoid proteins. The relationship between the landscapes and dynamic properties of the protein surfaces can be further generalized to the other types of protein structures and eventually used in the computer algorithms, allowing prediction of protein-ligand interactions by analysis of protein surfaces. © 2015 Wiley Periodicals, Inc.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

SIMPLE estimate of the free energy change due to aliphatic mutations: superior predictions based on first principles.

PubMed

Bueno, Marta; Camacho, Carlos J; Sancho, Javier

2007-09-01

The bioinformatics revolution of the last decade has been instrumental in the development of empirical potentials to quantitatively estimate protein interactions for modeling and design. Although computationally efficient, these potentials hide most of the relevant thermodynamics in 5-to-40 parameters that are fitted against a large experimental database. Here, we revisit this longstanding problem and show that a careful consideration of the change in hydrophobicity, electrostatics, and configurational entropy between the folded and unfolded state of aliphatic point mutations predicts 20-30% less false positives and yields more accurate predictions than any published empirical energy function. This significant improvement is achieved with essentially no free parameters, validating past theoretical and experimental efforts to understand the thermodynamics of protein folding. Our first principle analysis strongly suggests that both the solute-solute van der Waals interactions in the folded state and the electrostatics free energy change of exposed aliphatic mutations are almost completely compensated by similar interactions operating in the unfolded ensemble. Not surprisingly, the problem of properly accounting for the solvent contribution to the free energy of polar and charged group mutations, as well as of mutations that disrupt the protein backbone remains open. 2007 Wiley-Liss, Inc.

Understanding social anxiety as a risk for alcohol use disorders: Fear of scrutiny, not social interaction fears, prospectively predicts alcohol use disorders

PubMed Central

Buckner, Julia D.; Schmidt, Norman B.

2009-01-01

Increasing evidence indicates that social anxiety may be a premorbid risk factor for alcohol use disorders (AUD). The aim of this study was to replicate and extend previous work examining whether social anxiety is a risk factor for AUD by evaluating both the temporal antecedence and non-spuriousness of this relationship. We also examined whether social anxiety first-order factors (social interaction anxiety, observation anxieties) served as specific predictors of AUD. A non-referred sample of 404 psychologically healthy young adults (i.e. free from current or past Axis I psychopathology) was prospectively followed over approximately two years. Social anxiety (but not depression or trait anxiety) at baseline significantly predicted subsequent AUD onset. The relationship between social anxiety and AUD remained even after controlling for relevant variables (gender, depression, trait anxiety). Further, social anxiety first-order factors differentially predicted AUD onset, such that observation anxieties (but not social interaction anxiety) were prospectively linked to AUD onset. This study provides further support that social anxiety (and fear of scrutiny specifically) appears to serve as an important and potentially specific AUD-related variable that deserves serious attention as a potential vulnerability factor. PMID:18547587

Molecular dynamics, flexible docking, virtual screening, ADMET predictions, and molecular interaction field studies to design novel potential MAO-B inhibitors.

PubMed

Braun, Glaucia H; Jorge, Daniel M M; Ramos, Henrique P; Alves, Raquel M; da Silva, Vinicius B; Giuliatti, Silvana; Sampaio, Suley Vilela; Taft, Carlton A; Silva, Carlos H T P

2008-02-01

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson's disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

Asymmetric specialization and extinction risk in plant-flower visitor webs: a matter of morphology or abundance?

PubMed

Stang, Martina; Klinkhamer, Peter G L; van der Meijden, Eddy

2007-03-01

A recently discovered feature of plant-flower visitor webs is the asymmetric specialization of the interaction partners: specialized plants interact mainly with generalized flower visitors and specialized flower visitors mainly with generalized plants. Little is known about the factors leading to this asymmetry and their consequences for the extinction risk of species. Previous studies have proposed random interactions proportional to species abundance as an explanation. However, the simulation models used in these studies did not include potential biological constraints. In the present study, we tested the potential role of both morphological constraints and species abundance in promoting asymmetric specialization. We compared actual field data of a Mediterranean plant-flower visitor web with predictions of Monte Carlo simulations including different combinations of the potential factors structuring the web. Our simulations showed that both nectar-holder depth and abundance were able to produce asymmetry; but that the expected degree of asymmetry was stronger if based on both. Both factors can predict the number of interaction partners, but only nectar-holder depth was able to predict the degree of asymmetry of a certain species. What is more, without the size threshold the influence of abundance would disappear over time. Thus, asymmetric specialization seems to be the result of a size threshold and, only among the allowed interactions above this size threshold, a result of random interactions proportional to abundance. The simulations also showed that asymmetric specialization could not be the reason that the extinction risk of specialists and generalists is equalized, as suggested in the literature. In asymmetric webs specialists clearly had higher short-term extinction risks. In fact, primarily generalist visitors seem to profit from asymmetric specialization. In our web, specialists were less abundant than generalists. Therefore, including abundance in the simulation models increased the difference between specialists and generalists even more.

Prediction of peer-rated adult hostility from autonomy struggles in adolescent–family interactions

PubMed Central

ALLEN, JOSEPH P.; HAUSER, STUART T.; O’CONNOR, THOMAS G.; BELL, KATHY L.

2006-01-01

Observed parent–adolescent autonomy struggles were assessed as potential predictors of the development of peer-rated hostility over a decade later in young adulthood in both normal and previously psychiatrically hospitalized groups of adolescents. Longitudinal, multireporter data were obtained by coding family interactions involving 83 adolescents and their parents at age 16 years and then obtaining ratings by close friends of adolescents’ hostility at age 25 years. Fathers’ behavior undermining adolescents’ autonomy in interactions at age 16 years were predictive of adolescents-as-young-adults’ hostility, as rated by close friends at age 25 years. These predictions contributed additional variance to understanding young adult hostility even after accounting for concurrent levels of adolescent hostility at age 16 years and paternal hostility at this age, each of which also significantly contributed to predicting future hostility. Results are discussed as highlighting a pathway by which difficulties attaining autonomy in adolescence may presage the development of long-term difficulties in social functioning. PMID:11893089

Invited commentary: on population subgroups, mathematics, and interventions.

PubMed

Jacobs, David R; Meyer, Katie A

2011-02-15

New sex-specific equations, each with race/ethnic-specific intercept, for predicted lung function illustrate a methodological point, that complex differences between groups may not imply interactions with other predictors, such as age and height. The new equations find that race/ethnic identity does not interact with either age or height in the prediction equations, although there are race/ethnic-specific offsets. Further study is warranted of the effect of possible small race/ethnic interactions on disease classification. Additional study of repeated measures of lung function is warranted, given that the new equations were developed in cross-sectional designs. Predicting lung function is more than a methodological exercise. Predicted values are important in disease diagnosis and monitoring. It is suggested that measurement and tracking of lung function throughout young adulthood could be used to provide an early warning of potential long-term lung function losses to encourage improvement of risky behaviors including smoking and failure to maintain normal body weight in the general population.

Evaluating multiple determinants of the structure of plant-animal mutualistic networks.

PubMed

Vázquez, Diego P; Chacoff, Natacha P; Cagnolo, Luciano

2009-08-01

The structure of mutualistic networks is likely to result from the simultaneous influence of neutrality and the constraints imposed by complementarity in species phenotypes, phenologies, spatial distributions, phylogenetic relationships, and sampling artifacts. We develop a conceptual and methodological framework to evaluate the relative contributions of these potential determinants. Applying this approach to the analysis of a plant-pollinator network, we show that information on relative abundance and phenology suffices to predict several aggregate network properties (connectance, nestedness, interaction evenness, and interaction asymmetry). However, such information falls short of predicting the detailed network structure (the frequency of pairwise interactions), leaving a large amount of variation unexplained. Taken together, our results suggest that both relative species abundance and complementarity in spatiotemporal distribution contribute substantially to generate observed network patters, but that this information is by no means sufficient to predict the occurrence and frequency of pairwise interactions. Future studies could use our methodological framework to evaluate the generality of our findings in a representative sample of study systems with contrasting ecological conditions.

Novel nonlinear knowledge-based mean force potentials based on machine learning.

PubMed

Dong, Qiwen; Zhou, Shuigeng

2011-01-01

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.

Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

DTIC Science & Technology

2016-08-27

acted to inhibit both TAK1 and MEK. Experimental data for these prediction tests are shown in Figure 4, and comparison between predictions and valida...would decrease did not contain this interaction. The fact that phospho-cJun did decrease in the experimental test of this prediction (Figure 4...pathways primarily through TAK1. Does IL-1 signal through MEKK1 in HepG2 cells? Given the potential importance of MEKK1, we experimentally tested whether IL

In vitro and physiologically‐based pharmacokinetic based assessment of drug–drug interaction potential of canagliflozin

PubMed Central

Dallas, Shannon; Sensenhauser, Carlo; Lim, Heng Keang; Scheers, Ellen; Verboven, Peter; Cuyckens, Filip; Leclercq, Laurent; Evans, David C.; Kelley, Michael F.; Johnson, Mark D.; Snoeys, Jan

2016-01-01

Aims Canagliflozin is a recently approved drug for use in the treatment of type 2 diabetes. The potential for canagliflozin to cause clinical drug–drug interactions (DDIs) was assessed. Methods DDI potential of canagliflozin was investigated using in vitro test systems containing drug metabolizing enzymes or transporters. Basic predictive approaches were applied to determine potential interactions in vivo. A physiologically‐based pharmacokinetic (PBPK) model was developed and clinical DDI simulations were performed to determine the likelihood of cytochrome P450 (CYP) inhibition by canagliflozin. Results Canagliflozin was primarily metabolized by uridine 5′‐diphospho‐glucuronosyltransferase 1A9 and 2B4 enzymes. Canagliflozin was a substrate of efflux transporters (P‐glycoprotein, breast cancer resistance protein and multidrug resistance‐associated protein‐2) but was not a substrate of uptake transporters (organic anion transporter polypeptide isoforms OATP1B1, OATP1B3, organic anion transporters OAT1 and OAT3, and organic cationic transporters OCT1, and OCT2). In inhibition assays, canagliflozin was shown to be a weak in vitro inhibitor (IC50) of CYP3A4 (27 μmol l –1, standard error [SE] 4.9), CYP2C9 (80 μmol l –1, SE 8.1), CYP2B6 (16 μmol l–1, SE 2.1), CYP2C8 (75 μmol l –1, SE 6.4), P‐glycoprotein (19.3 μmol l –1, SE 7.2), and multidrug resistance‐associated protein‐2 (21.5 μmol l –1, SE 3.1). Basic models recommended in DDI guidelines (US Food & Drug Administration and European Medicines Agency) predicted moderate to low likelihood of interaction for these CYPs and efflux transporters. PBPK DDI simulations of canagliflozin with CYP probe substrates (simvastatin, S‐warfarin, bupropion, repaglinide) did not show relevant interaction in humans since mean areas under the concentration‐time curve and maximum plasma concentration ratios for probe substrates with and without canagliflozin and its 95% CIs were within 0.80–1.25. Conclusions In vitro DDI followed by a predictive or PBPK approach was applied to determine DDI potential of canagliflozin. Overall, canagliflozin is neither a perpetrator nor a victim of clinically important interactions. PMID:27862160

RACER a Coarse-Grained RNA Model for Capturing Folding Free Energy in Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Cheng, Sara; Bell, David; Ren, Pengyu

RACER is a coarse-grained RNA model that can be used in molecular dynamics simulations to predict native structures and sequence-specific variation of free energy of various RNA structures. RACER is capable of accurate prediction of native structures of duplexes and hairpins (average RMSD of 4.15 angstroms), and RACER can capture sequence-specific variation of free energy in excellent agreement with experimentally measured stabilities (r-squared =0.98). The RACER model implements a new effective non-bonded potential and re-parameterization of hydrogen bond and Debye-Huckel potentials. Insights from the RACER model include the importance of treating pairing and stacking interactions separately in order to distinguish folded an unfolded states and identification of hydrogen-bonding, base stacking, and electrostatic interactions as essential driving forces for RNA folding. Future applications of the RACER model include predicting free energy landscapes of more complex RNA structures and use of RACER for multiscale simulations.

Inhibition of cytochrome P450 3A by acetoxylated analogues of resveratrol in in vitro and in silico models

PubMed Central

Basheer, Loai; Schultz, Keren; Kerem, Zohar

2016-01-01

Many dietary compounds, including resveratrol, are potent inhibitors of CYP3A4. Here we examined the potential to predict inhibition capacity of dietary polyphenolics using an in silico and in vitro approaches and synthetic model compounds. Mono, di, and tri-acetoxy resveratrol were synthesized, a cell line of human intestine origin and microsomes from rat liver served to determine their in vitro inhibition of CYP3A4, and compared to that of resveratrol. Docking simulation served to predict the affinity of the synthetic model compounds to the enzyme. Modelling of the enzyme’s binding site revealed three types of interaction: hydrophobic, electrostatic and H-bonding. The simulation revealed that each of the examined acetylations of resveratrol led to the loss of important interactions of all types. Tri-acetoxy resveratrol was the weakest inhibitor in vitro despite being the more lipophilic and having the highest affinity for the binding site. The simulation demonstrated exclusion of all interactions between tri-acetoxy resveratrol and the heme due to distal binding, highlighting the complexity of the CYP3A4 binding site, which may allow simultaneous accommodation of two molecules. Finally, the use of computational modelling may serve as a quick predictive tool to identify potential harmful interactions between dietary compounds and prescribed drugs. PMID:27530542

Interactions among ecosystem stressors and their importance in conservation

PubMed Central

Darling, Emily S.; Brown, Christopher J.

2016-01-01

Interactions between multiple ecosystem stressors are expected to jeopardize biological processes, functions and biodiversity. The scientific community has declared stressor interactions—notably synergies—a key issue for conservation and management. Here, we review ecological literature over the past four decades to evaluate trends in the reporting of ecological interactions (synergies, antagonisms and additive effects) and highlight the implications and importance to conservation. Despite increasing popularity, and ever-finer terminologies, we find that synergies are (still) not the most prevalent type of interaction, and that conservation practitioners need to appreciate and manage for all interaction outcomes, including antagonistic and additive effects. However, it will not be possible to identify the effect of every interaction on every organism's physiology and every ecosystem function because the number of stressors, and their potential interactions, are growing rapidly. Predicting the type of interactions may be possible in the near-future, using meta-analyses, conservation-oriented experiments and adaptive monitoring. Pending a general framework for predicting interactions, conservation management should enact interventions that are robust to uncertainty in interaction type and that continue to bolster biological resilience in a stressful world. PMID:26865306

Pairwise contact energy statistical potentials can help to find probability of point mutations.

PubMed

Saravanan, K M; Suvaithenamudhan, S; Parthasarathy, S; Selvaraj, S

2017-01-01

To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β) 8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than i Mutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Young adults' internet addiction: Prediction by the interaction of parental marital conflict and respiratory sinus arrhythmia.

PubMed

Zhang, Hui; Spinrad, Tracy L; Eisenberg, Nancy; Luo, Yun; Wang, Zhenhong

2017-10-01

The aim of the current study was to address the potential moderating roles of respiratory sinus arrhythmia (RSA; baseline and suppression) and participant sex in the relation between parents' marital conflict and young adults' internet addiction. Participants included 105 (65 men) Chinese young adults who reported on their internet addiction and their parents' marital conflict. Marital conflict interacted with RSA suppression to predict internet addiction. Specifically, high RSA suppression was associated with low internet addiction, regardless of parental marital conflict; however, for participants with low RSA suppression, a positive relation between marital conflict and internet addiction was found. Internet addiction also was predicted by a significant three-way interaction among baseline RSA, marital conflict, and participant sex. Specifically, for men, marital conflict positively predicted internet addiction under conditions of low (but not high) baseline RSA. For women, marital conflict positively predicted internet addiction under conditions of high (but not low) baseline RSA. Findings highlight the importance of simultaneous consideration of physiological factors, in conjunction with family factors, in the prediction of young adults' internet addiction. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermodynamic model of a solid with RKKY interaction and magnetoelastic coupling

NASA Astrophysics Data System (ADS)

Balcerzak, T.; Szałowski, K.; Jaščur, M.

2018-04-01

Thermodynamic description of a model system with magnetoelastic coupling is presented. The elastic, vibrational, electronic and magnetic energy contributions are taken into account. The long-range RKKY interaction is considered together with the nearest-neighbour direct exchange. The generalized Gibbs potential and the set of equations of state are derived, from which all thermodynamic functions are self-consistently obtained. Thermodynamic properties are calculated numerically for FCC structure for arbitrary external pressure, magnetic field and temperature, and widely discussed. In particular, for some parameters of interaction potential and electron concentration corresponding to antiferromagnetic phase, the existence of negative thermal expansion coefficient is predicted.

Prediction of Ras-effector interactions using position energy matrices.

PubMed

Kiel, Christina; Serrano, Luis

2007-09-01

One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences. Based on position energy matrices, the sequences of putative Ras-binding domains can be scanned quickly to calculate an energy sum value. By calibrating energy sum values using quantitative experimental binding data, thresholds can be defined and thus non-binding domains can be excluded quickly. Sequences which have energy sum values above this threshold are considered to be potential binding domains, and could be further analysed using homology modelling. This prediction method could be applied to other protein families sharing conserved interaction types, in order to determine in a fast way large scale cellular protein interaction networks. Thus, it could have an important impact on future in silico structural genomics approaches, in particular with regard to increasing structural proteomics efforts, aiming to determine all possible domain folds and interaction types. All matrices are deposited in the ADAN database (http://adan-embl.ibmc.umh.es/). Supplementary data are available at Bioinformatics online.

Spacecraft-environment interaction model cross comparison applied to Solar Probe Plus

NASA Astrophysics Data System (ADS)

Lapenta, G.; Deca, J.; Markidis, S.; Marchand, R.; Guillemant, S.; Matéo Vélez, J.; Miyake, Y.; Usui, H.; Ergun, R.; Sturner, A. P.

2013-12-01

Given that our society becomes increasingly dependent on space technology, it is imperative to develop a good understanding of spacecraft-plasma interactions. Two main issues are important. First, one needs to be able to design a reliable spacecraft that can survive in the harsh solar wind conditions, and second a very good knowledge of the behaviour and plasma structure around the spacecraft is required to be able to interpret and correct measurements from onboard instruments and science experiments. In this work we present the results of a cross-comparison study between five spacecraft-plasma models (EMSES, iPic3D, LASP, PTetra, SPIS) used to simulate the interaction of the Solar Probe Plus (SPP) satellite with the space environment under representative solar wind conditions near perihelion. The purpose of this cross-comparison is to assess the consistency and validity of the different numerical approaches from the similarities and differences of their predictions under well defined conditions, with attention to the implicit PIC code iPic3D, which has never been used for spacecraft-environment interaction studies before. The physical effects considered are spacecraft charging, photoelectron and secondary electron emission, the presence of a background magnetic field and density variations. The latter of which can cause the floating potential of SPP to go from negative to positive or visa versa, depending on the solar wind conditions, and spacecraft material properties. Simulation results are presented and compared with increasing levels of complexity in the physics to evaluate the sensitivity of the model predictions to certain physical effects. The comparisons focus particularly on spacecraft floating potential, detailed contributions to the currents collected and emitted by the spacecraft, and on the potential and density spatial profiles near the satellite. Model predictions obtained with our different computational approaches are found to be in good agreement when the physical processes are treated similarly. The comparisons considered here indicate that, with the correct parameterization of important physical effects such as photoemission and secondary electron emission, our simulation models should have the required skill to predict details of satellite-plasma interaction physics with a high level of confidence. This work was supported by the International Space Science Institute in Bern Switzerland. The potential profile around the Solar Probe Plus spacecraft in orbital flow, from the iPic3D code. The physical model includes photo- and secondary electrons and a static magnetic field.

Effector prediction in host-pathogen interaction based on a Markov model of a ubiquitous EPIYA motif

PubMed Central

2010-01-01

Background Effector secretion is a common strategy of pathogen in mediating host-pathogen interaction. Eight EPIYA-motif containing effectors have recently been discovered in six pathogens. Once these effectors enter host cells through type III/IV secretion systems (T3SS/T4SS), tyrosine in the EPIYA motif is phosphorylated, which triggers effectors binding other proteins to manipulate host-cell functions. The objectives of this study are to evaluate the distribution pattern of EPIYA motif in broad biological species, to predict potential effectors with EPIYA motif, and to suggest roles and biological functions of potential effectors in host-pathogen interactions. Results A hidden Markov model (HMM) of five amino acids was built for the EPIYA-motif based on the eight known effectors. Using this HMM to search the non-redundant protein database containing 9,216,047 sequences, we obtained 107,231 sequences with at least one EPIYA motif occurrence and 3115 sequences with multiple repeats of the EPIYA motif. Although the EPIYA motif exists among broad species, it is significantly over-represented in some particular groups of species. For those proteins containing at least four copies of EPIYA motif, most of them are from intracellular bacteria, extracellular bacteria with T3SS or T4SS or intracellular protozoan parasites. By combining the EPIYA motif and the adjacent SH2 binding motifs (KK, R4, Tarp and Tir), we built HMMs of nine amino acids and predicted many potential effectors in bacteria and protista by the HMMs. Some potential effectors for pathogens (such as Lawsonia intracellularis, Plasmodium falciparum and Leishmania major) are suggested. Conclusions Our study indicates that the EPIYA motif may be a ubiquitous functional site for effectors that play an important pathogenicity role in mediating host-pathogen interactions. We suggest that some intracellular protozoan parasites could secrete EPIYA-motif containing effectors through secretion systems similar to the T3SS/T4SS in bacteria. Our predicted effectors provide useful hypotheses for further studies. PMID:21143776

3D RISM theory with fast reciprocal-space electrostatics.

PubMed

Heil, Jochen; Kast, Stefan M

2015-03-21

The calculation of electrostatic solute-solvent interactions in 3D RISM ("three-dimensional reference interaction site model") integral equation theory is recast in a form that allows for a computational treatment analogous to the "particle-mesh Ewald" formalism as used for molecular simulations. In addition, relations that connect 3D RISM correlation functions and interaction potentials with thermodynamic quantities such as the chemical potential and average solute-solvent interaction energy are reformulated in a way that calculations of expensive real-space electrostatic terms on the 3D grid are completely avoided. These methodical enhancements allow for both, a significant speedup particularly for large solute systems and a smoother convergence of predicted thermodynamic quantities with respect to box size, as illustrated for several benchmark systems.

Analysis of the interactions between He + ions and transition metal surfaces using co-axial impact collision ion scattering spectroscopy

NASA Astrophysics Data System (ADS)

Walker, M.; Brown, M. G.; Draxler, M.; Fishwick, L.; Dowsett, M. G.; McConville, C. F.

2011-01-01

The interactions between low energy He + ions and a series of transition metal surfaces have been studied using co-axial impact collision ion scattering spectroscopy (CAICISS). Experimental data were collected from the Ni(110), Cu(100), Pd(111), Pt(111) and Au(111) surfaces using ion beams with primary energies between 1.5 keV and 4.0 keV. The shadow cone radii deduced from the experimental surface peak positions were found to closely match theoretical predictions. Data analysis was performed using both the FAN and Kalypso simulation codes, revealing a consistent requirement for a reduction of 0.252 in the screening length correction in the Molière approximation within the Thomas-Fermi (TFM) interaction potential. The adjustments of the screening length in the TFM potential, predicted by O'Connor, and the uncorrected Ziegler-Biersack-Littmark (ZBL) potential both yielded inaccurate results for all of the surfaces and incident energies studied. We also provide evidence that, despite their different computational methodologies, the FAN and Kalypso simulation codes generate similar results given identical input parameters for the analysis of 180° backscattering spectra.

Introductory lecture: interpreting and predicting Hofmeister salt ion and solute effects on biopolymer and model processes using the solute partitioning model.

PubMed

Record, M Thomas; Guinn, Emily; Pegram, Laurel; Capp, Michael

2013-01-01

Understanding how Hofmeister salt ions and other solutes interact with proteins, nucleic acids, other biopolymers and water and thereby affect protein and nucleic acid processes as well as model processes (e.g. solubility of model compounds) in aqueous solution is a longstanding goal of biophysical research. Empirical Hofmeister salt and solute "m-values" (derivatives of the observed standard free energy change for a model or biopolymer process with respect to solute or salt concentration m3) are equal to differences in chemical potential derivatives: m-value = delta(dmu2/dm3) = delta mu23, which quantify the preferential interactions of the solute or salt with the surface of the biopolymer or model system (component 2) exposed or buried in the process. Using the solute partitioning model (SPM), we dissect mu23 values for interactions of a solute or Hofmeister salt with a set of model compounds displaying the key functional groups of biopolymers to obtain interaction potentials (called alpha-values) that quantify the interaction of the solute or salt per unit area of each functional group or type of surface. Interpreted using the SPM, these alpha-values provide quantitative information about both the hydration of functional groups and the competitive interaction of water and the solute or salt with functional groups. The analysis corroborates and quantifies previous proposals that the Hofmeister anion and cation series for biopolymer processes are determined by ion-specific, mostly unfavorable interactions with hydrocarbon surfaces; the balance between these unfavorable nonpolar interactions and often-favorable interactions of ions with polar functional groups determine the series null points. The placement of urea and glycine betaine (GB) at opposite ends of the corresponding series of nonelectrolytes results from the favorable interactions of urea, and unfavorable interactions of GB, with many (but not all) biopolymer functional groups. Interaction potentials and local-bulk partition coefficients quantifying the distribution of solutes (e.g. urea, glycine betaine) and Hofmeister salt ions in the vicinity of each functional group make good chemical sense when interpreted in terms of competitive noncovalent interactions. These interaction potentials allow solute and Hofmeister (noncoulombic) salt effects on protein and nucleic acid processes to be interpreted or predicted, and allow the use of solutes and salts as probes of

Improving compound-protein interaction prediction by building up highly credible negative samples.

PubMed

Liu, Hui; Sun, Jianjiang; Guan, Jihong; Zheng, Jie; Zhou, Shuigeng

2015-06-15

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods. This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein-protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans. We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound-protein databases. Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/. © The Author 2015. Published by Oxford University Press.

A Computational Approach to Finding Novel Targets for Existing Drugs

PubMed Central

Li, Yvonne Y.; An, Jianghong; Jones, Steven J. M.

2011-01-01

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. PMID:21909252

Discovery of multiple interacting partners of gankyrin, a proteasomal chaperone and an oncoprotein--evidence for a common hot spot site at the interface and its functional relevance.

PubMed

Nanaware, Padma P; Ramteke, Manoj P; Somavarapu, Arun K; Venkatraman, Prasanna

2014-07-01

Gankyrin, a non-ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin-proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line- MDA-MB-435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA-MB-231 cells in which the endogenous CLIC1 is silenced, trans-expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein-protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. © 2013 Wiley Periodicals, Inc.

Potential for the dynamics of pedestrians in a socially interacting group

NASA Astrophysics Data System (ADS)

Zanlungo, Francesco; Ikeda, Tetsushi; Kanda, Takayuki

2014-01-01

We introduce a simple potential to describe the dynamics of the relative motion of two pedestrians socially interacting in a walking group. We show that the proposed potential, based on basic empirical observations and theoretical considerations, can qualitatively describe the statistical properties of pedestrian behavior. In detail, we show that the two-dimensional probability distribution of the relative distance is determined by the proposed potential through a Boltzmann distribution. After calibrating the parameters of the model on the two-pedestrian group data, we apply the model to three-pedestrian groups, showing that it describes qualitatively and quantitatively well their behavior. In particular, the model predicts that three-pedestrian groups walk in a V-shaped formation and provides accurate values for the position of the three pedestrians. Furthermore, the model correctly predicts the average walking velocity of three-person groups based on the velocity of two-person ones. Possible extensions to larger groups, along with alternative explanations of the social dynamics that may be implied by our model, are discussed at the end of the paper.

Whole Protein Native Fitness Potentials

NASA Astrophysics Data System (ADS)

Faraggi, Eshel; Kloczkowski, Andrzej

2013-03-01

Protein structure prediction can be separated into two tasks: sample the configuration space of the protein chain, and assign a fitness between these hypothetical models and the native structure of the protein. One of the more promising developments in this area is that of knowledge based energy functions. However, standard approaches using pair-wise interactions have shown shortcomings demonstrated by the superiority of multi-body-potentials. These shortcomings are due to residue pair-wise interaction being dependent on other residues along the chain. We developed a method that uses whole protein information filtered through machine learners to score protein models based on their likeness to native structures. For all models we calculated parameters associated with the distance to the solvent and with distances between residues. These parameters, in addition to energy estimates obtained by using a four-body-potential, DFIRE, and RWPlus were used as training for machine learners to predict the fitness of the models. Testing on CASP 9 targets showed that our method is superior to DFIRE, RWPlus, and the four-body potential, which are considered standards in the field.

Genetic variability of VEGF pathway genes in six randomized phase III trials assessing the addition of bevacizumab to standard therapy.

PubMed

de Haas, Sanne; Delmar, Paul; Bansal, Aruna T; Moisse, Matthieu; Miles, David W; Leighl, Natasha; Escudier, Bernard; Van Cutsem, Eric; Carmeliet, Peter; Scherer, Stefan J; Pallaud, Celine; Lambrechts, Diether

2014-10-01

Despite extensive translational research, no validated biomarkers predictive of bevacizumab treatment outcome have been identified. We performed a meta-analysis of individual patient data from six randomized phase III trials in colorectal, pancreatic, lung, renal, breast, and gastric cancer to explore the potential relationships between 195 common genetic variants in the vascular endothelial growth factor (VEGF) pathway and bevacizumab treatment outcome. The analysis included 1,402 patients (716 bevacizumab-treated and 686 placebo-treated). Twenty variants were associated (P < 0.05) with progression-free survival (PFS) in bevacizumab-treated patients. Of these, 4 variants in EPAS1 survived correction for multiple testing (q < 0.05). Genotype-by-treatment interaction tests revealed that, across these 20 variants, 3 variants in VEGF-C (rs12510099), EPAS1 (rs4953344), and IL8RA (rs2234671) were potentially predictive (P < 0.05), but not resistant to multiple testing (q > 0.05). A weak genotype-by-treatment interaction effect was also observed for rs699946 in VEGF-A, whereas Bayesian genewise analysis revealed that genetic variability in VHL was associated with PFS in the bevacizumab arm (q < 0.05). Variants in VEGF-A, EPAS1, and VHL were located in expression quantitative loci derived from lymphoblastoid cell lines, indicating that they affect the expression levels of their respective gene. This large genetic analysis suggests that variants in VEGF-A, EPAS1, IL8RA, VHL, and VEGF-C have potential value in predicting bevacizumab treatment outcome across tumor types. Although these associations did not survive correction for multiple testing in a genotype-by-interaction analysis, they are among the strongest predictive effects reported to date for genetic variants and bevacizumab efficacy.

Understanding Physical Activity through Interactions Between the Built Environment and Social Cognition: A Systematic Review.

PubMed

Rhodes, Ryan E; Saelens, Brian E; Sauvage-Mar, Claire

2018-05-16

Few people in most developed nations engage in regular physical activity (PA), despite its well-established health benefits. Socioecological models highlight the potential interaction of multiple factors from policy and the built environment to individual social cognition in explaining PA. The purpose of this review was to appraise this interaction tenet of the socioecological model between the built environment and social cognition to predict PA. Eligible studies had to have been published in peer-reviewed journals in the English language, and included any tests of interaction between social cognition and the built environment with PA. Literature searches, concluded in October 2017, used five common databases. Findings were grouped by type of PA outcomes (leisure, transportation, total PA and total moderate-vigorous PA [MVPA]), then grouped by the type of interactions between social cognitive and built environment constructs. The initial search yielded 308 hits, which was reduced to 22 independent studies of primarily high- to medium-quality after screening for eligibility criteria. The interaction tenet of the socioecological model was not supported for overall MVPA and total PA. By contrast, while there was heterogeneity of findings for leisure-time PA, environmental accessibility/convenience interacted with intention, and environmental aesthetics interacted with affective judgments, to predict leisure-time PA. Interactions between the built environment and social cognition in PA for transport are limited, with current results failing to support an effect. The results provide some support for interactive aspects of the built environment and social cognition in leisure-time PA, and thus highlight potential areas for integrated intervention of individual and environmental change.

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France

PubMed Central

Meersmans, Jeroen; Arrouays, Dominique; Van Rompaey, Anton J. J.; Pagé, Christian; De Baets, Sarah; Quine, Timothy A.

2016-01-01

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO2 emissions will be crucial to prevent further loss of carbon from our soils. PMID:27808169

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France.

PubMed

Meersmans, Jeroen; Arrouays, Dominique; Van Rompaey, Anton J J; Pagé, Christian; De Baets, Sarah; Quine, Timothy A

2016-11-03

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO 2 emissions will be crucial to prevent further loss of carbon from our soils.

Molecular design of new aggrecanases-2 inhibitors.

PubMed

Shan, Zhi Jie; Zhai, Hong Lin; Huang, Xiao Yan; Li, Li Na; Zhang, Xiao Yun

2013-10-01

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications. Copyright © 2013 Elsevier Ltd. All rights reserved.

Imaging carbon nanotube interactions, diffusion, and stability in nanopores.

PubMed

Eichmann, Shannon L; Smith, Billy; Meric, Gulsum; Fairbrother, D Howard; Bevan, Michael A

2011-07-26

We report optical microscopy measurements of three-dimensional trajectories of individual multiwalled carbon nanotubes (MWCNTs) in nanoscale silica slit pores. Trajectories are analyzed to nonintrusively measure MWCNT interactions, diffusion, and stability as a function of pH and ionic strength. Evanescent wave scattering is used to track MWCNT positions normal to pore walls with nanometer-scale resolution, and video microscopy is used to track lateral positions with spatial resolution comparable to the diffraction limit. Analysis of MWCNT excursions normal to pore walls yields particle-wall potentials that agree with theoretical electrostatic and van der Waals potentials assuming a rotationally averaged potential of mean force. MWCNT lateral mean square displacements are used to quantify translational diffusivities, which are comparable to predictions based on the best available theories. Finally, measured MWCNT pH and ionic strength dependent stabilities are in excellent agreement with predictions. Our findings demonstrate novel measurement and modeling tools to understand the behavior of confined MWCNTs relevant to a broad range of applications.

Interlayer interactions in graphites.

PubMed

Chen, Xiaobin; Tian, Fuyang; Persson, Clas; Duan, Wenhui; Chen, Nan-xian

2013-11-06

Based on ab initio calculations of both the ABC- and AB-stacked graphites, interlayer potentials (i.e., graphene-graphene interaction) are obtained as a function of the interlayer spacing using a modified Möbius inversion method, and are used to calculate basic physical properties of graphite. Excellent consistency is observed between the calculated and experimental phonon dispersions of AB-stacked graphite, showing the validity of the interlayer potentials. More importantly, layer-related properties for nonideal structures (e.g., the exfoliation energy, cleave energy, stacking fault energy, surface energy, etc.) can be easily predicted from the interlayer potentials, which promise to be extremely efficient and helpful in studying van der Waals structures.

Predicting human genetic interactions from cancer genome evolution.

PubMed

Lu, Xiaowen; Megchelenbrink, Wout; Notebaart, Richard A; Huynen, Martijn A

2015-01-01

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

Polymer physics predicts the effects of structural variants on chromatin architecture.

PubMed

Bianco, Simona; Lupiáñez, Darío G; Chiariello, Andrea M; Annunziatella, Carlo; Kraft, Katerina; Schöpflin, Robert; Wittler, Lars; Andrey, Guillaume; Vingron, Martin; Pombo, Ana; Mundlos, Stefan; Nicodemi, Mario

2018-05-01

Structural variants (SVs) can result in changes in gene expression due to abnormal chromatin folding and cause disease. However, the prediction of such effects remains a challenge. Here we present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer-promoter contacts. PRISMR predicts higher-order chromatin structure from genome-wide chromosome conformation capture (Hi-C) data. Using the EPHA4 locus as a model, the effects of pathogenic SVs are predicted in silico and compared to Hi-C data generated from mouse limb buds and patient-derived fibroblasts. PRISMR deconvolves the folding complexity of the EPHA4 locus and identifies SV-induced ectopic contacts and alterations of 3D genome organization in homozygous or heterozygous states. We show that SVs can reconfigure topologically associating domains, thereby producing extensive rewiring of regulatory interactions and causing disease by gene misexpression. PRISMR can be used to predict interactions in silico, thereby providing a tool for analyzing the disease-causing potential of SVs.

Thermodynamic properties of diamond and wurtzite model fluids from computer simulation and thermodynamic perturbation theory

NASA Astrophysics Data System (ADS)

Zhou, S.; Solana, J. R.

2018-03-01

Monte Carlo NVT simulations have been performed to obtain the thermodynamic and structural properties and perturbation coefficients up to third order in the inverse temperature expansion of the Helmholtz free energy of fluids with potential models proposed in the literature for diamond and wurtzite lattices. These data are used to analyze performance of a coupling parameter series expansion (CPSE). The main findings are summarized as follows, (1) The CPSE provides accurate predictions of the first three coefficient in the inverse temperature expansion of Helmholtz free energy for the potential models considered and the thermodynamic properties of these fluids are predicted more accurately when the CPSE is truncated at second or third order. (2) The Barker-Henderson (BH) recipe is appropriate for determining the effective hard sphere diameter for strongly repulsive potential cores, but its performance worsens with increasing the softness of the potential core. (3) For some thermodynamic properties the first-order CPSE works better for the diamond potential, whose tail is dominated by repulsive interactions, than for the potential, whose tail is dominated by attractive interactions. However, the first-order CPSE provides unsatisfactory results for the excess internal energy and constant-volume excess heat capacity for the two potential models.

Effective Potentials for Folding Proteins

NASA Astrophysics Data System (ADS)

Chen, Nan-Yow; Su, Zheng-Yao; Mou, Chung-Yu

2006-02-01

A coarse-grained off-lattice model that is not biased in any way to the native state is proposed to fold proteins. To predict the native structure in a reasonable time, the model has included the essential effects of water in an effective potential. Two new ingredients, the dipole-dipole interaction and the local hydrophobic interaction, are introduced and are shown to be as crucial as the hydrogen bonding. The model allows successful folding of the wild-type sequence of protein G and may have provided important hints to the study of protein folding.

Validation of NASCAP-2K Spacecraft-Environment Interactions Calculations

NASA Technical Reports Server (NTRS)

Davis, V. A.; Mandell, M. J.; Gardner, B. M.; Mikellides, I. G.; Neergaard, L. F.; Cooke, D. L.; Minor, J.

2004-01-01

The recently released Nascap-2k, version 2.0, three-dimensional computer code models interactions between spacecraft surfaces and low-earth-orbit, geosynchronous, auroral, and interplanetary plasma environments. It replaces the earlier three-dimensional spacecraft interactions codes NASCAP/GEO, NASCAP/LEO, POLAR, and DynaPAC. Nascap-2k has improved numeric techniques, a modern user interface, and a simple, interactive satellite surface definition module (Object ToolKit). We establish the accuracy of Nascap-2k both by comparing computed currents and potentials with analytic results and by comparing Nascap-2k results with published calculations using the earlier codes. Nascap-2k predicts Langmuir-Blodgett or Parker-Murphy current collection for a nearly spherical (100 surfaces) satellite in a short Debye length plasma depending on the absence or presence of a magnetic field. A low fidelity (in geometry and time) Nascap-2k geosynchronous charging calculation gives the same results as the corresponding low fidelity NASCAP/GEO calculation. A high fidelity calculation (using the Nascap-2k improved geometry and time stepping capabilities) gives higher potentials, which are more consistent with typical observations. Nascap-2k predicts the same current as a function of applied potential as was observed and calculated by NASCAP/LEO for the SPEAR I rocket with a bipolar sheath. A Nascap-2k DMSP charging calculation gives results similar to those obtained using POLAR and consistent with observation.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.

PubMed

Perišić, Ognjen

2018-03-16

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol's ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner's interacting scaffold is more flexible and adaptable.

An orientation sensitive approach in biomolecule interaction quantitative structure-activity relationship modeling and its application in ion-exchange chromatography.

PubMed

Kittelmann, Jörg; Lang, Katharina M H; Ottens, Marcel; Hubbuch, Jürgen

2017-01-27

Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases. Copyright © 2016 Elsevier B.V. All rights reserved.

Differential susceptibility in longitudinal models of gene-environment interaction for adolescent depression.

PubMed

Li, James J; Berk, Michele S; Lee, Steve S

2013-11-01

Although family support reliably predicts the development of adolescent depression and suicidal behaviors, relatively little is known about the interplay of family support with potential genetic factors. We tested the association of the 44 base pair polymorphism in the serotonin transporter linked promoter region gene (5-HTTLPR), family support (i.e., cohesion, communication, and warmth), and their interaction with self-reported depression symptoms and risk for suicide in 1,030 Caucasian adolescents and young adults from the National Longitudinal Study of Adolescent Health. High-quality family support predicted fewer symptoms of depression and reduced risk for suicidality. There was also a significant interaction between 5-HTTLPR and family support for boys and a marginally significant interaction for girls. Among boys with poor family support, youth with at least one short allele had more symptoms of depression and a higher risk for suicide attempts relative to boys homozygous for the long allele. However, in the presence of high family support, boys with the short allele had the fewest depression symptoms (but not suicide attempts). Results suggest that the short allele may increase reactivity to both negative and positive family influences in the development of depression. We discuss the potential role of interactive exchanges between family support and offspring genotype in the development of adolescent depression and suicidal behaviors.

Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

PubMed

O'Reilly, Paul; Ortutay, Csaba; Gernon, Grainne; O'Connell, Enda; Seoighe, Cathal; Boyce, Susan; Serrano, Luis; Szegezdi, Eva

2014-12-19

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression. Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response. The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

PubMed

Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Ma, Zhiwei; Grinter, Sam Z; Zou, Xiaoqin

2017-03-01

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Gas-surface interactions using accommodation coefficients for a dilute and a dense gas in a micro- or nanochannel: heat flux predictions using combined molecular dynamics and Monte Carlo techniques.

PubMed

Nedea, S V; van Steenhoven, A A; Markvoort, A J; Spijker, P; Giordano, D

2014-05-01

The influence of gas-surface interactions of a dilute gas confined between two parallel walls on the heat flux predictions is investigated using a combined Monte Carlo (MC) and molecular dynamics (MD) approach. The accommodation coefficients are computed from the temperature of incident and reflected molecules in molecular dynamics and used as effective coefficients in Maxwell-like boundary conditions in Monte Carlo simulations. Hydrophobic and hydrophilic wall interactions are studied, and the effect of the gas-surface interaction potential on the heat flux and other characteristic parameters like density and temperature is shown. The heat flux dependence on the accommodation coefficient is shown for different fluid-wall mass ratios. We find that the accommodation coefficient is increasing considerably when the mass ratio is decreased. An effective map of the heat flux depending on the accommodation coefficient is given and we show that MC heat flux predictions using Maxwell boundary conditions based on the accommodation coefficient give good results when compared to pure molecular dynamics heat predictions. The accommodation coefficients computed for a dilute gas for different gas-wall interaction parameters and mass ratios are transferred to compute the heat flux predictions for a dense gas. Comparison of the heat fluxes derived using explicit MD, MC with Maxwell-like boundary conditions based on the accommodation coefficients, and pure Maxwell boundary conditions are discussed. A map of the heat flux dependence on the accommodation coefficients for a dense gas, and the effective accommodation coefficients for different gas-wall interactions are given. In the end, this approach is applied to study the gas-surface interactions of argon and xenon molecules on a platinum surface. The derived accommodation coefficients are compared with values of experimental results.

Review of Tropical-Extratropical Teleconnections on Intraseasonal Time Scales

NASA Astrophysics Data System (ADS)

Stan, Cristiana; Straus, David M.; Frederiksen, Jorgen S.; Lin, Hai; Maloney, Eric D.; Schumacher, Courtney

2017-12-01

The interactions and teleconnections between the tropical and midlatitude regions on intraseasonal time scales are an important modulator of tropical and extratropical circulation anomalies and their associated weather patterns. These interactions arise due to the impact of the tropics on the extratropics, the impact of the midlatitudes on the tropics, and two-way interactions between the regions. Observational evidence, as well as theoretical studies with models of complexity ranging from the linear barotropic framework to intricate Earth system models, suggest the involvement of a myriad of processes and mechanisms in generating and maintaining these interconnections. At this stage, our understanding of these teleconnections is primarily a collection of concepts; a comprehensive theoretical framework has yet to be established. These intraseasonal teleconnections are increasingly recognized as an untapped source of potential subseasonal predictability. However, the complexity and diversity of mechanisms associated with these teleconnections, along with the lack of a conceptual framework to relate them, prevent this potential predictability from being translated into realized forecast skill. This review synthesizes our progress in understanding the observed characteristics of intraseasonal tropical-extratropical interactions and their associated mechanisms, identifies the significant gaps in this understanding, and recommends new research endeavors to address the remaining challenges.

ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures.

PubMed

Park, Jungkap; Saitou, Kazuhiro

2014-09-18

Multibody potentials accounting for cooperative effects of molecular interactions have shown better accuracy than typical pairwise potentials. The main challenge in the development of such potentials is to find relevant structural features that characterize the tightly folded proteins. Also, the side-chains of residues adopt several specific, staggered conformations, known as rotamers within protein structures. Different molecular conformations result in different dipole moments and induce charge reorientations. However, until now modeling of the rotameric state of residues had not been incorporated into the development of multibody potentials for modeling non-bonded interactions in protein structures. In this study, we develop a new multibody statistical potential which can account for the influence of rotameric states on the specificity of atomic interactions. In this potential, named "rotamer-dependent atomic statistical potential" (ROTAS), the interaction between two atoms is specified by not only the distance and relative orientation but also by two state parameters concerning the rotameric state of the residues to which the interacting atoms belong. It was clearly found that the rotameric state is correlated to the specificity of atomic interactions. Such rotamer-dependencies are not limited to specific type or certain range of interactions. The performance of ROTAS was tested using 13 sets of decoys and was compared to those of existing atomic-level statistical potentials which incorporate orientation-dependent energy terms. The results show that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. A new multibody statistical potential, ROTAS accounting for the influence of rotameric states on the specificity of atomic interactions was developed and tested on decoy sets. The results show that ROTAS has improved ability to recognize native structure from decoy models compared to other potentials. The effectiveness of ROTAS may provide insightful information for the development of many applications which require accurate side-chain modeling such as protein design, mutation analysis, and docking simulation.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ.

PubMed

Simpkin, Adam J; Rigden, Daniel J

2016-07-13

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target. Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface. The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter's druggable intrafilament interface as a potential drug target.

Modeling the Electrostatics of Hollow Shell Suspensions: Ion Distribution, Pair Interactions, and Many-Body Effects.

PubMed

Hallez, Yannick; Meireles, Martine

2016-10-11

Electrostatic interactions play a key role in hollow shell suspensions as they determine their structure, stability, thermodynamics, and rheology and also the loading capacity of small charged species for nanoreservoir applications. In this work, fast, reliable modeling strategies aimed at predicting the electrostatics of hollow shells for one, two, and many colloids are proposed and validated. The electrostatic potential inside and outside a hollow shell with a finite thickness and a specific permittivity is determined analytically in the Debye-Hückel (DH) limit. An expression for the interaction potential between two such hollow shells is then derived and validated numerically. It follows a classical Yukawa form with an effective charge depending on the shell geometry, permittivity, and inner and outer surface charge densities. The predictions of the Ornstein-Zernike (OZ) equation with this pair potential to determine equations of state are then evaluated by comparison to results obtained with a Brownian dynamics algorithm coupled to the resolution of the linearized Poisson-Boltzmann and Laplace equations (PB-BD simulations). The OZ equation based on the DLVO-like potential performs very well in the dilute regime as expected, but also quite well, and more surprisingly, in the concentrated regime in which full spheres exhibit significant many-body effects. These effects are shown to vanish for shells with small thickness and high permittivity. For highly charged hollow shells, we propose and validate a charge renormalization procedure. Finally, using PB-BD simulations, we show that the cell model predicts the ion distribution inside and outside hollow shells accurately in both electrostatically dilute and concentrated suspensions. We then determine the shell loading capacity as a function of salt concentration, volume fraction, and surface charge density for nanoreservoir applications such as drug delivery, sensing, or smart coatings.

Herb–drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDP-glucuronosyltransferase (UGT) 2B7

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

Ma, Hai-Ying, E-mail: cmu4h-mhy@126.com; Sun, Dong-Xue; Cao, Yun-Feng

2014-05-15

Herb–drug interaction strongly limits the clinical application of herbs and drugs, and the inhibition of herbal components towards important drug-metabolizing enzymes (DMEs) has been regarded as one of the most important reasons. The present study aims to investigate the inhibition potential of andrographolide derivatives towards one of the most important phase II DMEs UDP-glucuronosyltransferases (UGTs). Recombinant UGT isoforms (except UGT1A4)-catalyzed 4-methylumbelliferone (4-MU) glucuronidation reaction and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation were employed to firstly screen the andrographolide derivatives' inhibition potential. High specific inhibition of andrographolide derivatives towards UGT2B7 was observed. The inhibition type and parameters (K{sub i}) were determined for themore » compounds exhibiting strong inhibition capability towards UGT2B7, and human liver microsome (HLMs)-catalyzed zidovudine (AZT) glucuronidation probe reaction was used to furtherly confirm the inhibition behavior. In combination of inhibition parameters (K{sub i}) and in vivo concentration of andrographolide and dehydroandrographolide, the potential in vivo inhibition magnitude was predicted. Additionally, both the in vitro inhibition data and computational modeling results provide important information for the modification of andrographolide derivatives as selective inhibitors of UGT2B7. Taken together, data obtained from the present study indicated the potential herb–drug interaction between Andrographis paniculata and the drugs mainly undergoing UGT2B7-catalyzed metabolic elimination, and the andrographolide derivatives as potential candidates for the selective inhibitors of UGT2B7. - Highlights: • Specific inhibition of andrographolide derivatives towards UGT2B7. • Herb-drug interaction related withAndrographis paniculata. • Guidance for design of UGT2B7 specific inhibitors.« less

Macaques can predict social outcomes from facial expressions.

PubMed

Waller, Bridget M; Whitehouse, Jamie; Micheletta, Jérôme

2016-09-01

There is widespread acceptance that facial expressions are useful in social interactions, but empirical demonstration of their adaptive function has remained elusive. Here, we investigated whether macaques can use the facial expressions of others to predict the future outcomes of social interaction. Crested macaques (Macaca nigra) were shown an approach between two unknown individuals on a touchscreen and were required to choose between one of two potential social outcomes. The facial expressions of the actors were manipulated in the last frame of the video. One subject reached the experimental stage and accurately predicted different social outcomes depending on which facial expressions the actors displayed. The bared-teeth display (homologue of the human smile) was most strongly associated with predicted friendly outcomes. Contrary to our predictions, screams and threat faces were not associated more with conflict outcomes. Overall, therefore, the presence of any facial expression (compared to neutral) caused the subject to choose friendly outcomes more than negative outcomes. Facial expression in general, therefore, indicated a reduced likelihood of social conflict. The findings dispute traditional theories that view expressions only as indicators of present emotion and instead suggest that expressions form part of complex social interactions where individuals think beyond the present.

Interactions between callous unemotional behaviors and executive function in early childhood predict later socioemotional functioning

PubMed Central

Waller, Rebecca; Hyde, Luke W.; Baskin-Sommers, Arielle; Olson, Sheryl L.

2018-01-01

Callous unemotional (CU) behaviors are linked to aggression, behavior problems, and difficulties in peer relationships in children and adolescents. However, few studies have examined whether early childhood CU behaviors predict aggression or peer-rejection during late-childhood or potential moderation of this relationship by executive function. The current study examined whether the interaction of CU behaviors and executive function in early childhood predicted different forms of aggression in late-childhood, including proactive, reactive, and relational aggression, as well as how much children were liked by their peers. Data from cross-informant reports and multiple observational tasks were collected from a high-risk sample (N=240; female=118) at ages 3 and 10 years old. Parent reports of CU behaviors at age 3 predicted teacher reports of reactive, proactive, and relational aggression, as well as lower peer-liking at age 10. Moderation analysis showed that specifically at high levels of CU behaviors and low levels of observed executive function, children were reported by teachers as showing greater reactive and proactive aggression, and were less-liked by peers. Findings demonstrate that early childhood CU behaviors and executive function have unique main and interactive effects on both later aggression and lower peer-liking even when taking into account stability in behavior problems over time. By elucidating how CU behaviors and deficits in executive function potentiate each other during early childhood, we can better characterize the emergence of severe and persistent behavior and interpersonal difficulties across development. PMID:27418255

Harsh Parenting and Fearfulness in Toddlerhood Interact to Predict Amplitudes of Preschool Error-Related Negativity

PubMed Central

Brooker, Rebecca J.; Buss, Kristin A.

2014-01-01

Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children’s risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. PMID:24721466

Analytical bond order potential for simulations of BeO 1D and 2D nanostructures and plasma-surface interactions

NASA Astrophysics Data System (ADS)

Byggmästar, J.; Hodille, E. A.; Ferro, Y.; Nordlund, K.

2018-04-01

An analytical interatomic bond order potential for the Be-O system is presented. The potential is fitted and compared to a large database of bulk BeO and point defect properties obtained using density functional theory. Its main applications include simulations of plasma-surface interactions involving oxygen or oxide layers on beryllium, as well as simulations of BeO nanotubes and nanosheets. We apply the potential in a study of oxygen irradiation of Be surfaces, and observe the early stages of an oxide layer forming on the Be surface. Predicted thermal and elastic properties of BeO nanotubes and nanosheets are simulated and compared with published ab initio data.

Evaluation of the potential of Raman microspectroscopy for prediction of chemotherapeutic response to cisplatin in lung adenocarcinoma.

PubMed

Nawaz, Haq; Bonnier, Franck; Knief, Peter; Howe, Orla; Lyng, Fiona M; Meade, Aidan D; Byrne, Hugh J

2010-12-01

The study of the interaction of anticancer drugs with mammalian cells in vitro is important to elucidate the mechanisms of action of the drug on its biological targets. In this context, Raman spectroscopy is a potential candidate for high throughput, non-invasive analysis. To explore this potential, the interaction of cis-diamminedichloroplatinum(II) (cisplatin) with a human lung adenocarcinoma cell line (A549) was investigated using Raman microspectroscopy. The results were correlated with parallel measurements from the MTT cytotoxicity assay, which yielded an IC(50) value of 1.2 ± 0.2 µM. To further confirm the spectral results, Raman spectra were also acquired from DNA extracted from A549 cells exposed to cisplatin and from unexposed controls. Partial least squares (PLS) multivariate regression and PLS Jackknifing were employed to highlight spectral regions which varied in a statistically significant manner with exposure to cisplatin and with the resultant changes in cellular physiology measured by the MTT assay. The results demonstrate the potential of the cellular Raman spectrum to non-invasively elucidate spectral changes that have their origin either in the biochemical interaction of external agents with the cell or its physiological response, allowing the prediction of the cellular response and the identification of the origin of the chemotherapeutic response at a molecular level in the cell.

Molecular interactions between photosystem I and ferredoxin: an integrated energy frustration and experimental model

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

Cashman, Derek J.; Zhu, Tuo; Simmerman, Richard F.

2014-08-01

The stromal domain (PsaC, PsaD, and PsaE) of photosystem I (PSI) reduces transiently bound ferredoxin (Fd) or flavodoxin. Experimental structures exist for all of these protein partners individually, but no experimental structure of the PSI/Fd or PSI/flavodoxin complexes is presently available. Molecular models of Fd docked onto the stromal domain of the cyanobacterial PSI site are constructed here utilizing X-ray and NMR structures of PSI and Fd, respectively. Moreover, predictions of potential protein-protein interaction regions are based on experimental site-directed mutagenesis and cross-linking studies to guide rigid body docking calculations of Fd into PSI, complemented by energy landscape theory tomore » bring together regions of high energetic frustration on each of the interacting proteins. Results identify two regions of high localized frustration on the surface of Fd that contain negatively charged Asp and Glu residues. Our study predicts that these regions interact predominantly with regions of high localized frustration on the PsaC, PsaD, and PsaE chains of PSI, which include several residues predicted by previous experimental studies.« less

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

Hao, Ming; Bryant, Stephen H; Wang, Yanli

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

An update on the potential role of intestinal first-pass metabolism for the prediction of drug-drug interactions: the role of PBPK modeling.

PubMed

Alqahtani, Saeed; Bukhari, Ishfaq; Albassam, Ahmed; Alenazi, Maha

2018-05-28

The intestinal absorption process is a combination of several events that are governed by various factors. Several transport mechanisms are involved in drug absorption through enterocytes via active and/or passive processes. The transported molecules then undergo intestinal metabolism, which together with intestinal transport may affect the systemic availability of drugs. Many studies have provided clear evidence on the significant role of intestinal first-pass metabolism on drug bioavailability and degree of drug-drug interactions (DDIs). Areas covered: This review provides an update on the role of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of drug-drug interactions. It also provides a comprehensive overview and summary of the latest update in the role of PBPK modeling in prediction of intestinal metabolism and DDIs in humans. Expert opinion: The contribution of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of DDIs has become more evident over the last few years. Several in vitro, in situ, and in vivo models have been developed to evaluate the role of first-pass metabolism and to predict DDIs. Currently, physiologically based pharmacokinetic modeling is considered the most valuable tool for the prediction of intestinal first-pass metabolism and DDIs.

Neural Reactivity to Emotional Stimuli Prospectively Predicts the Impact of a Natural Disaster on Psychiatric Symptoms in Children.

PubMed

Kujawa, Autumn; Hajcak, Greg; Danzig, Allison P; Black, Sarah R; Bromet, Evelyn J; Carlson, Gabrielle A; Kotov, Roman; Klein, Daniel N

2016-09-01

Natural disasters expose entire communities to stress and trauma, leading to increased risk for psychiatric symptoms. Yet, the majority of exposed individuals are resilient, highlighting the importance of identifying underlying factors that contribute to outcomes. The current study was part of a larger prospective study of children in Long Island, New York (n = 260). At age 9, children viewed unpleasant and pleasant images while the late positive potential (LPP), an event-related potential component that reflects sustained attention toward salient information, was measured. Following the event-related potential assessment, Hurricane Sandy, the second costliest hurricane in United States history, hit the region. Eight weeks after the hurricane, mothers reported on exposure to hurricane-related stress and children's internalizing and externalizing symptoms. Symptoms were reassessed 8 months after the hurricane. The LPP predicted both internalizing and externalizing symptoms after accounting for prehurricane symptomatology and interacted with stress to predict externalizing symptoms. Among children exposed to higher levels of hurricane-related stress, enhanced neural reactivity to unpleasant images predicted greater externalizing symptoms 8 weeks after the disaster, while greater neural reactivity to pleasant images predicted lower externalizing symptoms. Moreover, interactions between the LPP and stress continued to predict externalizing symptoms 8 months after the hurricane. Results indicate that heightened neural reactivity and attention toward unpleasant information, as measured by the LPP, predispose children to psychiatric symptoms when exposed to higher levels of stress related to natural disasters, while greater reactivity to and processing of pleasant information may be a protective factor. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Mechanism-based inactivation of human cytochrome P450 enzymes: strategies for diagnosis and drug-drug interaction risk assessment.

PubMed

Venkatakrishnan, K; Obach, R S; Rostami-Hodjegan, A

2007-01-01

Among drugs that cause pharmacokinetic drug-drug interactions, mechanism-based inactivators of cytochrome P450 represent several of those agents that cause interactions of the greatest magnitude. In vitro inactivation kinetic data can be used to predict the potential for new drugs to cause drug interactions in the clinic. However, several factors exist, each with its own uncertainty, that must be taken into account in order to predict the magnitude of interactions reliably. These include aspects of in vitro experimental design, an understanding of relevant in vivo concentrations of the inactivator, and the extent to which the inactivated enzyme is involved in the clearance of the affected drug. Additionally, the rate of enzyme degradation in vivo is also an important factor that needs to be considered in the prediction of the drug interaction magnitudes. To address mechanism-based inactivation for new drugs, various in vitro experimental approaches have been employed. The selection of approaches for in vitro kinetic characterization of inactivation as well as in vitro-in vivo extrapolation should be guided by the purpose of the exercise and the stage of drug discovery and development, with an increase in the level of sophistication throughout the research and development process.

Comment on “Isolating the non-polar contributions to the intermolecular potential for water-alkane interactions” [J. Chem. Phys. 141, 064905 (2014)

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

McDaniel, Jesse G.; Yethiraj, Arun

The manuscript by Ballal et al.(Ref 1) presents an interesting study demonstrating the inability of popular force fields with standard combination rules to accurately describe water/alkane interactions. The authors find that the Lorentz-Berthelot combination rules on the SPC/E water and TraPPE alkane potentials give a cross interaction that fails to predict the (low-water content) water solubility in various alkanes. Realizing that both explicit polarization as well as the static octupole moment of methane are missing in these potentials, the authors examine the effect of these terms, but are still unable to resolve the discrepancy. They conclude with the statement thatmore » “the research community lacks a complete picture of water-alkane interactions at the molecular level.« less

Life history determines genetic structure and evolutionary potential of host–parasite interactions

PubMed Central

Barrett, Luke G.; Thrall, Peter H.; Burdon, Jeremy J.; Linde, Celeste C.

2009-01-01

Measures of population genetic structure and diversity of disease-causing organisms are commonly used to draw inferences regarding their evolutionary history and potential to generate new variation in traits that determine interactions with their hosts. Parasite species exhibit a range of population structures and life-history strategies, including different transmission modes, life-cycle complexity, off-host survival mechanisms and dispersal ability. These are important determinants of the frequency and predictability of interactions with host species. Yet the complex causal relationships between spatial structure, life history and the evolutionary dynamics of parasite populations are not well understood. We demonstrate that a clear picture of the evolutionary potential of parasitic organisms and their demographic and evolutionary histories can only come from understanding the role of life history and spatial structure in influencing population dynamics and epidemiological patterns. PMID:18947899

Comment on “Isolating the non-polar contributions to the intermolecular potential for water-alkane interactions” [J. Chem. Phys. 141, 064905 (2014)

DOE PAGES

McDaniel, Jesse G.; Yethiraj, Arun

2016-04-06

The manuscript by Ballal et al.(Ref 1) presents an interesting study demonstrating the inability of popular force fields with standard combination rules to accurately describe water/alkane interactions. The authors find that the Lorentz-Berthelot combination rules on the SPC/E water and TraPPE alkane potentials give a cross interaction that fails to predict the (low-water content) water solubility in various alkanes. Realizing that both explicit polarization as well as the static octupole moment of methane are missing in these potentials, the authors examine the effect of these terms, but are still unable to resolve the discrepancy. They conclude with the statement thatmore » “the research community lacks a complete picture of water-alkane interactions at the molecular level.« less

Life history determines genetic structure and evolutionary potential of host-parasite interactions.

PubMed

Barrett, Luke G; Thrall, Peter H; Burdon, Jeremy J; Linde, Celeste C

2008-12-01

Measures of population genetic structure and diversity of disease-causing organisms are commonly used to draw inferences regarding their evolutionary history and potential to generate new variation in traits that determine interactions with their hosts. Parasite species exhibit a range of population structures and life-history strategies, including different transmission modes, life-cycle complexity, off-host survival mechanisms and dispersal ability. These are important determinants of the frequency and predictability of interactions with host species. Yet the complex causal relationships between spatial structure, life history and the evolutionary dynamics of parasite populations are not well understood. We demonstrate that a clear picture of the evolutionary potential of parasitic organisms and their demographic and evolutionary histories can only come from understanding the role of life history and spatial structure in influencing population dynamics and epidemiological patterns.

Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

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

Tamagnini, Paolo; Krause, Josua W.; Dasgupta, Aritra

2017-05-14

To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treatingmore » a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.« less

Changes of antioxidant potential of pasta fortified with parsley (Petroselinum Crispum mill.) leaves in the light of protein-phenolics interactions.

PubMed

Sęczyk, Łukasz; Świeca, Michał; Gawlik-Dziki, Urszula

2015-01-01

Pasta is considered as an effective carrier of prohealth ingredients in food fortification. The aim of this study was to examine the changes of antioxidant potential of wheat pasta affected by fortification with powdered parsley leaves. A special attention was paid to effectiveness of fortification in the light of proteinphenolic interactions. To improve antioxidant activity of pasta, part of wheat flour was replaced with powdered parsley leaves from 1% to 4% (w/w). The total phenolics content was determined with Folin-Ciocalteau reagent. Antioxidant capacity was evaluated using in vitro assays - abilities to scavenge free radicals (ABTS) and to reduce iron (III) (FRAP). Predicted phenolic contents and antioxidant activity were calculated. To determine the protein-phenolics interactions SE-HPLC and SDS-PAGE techniques were used. Fortification of pasta had a positive effect on its phenolic contents and antioxidant properties. The highest phenolics level and antioxidant activity of pasta were obtained by supplementation with 4% of parsley leaves. However, in most cases experimental values were significantly lower than those predicted. The protein profiles obtained after SDS-PAGE differed significantly among control and enriched pasta. Furthermore, the addition of parsley leaves to pasta resulted in increase of peaks areas obtained by SE-HPLC. Results indicate the occurrence of the protein-phenolics interactions in fortified pasta. Overall, the effectiveness of fortification and consequently biological effect is limited by many factors including interactions between phenolics and pasta proteins. In the light of this results the study of potential interaction of bioactive supplements with food matrix should be taken into account during designing new functional food products.

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition

PubMed Central

Kulp, John L.; Cloudsdale, Ian S.; Kulp, John L.

2017-01-01

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition. PMID:28837642

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition.

PubMed

Kulp, John L; Cloudsdale, Ian S; Kulp, John L; Guarnieri, Frank

2017-01-01

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition.

Metabolism of captopril carboxyl ester derivatives for percutaneous absorption.

PubMed

Gullick, Darren R; Ingram, Matthew J; Pugh, W John; Cox, Paul A; Gard, Paul; Smart, John D; Moss, Gary P

2009-02-01

To determine the metabolism of captopril n-carboxyl derivatives and how this may impact on their use as transdermal prodrugs. The pharmacological activity of the ester derivatives was also characterised in order to compare the angiotensin converting enzyme inhibitory potency of the derivatives compared with the parent drug, captopril. The metabolism rates of the ester derivatives were determined in vitro (using porcine liver esterase and porcine ear skin) and in silico (using molecular modelling to investigate the potential to predict metabolism). Relatively slow pseudo first-order metabolism of the prodrugs was observed, with the ethyl ester displaying the highest rate of metabolism. A strong relationship was established between in-vitro methods, while in-silico methods support the use of in-vitro methods and highlight the potential of in-silico techniques to predict metabolism. All the prodrugs behaved as angiotensin converting enzyme inhibitors, with the methyl ester displaying optimum inhibition. In-vitro porcine liver esterase metabolism rates inform in-vitro skin rates well, and in-silico interaction energies relate well to both. Thus, in-silico methods may be developed that include interaction energies to predict metabolism rates.

Callous-Unemotional Traits Predict Self-Reported Offending in Adolescent Boys: The Mediating Role of Delinquent Peers and the Moderating Role of Parenting Practices

ERIC Educational Resources Information Center

Ray, James V.; Frick, Paul J.; Thornton, Laura C.; Wall Myers, Tina D.; Steinberg, Laurence; Cauffman, Elizabeth

2017-01-01

Research has only recently begun to examine how callous-unemotional (CU) traits interact with contextual factors to predict delinquent behavior. The current study attempts to explain the well-established link between CU traits and offending by testing the potential mediating and moderating roles of 2 critical contextual factors: peer delinquency…

The spatial scaling of species interaction networks.

PubMed

Galiana, Nuria; Lurgi, Miguel; Claramunt-López, Bernat; Fortin, Marie-Josée; Leroux, Shawn; Cazelles, Kevin; Gravel, Dominique; Montoya, José M

2018-05-01

Species-area relationships (SARs) are pivotal to understand the distribution of biodiversity across spatial scales. We know little, however, about how the network of biotic interactions in which biodiversity is embedded changes with spatial extent. Here we develop a new theoretical framework that enables us to explore how different assembly mechanisms and theoretical models affect multiple properties of ecological networks across space. We present a number of testable predictions on network-area relationships (NARs) for multi-trophic communities. Network structure changes as area increases because of the existence of different SARs across trophic levels, the preferential selection of generalist species at small spatial extents and the effect of dispersal limitation promoting beta-diversity. Developing an understanding of NARs will complement the growing body of knowledge on SARs with potential applications in conservation ecology. Specifically, combined with further empirical evidence, NARs can generate predictions of potential effects on ecological communities of habitat loss and fragmentation in a changing world.

Hydraulic fracture height limits and fault interactions in tight oil and gas formations

NASA Astrophysics Data System (ADS)

Flewelling, Samuel A.; Tymchak, Matthew P.; Warpinski, Norm

2013-07-01

widespread use of hydraulic fracturing (HF) has raised concerns about potential upward migration of HF fluid and brine via induced fractures and faults. We developed a relationship that predicts maximum fracture height as a function of HF fluid volume. These predictions generally bound the vertical extent of microseismicity from over 12,000 HF stimulations across North America. All microseismic events were less than 600 m above well perforations, although most were much closer. Areas of shear displacement (including faults) estimated from microseismic data were comparatively small (radii on the order of 10 m or less). These findings suggest that fracture heights are limited by HF fluid volume regardless of whether the fluid interacts with faults. Direct hydraulic communication between tight formations and shallow groundwater via induced fractures and faults is not a realistic expectation based on the limitations on fracture height growth and potential fault slip.

Using an innovative multiple regression procedure in a cancer population (Part II): fever, depressive affect, and mobility problems clarify an influential symptom pair (pain-fatigue/weakness) and cluster (pain-fatigue/weakness-sleep problems).

PubMed

Francoeur, Richard B

2015-01-01

Most patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. However, only combinations where symptoms are mutually influential hold potential for identifying patient subgroups at greater risk, and in some contexts, interventions with "cross-over" (multisymptom) effects. Improved methods to detect and interpret interactions among symptoms, signs, or biomarkers are needed to reveal these influential pairs and clusters. I recently created sequential residual centering (SRC) to reduce multicollinearity in moderated regression, which enhances sensitivity to detect these interactions. I applied SRC to moderated regressions of single-item symptoms that interact to predict outcomes from 268 palliative radiation outpatients. I investigated: 1) the hypothesis that the interaction, pain × fatigue/weakness × sleep problems, predicts depressive affect only when fever presents, and 2) an exploratory analysis, when fever is absent, that the interaction, pain × fatigue/weakness × sleep problems × depressive affect, predicts mobility problems. In the fever context, three-way interactions (and derivative terms) of the four symptoms (pain, fatigue/weakness, fever, sleep problems) are tested individually and simultaneously; in the non-fever context, a single four-way interaction (and derivative terms) is tested. Fever interacts separately with fatigue/weakness and sleep problems; these comoderators each magnify the pain-depressive affect relationship along the upper or full range of pain values. In non-fever contexts, fatigue/weakness, sleep problems, and depressive affect comagnify the relationship between pain and mobility problems. Different mechanisms contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters.

Increasing potential predictability of Indian Summer monsoon active and break spells

NASA Astrophysics Data System (ADS)

Mani, N. J.; Goswami, B.

2009-12-01

An understanding of the limit on potential predictability is crucial for developing appropriate tools for extended range prediction of active/break spells of Indian summer monsoon (ISM). The global low frequency changes in climate modulate the annual cycle of the ISM and can influence the intrinsic predictability limit of the ISM intraseasonal oscillations (ISOs). Using 104 year (1901-2004) long daily rainfall data, the change in potential predictability of active and break spells are estimated by an empirical method. Using an ISO index based on 10-90 day filtered precipitation, Goswami and Xavier (2003)showed that the monsoon breaks are intrinsically more predictable (20-25 days) than the active conditions (10-15 days. In the present study, employing the same method in 15 year sliding windows, we found that the potential predictability of both active and break spells have undergone a rapid increase during the recent three decades. The potential predictability of active spells has shown an increase from 1 week to 2 weeks while that for break spells increased from 2 weeks to 3 weeks. This result is interesting and intriguing in the backdrop of recent finding that the potential predictability of monsoon weather has decreased substantially over the same period compared to earlier decades due to increased potential instability of the atmosphere. The possible role of internal dynamics and external forcing in producing this change has been explored. The variance among peak active/break conditions shows a steady decrease over the years, indicating a lesser event to event variability in the magnitude of ISO peak phases in recent years. The ISO predictability may be closely linked to the error energy cascading from the synoptic scales and the interaction between these scales. Computation of nonlinear kinetic energy exchange between synoptic and ISO scales in frequency domain, also support the notion of ineffectual influence of synoptic scale errors on the ISO scale.Ref: Goswami, B N and P K Xavier, 2003,GRL. 30(18), 1966, doi:10.1029/2003GL017,810, 2003. Fig 1. Change in potential predictability of rainfall ISO through a 15 year sliding window. a) potential predictability for evolution from active to break b) potential predictability for evolution from break to active.

Corresponding-states laws for protein solutions.

PubMed

Katsonis, Panagiotis; Brandon, Simon; Vekilov, Peter G

2006-09-07

The solvent around protein molecules in solutions is structured and this structuring introduces a repulsion in the intermolecular interaction potential at intermediate separations. We use Monte Carlo simulations with isotropic, pair-additive systems interacting with such potentials. We test if the liquid-liquid and liquid-solid phase lines in model protein solutions can be predicted from universal curves and a pair of experimentally determined parameters, as done for atomic and colloid materials using several laws of corresponding states. As predictors, we test three properties at the critical point for liquid-liquid separation: temperature, as in the original van der Waals law, the second virial coefficient, and a modified second virial coefficient, all paired with the critical volume fraction. We find that the van der Waals law is best obeyed and appears more general than its original formulation: A single universal curve describes all tested nonconformal isotropic pair-additive systems. Published experimental data for the liquid-liquid equilibrium for several proteins at various conditions follow a single van der Waals curve. For the solid-liquid equilibrium, we find that no single system property serves as its predictor. We go beyond corresponding-states correlations and put forth semiempirical laws, which allow prediction of the critical temperature and volume fraction solely based on the range of attraction of the intermolecular interaction potential.

Competition-interaction landscapes for the joint response of forests to climate change.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew C; Zhu, Kai

2014-06-01

The recent global increase in forest mortality episodes could not have been predicted from current vegetation models that are calibrated to regional climate data. Physiological studies show that mortality results from interactions between climate and competition at the individual scale. Models of forest response to climate do not include interactions because they are hard to estimate and require long-term observations on individual trees obtained at frequent (annual) intervals. Interactions involve multiple tree responses that can only be quantified if these responses are estimated as a joint distribution. A new approach provides estimates of climate–competition interactions in two critical ways, (i) among individuals, as a joint distribution of responses to combinations of inputs, such as resources and climate, and (ii) within individuals, due to allocation requirements that control outputs, such as demographic rates. Application to 20 years of data from climate and competition gradients shows that interactions control forest responses, and their omission from models leads to inaccurate predictions. Species most vulnerable to increasing aridity are not those that show the largest growth response to precipitation, but rather depend on interactions with the local resource environment. This first assessment of regional species vulnerability that is based on the scale at which climate operates, individual trees competing for carbon and water, supports predictions of potential savannification in the southeastern US.

Cytochrome P450 2C9-natural antiarthritic interactions: Evaluation of inhibition magnitude and prediction from in vitro data.

PubMed

Tan, Boon Hooi; Ahemad, Nafees; Pan, Yan; Palanisamy, Uma Devi; Othman, Iekhsan; Yiap, Beow Chin; Ong, Chin Eng

2018-04-01

Many dietary supplements are promoted to patients with osteoarthritis (OA) including the three naturally derived compounds, glucosamine, chondroitin and diacerein. Despite their wide spread use, research on interaction of these antiarthritic compounds with human hepatic cytochrome P450 (CYP) enzymes is limited. This study aimed to examine the modulatory effects of these compounds on CYP2C9, a major CYP isoform, using in vitro biochemical assay and in silico models. Utilizing valsartan hydroxylase assay as probe, all forms of glucosamine and chondroitin exhibited IC 50 values beyond 1000 μM, indicating very weak potential in inhibiting CYP2C9. In silico docking postulated no interaction with CYP2C9 for chondroitin and weak bonding for glucosamine. On the other hand, diacerein exhibited mixed-type inhibition with IC 50 value of 32.23 μM and K i value of 30.80 μM, indicating moderately weak inhibition. Diacerein's main metabolite, rhein, demonstrated the same mode of inhibition as diacerein but stronger potency, with IC 50 of 6.08 μM and K i of 1.16 μM. The docking of both compounds acquired lower CDOCKER interaction energy values, with interactions dominated by hydrogen and hydrophobic bondings. The ranking with respect to inhibition potency for the investigated compounds was generally the same in both in vitro enzyme assay and in silico modeling with order of potency being diacerein/rhein > various glucosamine/chondroitin forms. In vitro-in vivo extrapolation of inhibition kinetics (using 1 + [I]/K i ratio) demonstrated negligible potential of diacerein to cause interaction in vivo, whereas rhein was predicted to cause in vivo interaction, suggesting potential interaction risk with the CYP2C9 drug substrates. Copyright © 2018 John Wiley & Sons, Ltd.

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

PubMed

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra; Sacquin-Mora, Sophie

2016-10-01

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Great interactions: How binding incorrect partners can teach us about protein recognition and function

PubMed Central

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra

2016-01-01

ABSTRACT Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:27287388

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Attachment predicts cortisol response and closeness in dyadic social interaction.

PubMed

Ketay, Sarah; Beck, Lindsey A

2017-06-01

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

Wetting, meniscus structure, and capillary interactions of microspheres bound to a cylindrical liquid interface.

PubMed

Kim, Paul Y; Dinsmore, Anthony D; Hoagland, David A; Russell, Thomas P

2018-03-14

Wetting, meniscus structure, and capillary interactions for polystyrene microspheres deposited on constant curvature cylindrical liquid interfaces, constructed from nonvolatile ionic or oligomeric liquids, were studied by optical interferometry and optical microscopy. The liquid interface curvature resulted from the preferential wetting of finite width lines patterned onto planar silicon substrates. Key variables included sphere diameter, nominal (or average) contact angle, and deviatoric interfacial curvature. Menisci adopted the quadrupolar symmetry anticipated by theory, with interfacial deformation closely following predicted dependences on sphere diameter and nominal contact angle. Unexpectedly, the contact angle was not constant locally around the contact line, the nominal contact angle varied among seemingly identical spheres, and the maximum interface deviation did not follow the predicted dependence on deviatoric interfacial curvature. Instead, this deviation was up to an order-of-magnitude larger than predicted. Trajectories of neighboring microspheres visually manifested quadrupole-quadrupole interactions, eventually producing square sphere packings that foreshadow interfacial assembly as a potential route to hierarchical 2D particle structures.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Ballard, Christopher C.; Esty, C. Clark; Egolf, David A.

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation.

PubMed

Ballard, Christopher C; Esty, C Clark; Egolf, David A

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H

2016-12-01

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

PubMed Central

Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Airola, Antti; Wennerberg, Krister

2017-01-01

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications. PMID:28787438

Faraday Discussion 160 Introductory Lecture: Interpreting and Predicting Hofmeister Salt Ion and Solute Effects on Biopolymer and Model Processes Using the Solute Partitioning Model

PubMed Central

Record, M. Thomas; Guinn, Emily; Pegram, Laurel; Capp, Michael

2013-01-01

Understanding how Hofmeister salt ions and other solutes interact with proteins, nucleic acids, other biopolymers and water and thereby affect protein and nucleic acid processes as well as model processes (e.g solubility of model compounds) in aqueous solution is a longstanding goal of biophysical research. Empirical Hofmeister salt and solute “m-values” (derivatives of the observed standard free energy change for a model or biopolymer process with respect to solute or salt concentration m3) are equal to differences in chemical potential derivatives: m-value = Δ(dμ2/dm3) = Δμ23 which quantify the preferential interactions of the solute or salt with the surface of the biopolymer or model system (component 2) exposed or buried in the process. Using the SPM, we dissect μ23 values for interactions of a solute or Hofmeister salt with a set of model compounds displaying the key functional groups of biopolymers to obtain interaction potentials (called α-values) that quantify the interaction of the solute or salt per unit area of each functional group or type of surface. Interpreted using the SPM, these α-values provide quantitative information about both the hydration of functional groups and the competitive interaction of water and the solute or salt with functional groups. The analysis corroborates and quantifies previous proposals that the Hofmeister anion and cation series for biopolymer processes are determined by ion-specific, mostly unfavorable interactions with hydrocarbon surfaces; the balance between these unfavorable nonpolar interactions and often-favorable interactions of ions with polar functional groups determine the series null points. The placement of urea and glycine betaine (GB) at opposite ends of the corresponding series of nonelectrolytes results from the favorable interactions of urea, and unfavorable interactions of GB, with many (but not all) biopolymer functional groups. Interaction potentials and local-bulk partition coefficients quantifying the distribution of solutes (e.g. urea, glycine betaine) and Hofmeister salt ions in the vicinity of each functional group make good chemical sense when interpreted in terms of competitive noncovalent interactions. These interaction potentials allow solute and Hofmeister (noncoulombic) salt effects on protein and nucleic acid processes to be interpreted or predicted, and allow the use of solutes and salts as probes of interface formation and large-scale conformational changes in the steps of a biopolymer mechanism. PMID:23795491

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.

PubMed

Kinoshita, Kengo; Murakami, Yoichi; Nakamura, Haruki

2007-07-01

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

Knowledge-driven genomic interactions: an application in ovarian cancer.

PubMed

Kim, Dokyoon; Li, Ruowang; Dudek, Scott M; Frase, Alex T; Pendergrass, Sarah A; Ritchie, Marylyn D

2014-01-01

Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner. Thus, we propose a novel approach for identifying knowledge-driven genomic interactions and applying it to discover models associated with cancer clinical phenotypes using grammatical evolution neural networks (GENN). In order to demonstrate the utility of the proposed approach, an ovarian cancer data from the Cancer Genome Atlas (TCGA) was used for predicting clinical stage as a pilot project. We identified knowledge-driven genomic interactions associated with cancer stage from single knowledge bases such as sources of pathway-pathway interaction, but also knowledge-driven genomic interactions across different sets of knowledge bases such as pathway-protein family interactions by integrating different types of information. Notably, an integration model from different sources of biological knowledge achieved 78.82% balanced accuracy and outperformed the top models with gene expression or single knowledge-based data types alone. Furthermore, the results from the models are more interpretable because they are framed in the context of specific biological pathways or other expert knowledge. The success of the pilot study we have presented herein will allow us to pursue further identification of models predictive of clinical cancer survival and recurrence. Understanding the underlying tumorigenesis and progression in ovarian cancer through the global view of interactions within/between different biological knowledge sources has the potential for providing more effective screening strategies and therapeutic targets for many types of cancer.

Model colloid system for interfacial sorption kinetics

NASA Astrophysics Data System (ADS)

Salipante, Paul; Hudson, Steven

2014-11-01

Adsorption kinetics of nanometer scale molecules, such as proteins at interfaces, is usually determined through measurements of surface coverage. Their small size limits the ability to directly observe individual molecule behavior. To better understand the behavior of nanometer size molecules and the effect on interfacial kinetics, we use micron size colloids with a weak interfacial interaction potential as a model system. Thus, the interaction strength is comparable to many nanoscale systems (less than 10 kBT). The colloid-interface interaction potential is tuned using a combination of depletion, electrostatic, and gravitational forces. The colloids transition between an entropically trapped adsorbed state and a desorbed state through Brownian motion. Observations are made using an LED-based Total Internal Reflection Microscopy (TIRM) setup. The observed adsorption and desorption rates are compared theoretical predictions based on the measured interaction potential and near wall particle diffusivity. This experimental system also allows for the study of more complex dynamics such as nonspherical colloids and collective effects at higher concentrations.

Anomalous dynamics of interstitial dopants in soft crystals

PubMed Central

Tauber, Justin; Higler, Ruben; Sprakel, Joris

2016-01-01

The dynamics of interstitial dopants govern the properties of a wide variety of doped crystalline materials. To describe the hopping dynamics of such interstitial impurities, classical approaches often assume that dopant particles do not interact and travel through a static potential energy landscape. Here we show, using computer simulations, how these assumptions and the resulting predictions from classical Eyring-type theories break down in entropically stabilized body-centered cubic (BCC) crystals due to the thermal excitations of the crystalline matrix. Deviations are particularly severe close to melting where the lattice becomes weak and dopant dynamics exhibit strongly localized and heterogeneous dynamics. We attribute these anomalies to the failure of both assumptions underlying the classical description: (i) The instantaneous potential field experienced by dopants becomes largely disordered due to thermal fluctuations and (ii) elastic interactions cause strong dopant–dopant interactions even at low doping fractions. These results illustrate how describing nonclassical dopant dynamics requires taking the effective disordered potential energy landscape of strongly excited crystals and dopant–dopant interactions into account. PMID:27856751

Thomas-Fermi simulations of dense plasmas without pseudopotentials

NASA Astrophysics Data System (ADS)

Starrett, C. E.

2017-07-01

The Thomas-Fermi model for warm and hot dense matter is widely used to predict material properties such as the equation of state. However, for practical reasons current implementations use pseudopotentials for the electron-nucleus interaction instead of the bare Coulomb potential. This complicates the calculation and quantities such as free energy cannot be converged with respect to the pseudopotential parameters. We present a method that retains the bare Coulomb potential for the electron-nucleus interaction and does not use pseudopotentials. We demonstrate that accurate free energies are obtained by checking variational consistency. Examples for aluminum and iron plasmas are presented.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues

PubMed Central

Perišić, Ognjen

2018-01-01

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable. PMID:29547506

Ionic strength independence of charge distributions in solvation of biomolecules

NASA Astrophysics Data System (ADS)

Virtanen, J. J.; Sosnick, T. R.; Freed, K. F.

2014-12-01

Electrostatic forces enormously impact the structure, interactions, and function of biomolecules. We perform all-atom molecular dynamics simulations for 5 proteins and 5 RNAs to determine the dependence on ionic strength of the ion and water charge distributions surrounding the biomolecules, as well as the contributions of ions to the electrostatic free energy of interaction between the biomolecule and the surrounding salt solution (for a total of 40 different biomolecule/solvent combinations). Although water provides the dominant contribution to the charge density distribution and to the electrostatic potential even in 1M NaCl solutions, the contributions of water molecules and of ions to the total electrostatic interaction free energy with the solvated biomolecule are comparable. The electrostatic biomolecule/solvent interaction energies and the total charge distribution exhibit a remarkable insensitivity to salt concentrations over a huge range of salt concentrations (20 mM to 1M NaCl). The electrostatic potentials near the biomolecule's surface obtained from the MD simulations differ markedly, as expected, from the potentials predicted by continuum dielectric models, even though the total electrostatic interaction free energies are within 11% of each other.

Semiempirical prediction of protein folds

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Colubri, Andrés; Appignanesi, Gustavo

2001-08-01

We introduce a semiempirical approach to predict ab initio expeditious pathways and native backbone geometries of proteins that fold under in vitro renaturation conditions. The algorithm is engineered to incorporate a discrete codification of local steric hindrances that constrain the movements of the peptide backbone throughout the folding process. Thus, the torsional state of the chain is assumed to be conditioned by the fact that hopping from one basin of attraction to another in the Ramachandran map (local potential energy surface) of each residue is energetically more costly than the search for a specific (Φ, Ψ) torsional state within a single basin. A combinatorial procedure is introduced to evaluate coarsely defined torsional states of the chain defined ``modulo basins'' and translate them into meaningful patterns of long range interactions. Thus, an algorithm for structure prediction is designed based on the fact that local contributions to the potential energy may be subsumed into time-evolving conformational constraints defining sets of restricted backbone geometries whereupon the patterns of nonbonded interactions are constructed. The predictive power of the algorithm is assessed by (a) computing ab initio folding pathways for mammalian ubiquitin that ultimately yield a stable structural pattern reproducing all of its native features, (b) determining the nucleating event that triggers the hydrophobic collapse of the chain, and (c) comparing coarse predictions of the stable folds of moderately large proteins (N~100) with structural information extracted from the protein data bank.

Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35.

PubMed

Yu, Jinchao; Andreani, Jessica; Ochsenbein, Françoise; Guerois, Raphaël

2017-03-01

Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Accurate classical short-range forces for the study of collision cascades in Fe–Ni–Cr

DOE PAGES

Béland, Laurent Karim; Tamm, Artur; Mu, Sai; ...

2017-05-10

The predictive power of a classical molecular dynamics simulation is largely determined by the physical validity of its underlying empirical potential. In the case of high-energy collision cascades, it was recently shown that correctly modeling interactions at short distances is necessary to accurately predict primary damage production. An ab initio based framework is introduced for modifying an existing embedded-atom method FeNiCr potential to handle these short-range interactions. Density functional theory is used to calculate the energetics of two atoms approaching each other, embedded in the alloy, and to calculate the equation of state of the alloy as it is compressed.more » The pairwise terms and the embedding terms of the potential are modi ed in accordance with the ab initio results. Using this reparametrized potential, collision cascades are performed in Ni 50Fe 50, Ni 80Cr 20 and Ni 33Fe 33Cr 33. The simulations reveal that alloying Ni and NiCr to Fe reduces primary damage production, in agreement with some previous calculations. Alloying Ni and NiFe to Cr does not reduce primary damage production, in contradiction with previous calculations.« less

Computer-aided prediction of xenobiotic metabolism in the human body

NASA Astrophysics Data System (ADS)

Bezhentsev, V. M.; Tarasova, O. A.; Dmitriev, A. V.; Rudik, A. V.; Lagunin, A. A.; Filimonov, D. A.; Poroikov, V. V.

2016-08-01

The review describes the major databases containing information about the metabolism of xenobiotics, including data on drug metabolism, metabolic enzymes, schemes of biotransformation and the structures of some substrates and metabolites. Computational approaches used to predict the interaction of xenobiotics with metabolic enzymes, prediction of metabolic sites in the molecule, generation of structures of potential metabolites for subsequent evaluation of their properties are considered. The advantages and limitations of various computational methods for metabolism prediction and the prospects for their applications to improve the safety and efficacy of new drugs are discussed. Bibliography — 165 references.

Exploring the Potential of Direct-To-Consumer Genomic Test Data for Predicting Adverse Drug Events.

PubMed

Zhang, Patrick M; Sarkar, Indra Neil

2018-01-01

Recent technological advancements in genetic testing and the growing accessibility of public genomic data provide researchers with a unique avenue to approach personalized medicine. This feasibility study examined the potential of direct-to-consumer (DTC) genomic tests (focusing on 23andMe) in research and clinical applications. In particular, we combined population genetics information from the Personal Genome Project with adverse event reports from AEOLUS and pharmacogenetic information from PharmGKB. Primarily, associations between drugs based on co-occurring genetic variations and associations between variants and adverse events were used to assess the potential for leveraging single nucleotide polymorphism information from 23andMe. The results of this study suggest potential clinical uses of DTC tests in light of potential drug interactions. Furthermore, the results suggest great potential for analyzing associations at a population level to facilitate knowledge discovery in the realm of predicting adverse drug events.

Physics-based scoring of protein-ligand interactions: explicit polarizability, quantum mechanics and free energies.

PubMed

Bryce, Richard A

2011-04-01

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

Hexaquark states as possible candidates for di-baryonic molecular states with Yukawa potential in a semi-relativistic scheme

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

Patel, Smruti J., E-mail: fizix.smriti@gmail.com; Vinodkumar, P. C.

2016-05-06

We study the mass spectra of hexaquark states as di-hadronic molecules with Yukawa potential in a semi-relativistic scheme. We have solved numerically the relevant equation using mathematica notebook of Range-Kutta method including effective Yukawa like potential between two baryons to model the two-body interaction and have calculated their masses and binding energy. We have been able to assign the J{sup P} values for many of the exotic states according to their compositions. We have predicted some of the di-baryonic exotic states for which experimental as well as theoretical data are not available and we look forward to see the experimentalmore » support in favour of our predictions. So in the absence of such results our predictions can be used as guidelines for future experimental and theoretical analysis of exotic states.« less

Demographic and clinical correlates of autism symptom domains and autism spectrum diagnosis.

PubMed

Frazier, Thomas W; Youngstrom, Eric A; Embacher, Rebecca; Hardan, Antonio Y; Constantino, John N; Law, Paul; Findling, Robert L; Eng, Charis

2014-07-01

Demographic and clinical factors may influence assessment of autism symptoms. This study evaluated these correlates and also examined whether social communication and interaction and restricted/repetitive behavior provided unique prediction of autism spectrum disorder diagnosis. We analyzed data from 7352 siblings included in the Interactive Autism Network registry. Social communication and interaction and restricted/repetitive behavior symptoms were obtained using caregiver-reports on the Social Responsiveness Scale. Demographic and clinical correlates were covariates in regression models predicting social communication and interaction and restricted/repetitive behavior symptoms. Logistic regression and receiver operating characteristic curve analyses evaluated the incremental validity of social communication and interaction and restricted/repetitive behavior domains over and above global autism symptoms. Autism spectrum disorder diagnosis was the strongest correlate of caregiver-reported social communication and interaction and restricted/repetitive behavior symptoms. The presence of comorbid diagnoses also increased symptom levels. Social communication and interaction and restricted/repetitive behavior symptoms provided significant, but modest, incremental validity in predicting diagnosis beyond global autism symptoms. These findings suggest that autism spectrum disorder diagnosis is by far the largest determinant of quantitatively measured autism symptoms. Externalizing (attention deficit hyperactivity disorder) and internalizing (anxiety) behavior, low cognitive ability, and demographic factors may confound caregiver-report of autism symptoms, potentially necessitating a continuous norming approach to the revision of symptom measures. Social communication and interaction and restricted/repetitive behavior symptoms may provide incremental validity in the diagnosis of autism spectrum disorder. © The Author(s) 2013.

Scaling from single molecule to macroscopic adhesion at polymer/metal interfaces.

PubMed

Utzig, Thomas; Raman, Sangeetha; Valtiner, Markus

2015-03-10

Understanding the evolution of macroscopic adhesion based on fundamental molecular interactions is crucial to designing strong and smart polymer/metal interfaces that play an important role in many industrial and biomedical applications. Here we show how macroscopic adhesion can be predicted on the basis of single molecular interactions. In particular, we carry out dynamic single molecule-force spectroscopy (SM-AFM) in the framework of Bell-Evans' theory to gain information about the energy barrier between the bound and unbound states of an amine/gold junction. Furthermore, we use Jarzynski's equality to obtain the equilibrium ground-state energy difference of the amine/gold bond from these nonequilibrium force measurements. In addition, we perform surface forces apparatus (SFA) experiments to measure macroscopic adhesion forces at contacts where approximately 10(7) amine/gold bonds are formed simultaneously. The SFA approach provides an amine/gold interaction energy (normalized by the number of interacting molecules) of (36 ± 1)k(B)T, which is in excellent agreement with the interaction free energy of (35 ± 3)k(B)T calculated using Jarzynski's equality and single-molecule AFM experiments. Our results validate Jarzynski's equality for the field of polymer/metal interactions by measuring both sides of the equation. Furthermore, the comparison of SFA and AFM shows how macroscopic interaction energies can be predicted on the basis of single molecular interactions, providing a new strategy to potentially predict adhesive properties of novel glues or coatings as well as bio- and wet adhesion.

Demographic and clinical correlates of autism symptom domains and autism spectrum diagnosis

PubMed Central

Frazier, Thomas W; Youngstrom, Eric A; Embacher, Rebecca; Hardan, Antonio Y; Constantino, John N; Law, Paul; Findling, Robert L; Eng, Charis

2014-01-01

Demographic and clinical factors may influence assessment of autism symptoms. This study evaluated these correlates and also examined whether social communication and interaction and restricted/repetitive behavior provided unique prediction of autism spectrum disorder diagnosis. We analyzed data from 7352 siblings included in the Interactive Autism Network registry. Social communication and interaction and restricted/repetitive behavior symptoms were obtained using caregiver-reports on the Social Responsiveness Scale. Demographic and clinical correlates were covariates in regression models predicting social communication and interaction and restricted/repetitive behavior symptoms. Logistic regression and receiver operating characteristic curve analyses evaluated the incremental validity of social communication and interaction and restricted/repetitive behavior domains over and above global autism symptoms. Autism spectrum disorder diagnosis was the strongest correlate of caregiver-reported social communication and interaction and restricted/repetitive behavior symptoms. The presence of comorbid diagnoses also increased symptom levels. Social communication and interaction and restricted/repetitive behavior symptoms provided significant, but modest, incremental validity in predicting diagnosis beyond global autism symptoms. These findings suggest that autism spectrum disorder diagnosis is by far the largest determinant of quantitatively measured autism symptoms. Externalizing (attention deficit hyperactivity disorder) and internalizing (anxiety) behavior, low cognitive ability, and demographic factors may confound caregiver-report of autism symptoms, potentially necessitating a continuous norming approach to the revision of symptom measures. Social communication and interaction and restricted/repetitive behavior symptoms may provide incremental validity in the diagnosis of autism spectrum disorder. PMID:24104512

Direct Determination of the Equilibrium Unbinding Potential Profile for a Short DNA Duplex from Force Spectroscopy Data

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

Noy, A

2004-05-04

Modern force microscopy techniques allow researchers to use mechanical forces to probe interactions between biomolecules. However, such measurements often happen in non-equilibrium regime, which precludes straightforward extraction of the equilibrium energy information. Here we use the work averaging method based on Jarzynski equality to reconstruct the equilibrium interaction potential from the unbinding of a complementary 14-mer DNA duplex from the results of non-equilibrium single-molecule measurements. The reconstructed potential reproduces most of the features of the DNA stretching transition, previously observed only in equilibrium stretching of long DNA sequences. We also compare the reconstructed potential with the thermodynamic parameters of DNAmore » duplex unbinding and show that the reconstruction accurately predicts duplex melting enthalpy.« less

A decision tree-based combination of ezrin-interacting proteins to estimate the prognostic risk of patients with esophageal squamous cell carcinoma.

PubMed

He, Jian-Zhong; Wu, Zhi-Yong; Wang, Shao-Hong; Ji, Xia; Yang, Cui-Xia; Xu, Xiu-E; Liao, Lian-Di; Wu, Jian-Yi; Li, En-Min; Zhang, Kai; Xu, Li-Yan

2017-08-01

Our previous studies have highlighted the importance of ezrin in esophageal squamous cell carcinoma (ESCC). Here our objective was to explore the clinical significance of ezrin-interacting proteins, which would provide a theoretical basis for understanding the function of ezrin and potential therapeutic targets for ESCC. We used affinity purification and mass spectrometry to identify PDIA3, CNPY2, and STMN1 as potential ezrin-interacting proteins. Confocal microscopy and coimmunoprecipitation analysis further confirmed the colocalization and interaction of ezrin with PDIA3, CNPY2, and STMN1. Tissue microarray data of ESCC samples (n=263) showed that the 5-year overall survival (OS) and disease-free survival (DFS) were significantly lower for the CNPY2 (OS, P=.003; DFS, P=.011) and STMN1 (OS, P=.010; DFS, P=.002) high-expression groups compared with the low-expression groups. By contrast, overexpression of PDIA3 was significantly correlated with favorable survival (OS, P<.001; DFS, P=.001). Cox regression demonstrated the prognostic value of PDIA3, CNPY2, and STMN1 in ESCC. Furthermore, decision tree analysis revealed that the resulting classifier of both ezrin and its interacting proteins could be used to better predict OS and DFS of patients with ESCC. In conclusion, a signature of ezrin-interacting proteins accurately predicts ESCC patient survival or tumor recurrence. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

PubMed

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.

Time-dependent inhibition (TDI) of CYP3A4 and CYP2C9 by noscapine potentially explains clinical noscapine-warfarin interaction.

PubMed

Fang, Zhong-Ze; Zhang, Yan-Yan; Ge, Guang-Bo; Huo, Hong; Liang, Si-Cheng; Yang, Ling

2010-02-01

To investigate the inhibition potential and kinetic information of noscapine to seven CYP isoforms and extrapolate in vivo noscapine-warfarin interaction magnitude from in vitro data. The activities of seven CYP isoforms (CYP3A4, CYP1A2, CYP2A6, CYP2E1, CYP2D6, CYP2C9, CYP2C8) in human liver microsomes were investigated following co- or preincubation with noscapine. A two-step incubation method was used to examine in vitro time-dependent inhibition (TDI) of noscapine. Reversible and TDI prediction equations were employed to extrapolate in vivo noscapine-warfarin interaction magnitude from in vitro data. Among seven CYP isoforms tested, the activities of CYP3A4 and CYP2C9 were strongly inhibited with an IC(50) of 10.8 +/- 2.5 microm and 13.3 +/- 1.2 microm. Kinetic analysis showed that inhibition of CYP2C9 by noscapine was best fit to a noncompetitive type with K(i) value of 8.8 microm, while inhibition of CYP3A4 by noscapine was best fit to a competitive manner with K(i) value of 5.2 microm. Noscapine also exhibited TDI to CYP3A4 and CYP2C9. The inactivation parameters (K(I) and k(inact)) were calculated to be 9.3 microm and 0.06 min(-1) for CYP3A4 and 8.9 microm and 0.014 min(-1) for CYP2C9, respectively. The AUC of (S)-warfarin and (R)-warfarin was predicted to increase 1.5% and 1.1% using C(max) or 0.5% and 0.4% using unbound C(max) with reversible inhibition prediction equation, while the AUC of (S)-warfarin and (R)-warfarin was estimated to increase by 110.9% and 48.9% using C(max) or 41.8% and 32.7% using unbound C(max) with TDI prediction equation. TDI of CYP3A4 and CYP2C9 by noscapine potentially explains clinical noscapine-warfarin interaction.

Interaction of the geomagnetic field with northward interplanetary magnetic field

NASA Astrophysics Data System (ADS)

Bhattarai, Shree Krishna

The interaction of the solar wind with Earth's magnetic field causes the transfer of momentum and energy from the solar wind to geospace. The study of this interaction is gaining significance as our society is becoming more and more space based, due to which, predicting space weather has become more important. The solar wind interacts with the geomagnetic field primarily via two processes: viscous interaction and the magnetic reconnection. Both of these interactions result in the generation of an electric field in Earth's ionosphere. The overall topology and dynamics of the magnetosphere, as well as the electric field imposed on the ionosphere, vary with speed, density, and magnetic field orientation of the solar wind as well as the conductivity of the ionosphere. In this dissertation, I will examine the role of northward interplanetary magnetic field (IMF) and discuss the global topology of the magnetosphere and the interaction with the ionosphere using results obtained from the Lyon-Fedder-Mobarry (LFM) simulation. The electric potentials imposed on the ionosphere due to viscous interaction and magnetic reconnection are called the viscous and the reconnection potentials, respectively. A proxy to measure the overall effect of these potentials is to measure the cross polar potential (CPP). The CPP is defined as the difference between the maximum and the minimum of the potential in a given polar ionosphere. I will show results from the LFM simulation showing saturation of the CPP during periods with purely northward IMF of sufficiently large magnitude. I will further show that the viscous potential, which was assumed to be independent of IMF orientation until this work, is reduced during periods of northward IMF. Furthermore, I will also discuss the implications of these results for a simulation of an entire solar rotation.

Pair Interaction of Dislocations in Two-Dimensional Crystals

NASA Astrophysics Data System (ADS)

Eisenmann, C.; Gasser, U.; Keim, P.; Maret, G.; von Grünberg, H. H.

2005-10-01

The pair interaction between crystal dislocations is systematically explored by analyzing particle trajectories of two-dimensional colloidal crystals measured by video microscopy. The resulting pair energies are compared to Monte Carlo data and to predictions derived from the standard Hamiltonian of the elastic theory of dislocations. Good agreement is found with respect to the distance and temperature dependence of the interaction potential, but not regarding the angle dependence where discrete lattice effects become important. Our results on the whole confirm that the dislocation Hamiltonian allows a quantitative understanding of the formation and interaction energies of dislocations in two-dimensional crystals.

Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures

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

Dunn, Nicholas J. H.; Noid, W. G., E-mail: wnoid@chem.psu.edu

This work investigates the promise of a “bottom-up” extended ensemble framework for developing coarse-grained (CG) models that provide predictive accuracy and transferability for describing both structural and thermodynamic properties. We employ a force-matching variational principle to determine system-independent, i.e., transferable, interaction potentials that optimally model the interactions in five distinct heptane-toluene mixtures. Similarly, we employ a self-consistent pressure-matching approach to determine a system-specific pressure correction for each mixture. The resulting CG potentials accurately reproduce the site-site rdfs, the volume fluctuations, and the pressure equations of state that are determined by all-atom (AA) models for the five mixtures. Furthermore, we demonstratemore » that these CG potentials provide similar accuracy for additional heptane-toluene mixtures that were not included their parameterization. Surprisingly, the extended ensemble approach improves not only the transferability but also the accuracy of the calculated potentials. Additionally, we observe that the required pressure corrections strongly correlate with the intermolecular cohesion of the system-specific CG potentials. Moreover, this cohesion correlates with the relative “structure” within the corresponding mapped AA ensemble. Finally, the appendix demonstrates that the self-consistent pressure-matching approach corresponds to minimizing an appropriate relative entropy.« less

Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures.

PubMed

Dunn, Nicholas J H; Noid, W G

2016-05-28

This work investigates the promise of a "bottom-up" extended ensemble framework for developing coarse-grained (CG) models that provide predictive accuracy and transferability for describing both structural and thermodynamic properties. We employ a force-matching variational principle to determine system-independent, i.e., transferable, interaction potentials that optimally model the interactions in five distinct heptane-toluene mixtures. Similarly, we employ a self-consistent pressure-matching approach to determine a system-specific pressure correction for each mixture. The resulting CG potentials accurately reproduce the site-site rdfs, the volume fluctuations, and the pressure equations of state that are determined by all-atom (AA) models for the five mixtures. Furthermore, we demonstrate that these CG potentials provide similar accuracy for additional heptane-toluene mixtures that were not included their parameterization. Surprisingly, the extended ensemble approach improves not only the transferability but also the accuracy of the calculated potentials. Additionally, we observe that the required pressure corrections strongly correlate with the intermolecular cohesion of the system-specific CG potentials. Moreover, this cohesion correlates with the relative "structure" within the corresponding mapped AA ensemble. Finally, the appendix demonstrates that the self-consistent pressure-matching approach corresponds to minimizing an appropriate relative entropy.

Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides.

PubMed

Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A

2018-06-07

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor and principal regulator of the antioxidant pathway. The Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) binds to motifs in the N-terminal region of Nrf2, promoting its degradation. There is interest in developing ligands that can compete with Nrf2 for binding to Kelch, thereby activating its transcriptional activities and increasing antioxidant levels. Using experimental Δ G bind values of Kelch-binding motifs determined previously, a revised hydrophobicity-based model was developed for estimating Δ G bind from amino acid sequence and applied to rank potential uncharacterized Kelch-binding motifs identified from interaction databases and BLAST searches. Model predictions and molecular dynamics (MD) simulations suggested that full-length MAD2A binds Kelch more favorably than a high-affinity 20-mer Nrf2 E78P peptide, but that the motif in isolation is not a particularly strong binder. Endeavoring to develop shorter peptides for activating Nrf2, new designs were created based on the E78P peptide, some of which showed considerable propensity to form binding-competent structures in MD, and were predicted to interact with Kelch more favorably than the E78P peptide. The peptides could be promising new ligands for enhancing the oxidative stress response.

InterEvDock: a docking server to predict the structure of protein–protein interactions using evolutionary information

PubMed Central

Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

2016-01-01

The structural modeling of protein–protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/. PMID:27131368

A rice kinase-protein interaction map.

PubMed

Ding, Xiaodong; Richter, Todd; Chen, Mei; Fujii, Hiroaki; Seo, Young Su; Xie, Mingtang; Zheng, Xianwu; Kanrar, Siddhartha; Stevenson, Rebecca A; Dardick, Christopher; Li, Ying; Jiang, Hao; Zhang, Yan; Yu, Fahong; Bartley, Laura E; Chern, Mawsheng; Bart, Rebecca; Chen, Xiuhua; Zhu, Lihuang; Farmerie, William G; Gribskov, Michael; Zhu, Jian-Kang; Fromm, Michael E; Ronald, Pamela C; Song, Wen-Yuan

2009-03-01

Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

Descriptive Summaries of the Research, Development, Test and Evaluation, Army Appropriation. Supporting Data FY 1994, Budget Estimates Submitted to Congress, April 1993

DTIC Science & Technology

1993-04-01

determining effective group functioning, leader-group interaction , and decision making; (2) factors that determine effective, low error human performance...infectious disease and biological defense vaccines and drugs , vision, neurotxins, neurochemistry, molecular neurobiology, neurodegenrative diseases...Potential Rotor/Comprehensive Analysis Model for Rotor Aerodynamics-Johnson Aeronautics (FPR/CAMRAD-JA) code to predict Blade Vortex Interaction (BVI

On Predicting the Crystal Structure of Energetic Materials From Quantum Mechanics

DTIC Science & Technology

2008-12-01

DE ABSTRACT A quantum-mechanically-based potential energy function that describes interactions of dimers of the explosive ...method is capable of producing force fields for interactions of the molecular crystalline explosive RDX, and appears to be suitable to enable reliable...Ridge, TN. Byrd, E.F.C., Scuseria, G.E., Chabalowski, C.F., 2004: “An ab initio study of solid nitromethane , HMX, RDX and CL20: Successes and

Online Games as a Component of School Textbooks: A Test Predicting the Diffusion of Interactive Online Games Designed for the Textbook Reformation in South Korea

ERIC Educational Resources Information Center

Kim, Do Kyun; Dinu, Lucian F.; Chung, Wonjon

2013-01-01

Currently, the South Korean government is in the process of transforming school textbooks from a paper-based platform to a computer-based digital platform. Along with this effort, interactive online educational games (edu-games) have been examined as a potential component of the digital textbooks. Based on the theory of diffusion of innovations,…

Computational identification of post-translational modification-based nuclear import regulations by characterizing nuclear localization signal-import receptor interaction.

PubMed

Lin, Jhih-Rong; Liu, Zhonghao; Hu, Jianjun

2014-10-01

The binding affinity between a nuclear localization signal (NLS) and its import receptor is closely related to corresponding nuclear import activity. PTM-based modulation of the NLS binding affinity to the import receptor is one of the most understood mechanisms to regulate nuclear import of proteins. However, identification of such regulation mechanisms is challenging due to the difficulty of assessing the impact of PTM on corresponding nuclear import activities. In this study we proposed NIpredict, an effective algorithm to predict nuclear import activity given its NLS, in which molecular interaction energy components (MIECs) were used to characterize the NLS-import receptor interaction, and the support vector regression machine (SVR) was used to learn the relationship between the characterized NLS-import receptor interaction and the corresponding nuclear import activity. Our experiments showed that nuclear import activity change due to NLS change could be accurately predicted by the NIpredict algorithm. Based on NIpredict, we developed a systematic framework to identify potential PTM-based nuclear import regulations for human and yeast nuclear proteins. Application of this approach has identified the potential nuclear import regulation mechanisms by phosphorylation of two nuclear proteins including SF1 and ORC6. © 2014 Wiley Periodicals, Inc.

Interactive effects of global climate change and pollution on marine microbes: the way ahead.

PubMed

Coelho, Francisco J R C; Santos, Ana L; Coimbra, Joana; Almeida, Adelaide; Cunha, Angela; Cleary, Daniel F R; Calado, Ricardo; Gomes, Newton C M

2013-06-01

Global climate change has the potential to seriously and adversely affect marine ecosystem functioning. Numerous experimental and modeling studies have demonstrated how predicted ocean acidification and increased ultraviolet radiation (UVR) can affect marine microbes. However, researchers have largely ignored interactions between ocean acidification, increased UVR and anthropogenic pollutants in marine environments. Such interactions can alter chemical speciation and the bioavailability of several organic and inorganic pollutants with potentially deleterious effects, such as modifying microbial-mediated detoxification processes. Microbes mediate major biogeochemical cycles, providing fundamental ecosystems services such as environmental detoxification and recovery. It is, therefore, important that we understand how predicted changes to oceanic pH, UVR, and temperature will affect microbial pollutant detoxification processes in marine ecosystems. The intrinsic characteristics of microbes, such as their short generation time, small size, and functional role in biogeochemical cycles combined with recent advances in molecular techniques (e.g., metagenomics and metatranscriptomics) make microbes excellent models to evaluate the consequences of various climate change scenarios on detoxification processes in marine ecosystems. In this review, we highlight the importance of microbial microcosm experiments, coupled with high-resolution molecular biology techniques, to provide a critical experimental framework to start understanding how climate change, anthropogenic pollution, and microbiological interactions may affect marine ecosystems in the future.

Interactive effects of global climate change and pollution on marine microbes: the way ahead

PubMed Central

Coelho, Francisco J R C; Santos, Ana L; Coimbra, Joana; Almeida, Adelaide; Cunha, Ângela; Cleary, Daniel F R; Calado, Ricardo; Gomes, Newton C M

2013-01-01

Global climate change has the potential to seriously and adversely affect marine ecosystem functioning. Numerous experimental and modeling studies have demonstrated how predicted ocean acidification and increased ultraviolet radiation (UVR) can affect marine microbes. However, researchers have largely ignored interactions between ocean acidification, increased UVR and anthropogenic pollutants in marine environments. Such interactions can alter chemical speciation and the bioavailability of several organic and inorganic pollutants with potentially deleterious effects, such as modifying microbial-mediated detoxification processes. Microbes mediate major biogeochemical cycles, providing fundamental ecosystems services such as environmental detoxification and recovery. It is, therefore, important that we understand how predicted changes to oceanic pH, UVR, and temperature will affect microbial pollutant detoxification processes in marine ecosystems. The intrinsic characteristics of microbes, such as their short generation time, small size, and functional role in biogeochemical cycles combined with recent advances in molecular techniques (e.g., metagenomics and metatranscriptomics) make microbes excellent models to evaluate the consequences of various climate change scenarios on detoxification processes in marine ecosystems. In this review, we highlight the importance of microbial microcosm experiments, coupled with high-resolution molecular biology techniques, to provide a critical experimental framework to start understanding how climate change, anthropogenic pollution, and microbiological interactions may affect marine ecosystems in the future. PMID:23789087

Analysis of ISS Plasma Interaction

NASA Technical Reports Server (NTRS)

Reddell, Brandon; Alred, John; Kramer, Leonard; Mikatarian, Ron; Minow, Joe; Koontz, Steve

2006-01-01

To date, the International Space Station (ISS) has been one of the largest objects flown in lower earth orbit (LEO). The ISS utilizes high voltage solar arrays (160V) that are negatively grounded leading to pressurized elements that can float negatively with respect to the plasma. Because laboratory measurements indicate a dielectric breakdown potential difference of 80V, arcing could occur on the ISS structure. To overcome the possibility of arcing and clamp the potential of the structure, two Plasma Contactor Units (PCUs) were designed, built, and flown. Also a limited amount of measurements of the floating potential for the present ISS configuration were made by a Floating Potential Probe (FPP), indicating a minimum potential of 24 Volts at the measurement location. A predictive tool, the ISS Plasma Interaction Model (PIM) has been developed accounting for the solar array electron collection, solar array mast wire and effective conductive area on the structure. The model has been used for predictions of the present ISS configuration. The conductive area has been inferred based on available floating potential measurements. Analysis of FPP and PCU data indicated distribution of the conductive area along the Russian segment of the ISS structure. A significant input to PIM is the plasma environment. The International Reference Ionosphere (IRI 2001) was initially used to obtain plasma temperature and density values. However, IRI provides mean parameters, leading to difficulties in interpretation of on-orbit data, especially at eclipse exit where maximum charging can occur. This limits our predicative capability. Satellite and Incoherent Scatter Radar (ISR) data of plasma parameters have also been collected. Approximately 130,000 electron temperature (Te) and density (Ne) pairs for typical ISS eclipse exit conditions have been extracted from the reduced Langmuir probe data flown aboard the NASA DE-2 satellite. Additionally, another 18,000 Te and Ne pairs of ISR data from several radar locations around the globe were used to assure consistency of the satellite data. PIM predictions for ISS charging made with this data correlated very well with FPP data, indicating that the general physics of spacecraft charging with high voltage solar arrays have been captured. The predictions also provided the probabilities of occurrences for ISS charging. These probabilities give a numerical measure of the number of times when the ISS will approach or exceed the vehicle plasma hazard conditions for each configuration. In this paper we shall present the interaction mechanisms between the ISS and the surrounding plasma and give an overview of the PIM components. PIM predictions are compared with available data followed by a discussion of the variability of plasma parameters and the conductive area on the ISS. The ISS PIM will be further tested and verified as data from the Floating Potential Measurement Unit become available, and construction of the ISS continues.

Interactions between Callous Unemotional Behaviors and Executive Function in Early Childhood Predict later Aggression and Lower Peer-liking in Late-childhood.

PubMed

Waller, Rebecca; Hyde, Luke W; Baskin-Sommers, Arielle R; Olson, Sheryl L

2017-04-01

Callous unemotional (CU) behaviors are linked to aggression, behavior problems, and difficulties in peer relationships in children and adolescents. However, few studies have examined whether early childhood CU behaviors predict aggression or peer-rejection during late-childhood or potential moderation of this relationship by executive function. The current study examined whether the interaction of CU behaviors and executive function in early childhood predicted different forms of aggression in late-childhood, including proactive, reactive, and relational aggression, as well as how much children were liked by their peers. Data from cross-informant reports and multiple observational tasks were collected from a high-risk sample (N = 240; female = 118) at ages 3 and 10 years old. Parent reports of CU behaviors at age 3 predicted teacher reports of reactive, proactive, and relational aggression, as well as lower peer-liking at age 10. Moderation analysis showed that specifically at high levels of CU behaviors and low levels of observed executive function, children were reported by teachers as showing greater reactive and proactive aggression, and were less-liked by peers. Findings demonstrate that early childhood CU behaviors and executive function have unique main and interactive effects on both later aggression and lower peer-liking even when taking into account stability in behavior problems over time. By elucidating how CU behaviors and deficits in executive function potentiate each other during early childhood, we can better characterize the emergence of severe and persistent behavior and interpersonal difficulties across development.

Geospatial interface and model for predicting potential seagrass habitat

EPA Science Inventory

Restoration of ecosystem services provided by seagrass habitats in estuaries requires a clear understanding of the modes of action of multiple interacting stressors including nutrients, climate change, coastal land-use change, and habitat modification. We have developed a geos...

20170312 - In Silico Dynamics: computer simulation in a ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or bioche

In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

Large-scale De Novo Prediction of Physical Protein-Protein Association*

PubMed Central

Elefsinioti, Antigoni; Saraç, Ömer Sinan; Hegele, Anna; Plake, Conrad; Hubner, Nina C.; Poser, Ina; Sarov, Mihail; Hyman, Anthony; Mann, Matthias; Schroeder, Michael; Stelzl, Ulrich; Beyer, Andreas

2011-01-01

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org. PMID:21836163

microPIR2: a comprehensive database for human–mouse comparative study of microRNA–promoter interactions

PubMed Central

Piriyapongsa, Jittima; Bootchai, Chaiwat; Ngamphiw, Chumpol; Tongsima, Sissades

2014-01-01

microRNA (miRNA)–promoter interaction resource (microPIR) is a public database containing over 15 million predicted miRNA target sites located within human promoter sequences. These predicted targets are presented along with their related genomic and experimental data, making the microPIR database the most comprehensive repository of miRNA promoter target sites. Here, we describe major updates of the microPIR database including new target predictions in the mouse genome and revised human target predictions. The updated database (microPIR2) now provides ∼80 million human and 40 million mouse predicted target sites. In addition to being a reference database, microPIR2 is a tool for comparative analysis of target sites on the promoters of human–mouse orthologous genes. In particular, this new feature was designed to identify potential miRNA–promoter interactions conserved between species that could be stronger candidates for further experimental validation. We also incorporated additional supporting information to microPIR2 such as nuclear and cytoplasmic localization of miRNAs and miRNA–disease association. Extra search features were also implemented to enable various investigations of targets of interest. Database URL: http://www4a.biotec.or.th/micropir2 PMID:25425035

A novel knowledge-based potential for RNA 3D structure evaluation

NASA Astrophysics Data System (ADS)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

PubMed Central

Hsing, Michael; Byler, Kendall; Cherkasov, Artem

2009-01-01

Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Common features of microRNA target prediction tools

PubMed Central

Peterson, Sarah M.; Thompson, Jeffrey A.; Ufkin, Melanie L.; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output. PMID:24600468

Common features of microRNA target prediction tools.

PubMed

Peterson, Sarah M; Thompson, Jeffrey A; Ufkin, Melanie L; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Lithium-manganese dioxide cells for implantable defibrillator devices-Discharge voltage models

NASA Astrophysics Data System (ADS)

Root, Michael J.

The discharge potential behavior of lithium-manganese dioxide cells designed for implantable cardiac defibrillators was characterized as a function of extent of cell depletion for tests designed to discharge the cells for times between 1 and 7 years. The discharge potential curves may be separated into two segments from 0 ≤ x ≤ ∼0.51 and ∼0.51 ≤ x ≤ 1.00, where x is the dimensionless extent of discharge referenced to the rated cell capacity. The discharge potentials conform to Tafel kinetics in each segment. This behavior allows the discharge potential curves to be predicted for an arbitrary discharge load and long term discharge performance may be predicted from short term test results. The discharge potentials may subsequently be modeled by fitting the discharge curves to empirical functions like polynomials and Padé approximants. A function based on the Nernst equation that includes a term accounting for nonideal interactions between lithium ions and the cathode host material, such as the Redlich-Kister relationship, also may be used to predict discharge behavior.

Influence of nonelectrostatic ion-ion interactions on double-layer capacitance

NASA Astrophysics Data System (ADS)

Zhao, Hui

2012-11-01

Recently a Poisson-Helmholtz-Boltzmann (PHB) model [Bohinc , Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.85.031130 85, 031130 (2012)] was developed by accounting for solvent-mediated nonelectrostatic ion-ion interactions. Nonelectrostatic interactions are described by a Yukawa-like pair potential. In the present work, we modify the PHB model by adding steric effects (finite ion size) into the free energy to derive governing equations. The modified PHB model is capable of capturing both ion specificity and ion crowding. This modified model is then employed to study the capacitance of the double layer. More specifically, we focus on the influence of nonelectrostatic ion-ion interactions on charging a double layer near a flat surface in the presence of steric effects. We numerically compute the differential capacitance as a function of the voltage under various conditions. At small voltages and low salt concentrations (dilute solution), we find out that the predictions from the modified PHB model are the same as those from the classical Poisson-Boltzmann theory, indicating that nonelectrostatic ion-ion interactions and steric effects are negligible. At moderate voltages, nonelectrostatic ion-ion interactions play an important role in determining the differential capacitance. Generally speaking, nonelectrostatic interactions decrease the capacitance because of additional nonelectrostatic repulsion among excess counterions inside the double layer. However, increasing the voltage gradually favors steric effects, which induce a condensed layer with crowding of counterions near the electrode. Accordingly, the predictions from the modified PHB model collapse onto those computed by the modified Poisson-Boltzmann theory considering steric effects alone. Finally, theoretical predictions are compared and favorably agree with experimental data, in particular, in concentrated solutions, leading one to conclude that the modified PHB model adequately predicts the diffuse-charge dynamics of the double layer with ion specificity and steric effects.

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

PubMed

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

A Systematic Approach to Evaluate Herb-Drug Interaction Mechanisms: Investigation of Milk Thistle Extracts and Eight Isolated Constituents as CYP3A Inhibitors

PubMed Central

Brantley, Scott J.; Graf, Tyler N.; Oberlies, Nicholas H.

2013-01-01

Despite increasing recognition of potential untoward interactions between herbal products and conventional medications, a standard system for prospective assessment of these interactions remains elusive. This information gap was addressed by evaluating the drug interaction liability of the model herbal product milk thistle (Silybum marianum) with the CYP3A probe substrate midazolam. The inhibitory effects of commercially available milk thistle extracts and isolated constituents on midazolam 1′-hydroxylation were screened using human liver and intestinal microsomes. Relative to vehicle, the extract silymarin and constituents silybin A, isosilybin A, isosilybin B, and silychristin at 100 μM demonstrated >50% inhibition of CYP3A activity with at least one microsomal preparation, prompting IC50 determination. The IC50s for isosilybin B and silychristin were ∼60 and 90 μM, respectively, whereas those for the remaining constituents were >100 μM. Extracts and constituents that contained the 1,4-dioxane moiety demonstrated a >1.5-fold shift in IC50 when tested as potential mechanism-based inhibitors. The semipurified extract, silibinin, and the two associated constituents (silybin A and silybin B) demonstrated mechanism-based inhibition of recombinant CYP3A4 (KI, ∼100 μM; kinact, ∼0.20 min−1) but not microsomal CYP3A activity. The maximum predicted increases in midazolam area under the curve using the static mechanistic equation and recombinant CYP3A4 data were 1.75-fold, which may necessitate clinical assessment. Evaluation of the interaction liability of single herbal product constituents, in addition to commercially available extracts, will enable elucidation of mechanisms underlying potential clinically significant herb-drug interactions. Application of this framework to other herbal products would permit predictions of herb-drug interactions and assist in prioritizing clinical evaluation. PMID:23801821

A systematic approach to evaluate herb-drug interaction mechanisms: investigation of milk thistle extracts and eight isolated constituents as CYP3A inhibitors.

PubMed

Brantley, Scott J; Graf, Tyler N; Oberlies, Nicholas H; Paine, Mary F

2013-09-01

Despite increasing recognition of potential untoward interactions between herbal products and conventional medications, a standard system for prospective assessment of these interactions remains elusive. This information gap was addressed by evaluating the drug interaction liability of the model herbal product milk thistle (Silybum marianum) with the CYP3A probe substrate midazolam. The inhibitory effects of commercially available milk thistle extracts and isolated constituents on midazolam 1'-hydroxylation were screened using human liver and intestinal microsomes. Relative to vehicle, the extract silymarin and constituents silybin A, isosilybin A, isosilybin B, and silychristin at 100 μM demonstrated >50% inhibition of CYP3A activity with at least one microsomal preparation, prompting IC50 determination. The IC50s for isosilybin B and silychristin were ∼60 and 90 μM, respectively, whereas those for the remaining constituents were >100 μM. Extracts and constituents that contained the 1,4-dioxane moiety demonstrated a >1.5-fold shift in IC50 when tested as potential mechanism-based inhibitors. The semipurified extract, silibinin, and the two associated constituents (silybin A and silybin B) demonstrated mechanism-based inhibition of recombinant CYP3A4 (KI, ∼100 μM; kinact, ∼0.20 min(-1)) but not microsomal CYP3A activity. The maximum predicted increases in midazolam area under the curve using the static mechanistic equation and recombinant CYP3A4 data were 1.75-fold, which may necessitate clinical assessment. Evaluation of the interaction liability of single herbal product constituents, in addition to commercially available extracts, will enable elucidation of mechanisms underlying potential clinically significant herb-drug interactions. Application of this framework to other herbal products would permit predictions of herb-drug interactions and assist in prioritizing clinical evaluation.

Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

PubMed

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

2017-04-01

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

The Interaction between Interoceptive and Action States within a Framework of Predictive Coding

PubMed Central

Marshall, Amanda C.; Gentsch, Antje; Schütz-Bosbach, Simone

2018-01-01

The notion of predictive coding assumes that perception is an iterative process between prior knowledge and sensory feedback. To date, this perspective has been primarily applied to exteroceptive perception as well as action and its associated phenomenological experiences such as agency. More recently, this predictive, inferential framework has been theoretically extended to interoception. This idea postulates that subjective feeling states are generated by top–down inferences made about internal and external causes of interoceptive afferents. While the processing of motor signals for action control and the emergence of selfhood have been studied extensively, the contributions of interoceptive input and especially the potential interaction of motor and interoceptive signals remain largely unaddressed. Here, we argue for a specific functional relation between motor and interoceptive awareness. Specifically, we implicate interoceptive predictions in the generation of subjective motor-related feeling states. Furthermore, we propose a distinction between reflexive and pre-reflexive modes of agentic action control and suggest that interoceptive input may affect each differently. Finally, we advocate the necessity of continuous interoceptive input for conscious forms of agentic action control. We conclude by discussing further research contributions that would allow for a fuller understanding of the interaction between agency and interoceptive awareness. PMID:29515495

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

PubMed Central

Xia, Kai; Dong, Dong; Han, Jing-Dong J

2006-01-01

Background Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer. PMID:17112386

Infrared spectroscopy and molecular simulations of a polymeric sorbent and its enantioselective interactions with benzoin enantiomers.

PubMed

Tsui, Hung-Wei; Willing, Jonathan N; Kasat, Rahul B; Wang, Nien-Hwa Linda; Franses, Elias I

2011-11-10

Retention factors, k(R) and k(S), and enantioselectivities, S ≡ k(R)/k(S), of amylose tris[(S)-α-methylbenzylcarbamate] (AS) sorbent for benzoin (B) enantiomers were measured for various isopropyl alcohol (IPA)/n-hexane compositions of the high-performance liquid chromatography (HPLC) mobile phase. Novel data for pure n-hexane show that k(R) = 106, k(S) = 49.6, and S = 2.13. With some IPA from 0.5 to 10 vol %, with S = 1.8-1.4, the retention factors were smaller. Infrared spectra showed evidence of substantial hydrogen bonding (H-bonding) interactions in the pure polymer phase and additional H-bonding interactions between AS and benzoin. Density functional theory (DFT) was used to model the chain-chain and chain-benzoin H-bonding and other interactions. DFT was also used to predict fairly well the IR wavenumber shifts caused by the H-bonds. DFT simulations of IR bands of NH and C═O allowed for the first time the predictions of relative intensities and relative populations of H-bonding strengths. Molecular dynamics (MD) simulations were used to model a single 12-mer polymer chain. MD simulations predicted the existence of various potentially enantioselective cavities, two of which are sufficiently large to accommodate a benzoin molecule. Then "docking" studies of benzoin in AS with MD, Monte Carlo (MC), and MC/MD simulations were done to probe the AS-B interactions. The observed enantioselectivities are predicted to be primarily due to two H-bonds, of the kind AS CO···HO (R)-benzoin and AS NH···OC (R)-benzoin, and two π-π (phenyl-phenyl) interactions for (R)-benzoin and one H-bond, of type AS CO···HO (S)-benzoin, and one π-π interaction for (S)-benzoin. The MC/MD predictions are consistent with the HPLC and IR results.

Virus versus Host Plant MicroRNAs: Who Determines the Outcome of the Interaction?

PubMed Central

Maghuly, Fatemeh; Ramkat, Rose C.; Laimer, Margit

2014-01-01

Considering the importance of microRNAs (miRNAs) in the regulation of essential processes in plant pathogen interactions, it is not surprising that, while plant miRNA sequences counteract viral attack via antiviral RNA silencing, viruses in turn have developed antihost defense mechanisms blocking these RNA silencing pathways and establish a counter-defense. In the current study, computational and stem-loop Reverse Transcription – Polymerase Chain Reaction (RT-PCR) approaches were employed to a) predict and validate virus encoded mature miRNAs (miRs) in 39 DNA-A sequences of the bipartite genomes of African cassava mosaic virus (ACMV) and East African cassava mosaic virus-Uganda (EACMV-UG) isolates, b) determine whether virus encoded miRs/miRs* generated from the 5′/3′ harpin arms have the capacity to bind to genomic sequences of the host plants Jatropha or cassava and c) investigate whether plant encoded miR/miR* sequences have the potential to bind to the viral genomes. Different viral pre-miRNA hairpin sequences and viral miR/miR* length variants occurring as isomiRs were predicted in both viruses. These miRNAs were located in three Open Reading Frames (ORFs) and in the Intergenic Region (IR). Moreover, various target genes for miRNAs from both viruses were predicted and annotated in the host plant genomes indicating that they are involved in biotic response, metabolic pathways and transcription factors. Plant miRs/miRs* from conserved and highly expressed families were identified, which were shown to have potential targets in the genome of both begomoviruses, representing potential plant miRNAs mediating antiviral defense. This is the first assessment of predicted viral miRs/miRs* of ACMV and EACMV-UG and host plant miRNAs, providing a reference point for miRNA identification in pathogens and their hosts. These findings will improve the understanding of host- pathogen interaction pathways and the function of viral miRNAs in Euphorbiaceous crop plants. PMID:24896088

VLP Simulation: An Interactive Simple Virtual Model to Encourage Geoscience Skill about Volcano

NASA Astrophysics Data System (ADS)

Hariyono, E.; Liliasari; Tjasyono, B.; Rosdiana, D.

2017-09-01

The purpose of this study was to describe physics students predicting skills after following the geoscience learning using VLP (Volcano Learning Project) simulation. This research was conducted to 24 physics students at one of the state university in East Java-Indonesia. The method used is the descriptive analysis based on students’ answers related to predicting skills about volcanic activity. The results showed that the learning by using VLP simulation was very potential to develop physics students predicting skills. Students were able to explain logically about volcanic activity and they have been able to predict the potential eruption that will occur based on the real data visualization. It can be concluded that the VLP simulation is very suitable for physics student requirements in developing geosciences skill and recommended as an alternative media to educate the society in an understanding of volcanic phenomena.

Modeling potential hydrochemical responses to climate change and rising CO2 at the Hubbard Brook Experimental Forest using a dynamic biogeochemical model (PnET-BGC)

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe

2012-01-01

Dynamic hydrochemical models are useful tools for understanding and predicting the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. We used the biogeochemical model, PnET-BGC, to evaluate the effects of potential future changes in temperature,...

Climate-soil Interactions: Global Change, Local Properties, and Ecological Sites

USDA-ARS?s Scientific Manuscript database

Global climate change is predicted to alter historic patterns of precipitation and temperature in rangelands globally. Vegetation community response to altered weather patterns will be mediated at the site level by local-scale properties that govern ecological potential, including geology, topograph...

In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

PubMed

Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

2017-07-01

Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

PubMed

Moberg, Fallon B; Anestis, Michael D

2015-01-01

Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis.

PubMed

Cullis, B R; Smith, A B; Beeck, C P; Cowling, W A

2010-11-01

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

Electro-osmosis over inhomogeneously charged surfaces in presence of non-electrostatic ion-ion interactions

NASA Astrophysics Data System (ADS)

Ghosh, Uddipta; Chakraborty, Suman

2016-06-01

In this study, we attempt to bring out a generalized formulation for electro-osmotic flows over inhomogeneously charged surfaces in presence of non-electrostatic ion-ion interactions. To this end, we start with modified electro-chemical potential of the individual species and subsequently use it to derive modified Nernst-Planck equation accounting for the ionic fluxes generated because of the presence of non-electrostatic potential. We establish what we refer to as the Poisson-Helmholtz-Nernst-Planck equations, coupled with the Navier-Stokes equations, to describe the complete transport process. Our analysis shows that the presence of non-electrostatic interactions between the ions results in an excess body force on the fluid, and modifies the osmotic pressure as well, which has hitherto remained unexplored. We further apply our analysis to a simple geometry, in an effort to work out the Smoluchowski slip velocity for thin electrical double layer limits. To this end, we employ singular perturbation and develop a general framework for the asymptotic analysis. Our calculations reveal that the final expression for slip velocity remains the same as that without accounting for non-electrostatic interactions. However, the presence of non-electrostatic interactions along with ion specificity can significantly change the quantitative behavior of Smoluchowski slip velocity. We subsequently demonstrate that the presence of non-electrostatic interactions may significantly alter the effective interfacial potential, also termed as the "Zeta potential." Our analysis can potentially act as a guide towards the prediction and possibly quantitative determination of the implications associated with the existence of non-electrostatic potential, in an electrokinetic transport process.

Visual perception and regulatory conflict: motivation and physiology influence distance perception.

PubMed

Cole, Shana; Balcetis, Emily; Zhang, Sam

2013-02-01

Regulatory conflict can emerge when people experience a strong motivation to act on goals but a conflicting inclination to withhold action because physical resources available, or physiological potentials, are low. This study demonstrated that distance perception is biased in ways that theory suggests assists in managing this conflict. Participants estimated the distance to a target location. Individual differences in physiological potential measured via waist-to-hip ratio interacted with manipulated motivational states to predict visual perception. Among people low in physiological potential and likely to experience regulatory conflict, the environment appeared easier to traverse when motivation was strong compared with weak. Among people high in potential and less likely to experience conflict, perception was not predicted by motivational strength. The role of motivated distance perception in self-regulation is discussed. 2013 APA, all rights reserved

[Quantitative Prediction of Drug-Drug Interaction Caused by CYP Inhibition and Induction from In Vivo Data and Its Application in Daily Clinical Practices-Proposal for the Pharmacokinetic Interaction Significance Classification System (PISCS)].

PubMed

Ohno, Yoshiyuki

2018-01-01

　Drug-drug interactions (DDIs) can affect the clearance of various drugs from the body; however, these effects are difficult to sufficiently evaluate in clinical studies. This article outlines our approach to improving methods for evaluating and providing drug information relative to the effects of DDIs. In a previous study, total exposure changes to many substrate drugs of CYP caused by the co-administration of inhibitor or inducer drugs were successfully predicted using in vivo data. There are two parameters for the prediction: the contribution ratio of the enzyme to oral clearance for substrates (CR), and either the inhibition ratio for inhibitors (IR) or the increase in clearance of substrates produced by induction (IC). To apply these predictions in daily pharmacotherapy, the clinical significance of any pharmacokinetic changes must be carefully evaluated. We constructed a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered in a systematic manner, according to pharmacokinetic changes. The PISCS suggests that many current 'alert' classifications are potentially inappropriate, especially for drug combinations in which pharmacokinetics have not yet been evaluated. It is expected that PISCS would contribute to constructing a reliable system to alert pharmacists, physicians and consumers of a broad range of pharmacokinetic DDIs in order to more safely manage daily clinical practices.

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

PubMed Central

Brender, Jeffrey R.; Zhang, Yang

2015-01-01

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. PMID:26506533

Cognitive control over learning: Creating, clustering and generalizing task-set structure

PubMed Central

Collins, Anne G.E.; Frank, Michael J.

2013-01-01

Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID:23356780

Experimenting with ecosystem interaction networks in search of threshold potentials in real-world marine ecosystems.

PubMed

Thrush, Simon F; Hewitt, Judi E; Parkes, Samantha; Lohrer, Andrew M; Pilditch, Conrad; Woodin, Sarah A; Wethey, David S; Chiantore, Mariachiara; Asnaghi, Valentina; De Juan, Silvia; Kraan, Casper; Rodil, Ivan; Savage, Candida; Van Colen, Carl

2014-06-01

Thresholds profoundly affect our understanding and management of ecosystem dynamics, but we have yet to develop practical techniques to assess the risk that thresholds will be crossed. Combining ecological knowledge of critical system interdependencies with a large-scale experiment, we tested for breaks in the ecosystem interaction network to identify threshold potential in real-world ecosystem dynamics. Our experiment with the bivalves Macomona liliana and Austrovenus stutchburyi on marine sandflats in New Zealand demonstrated that reductions in incident sunlight changed the interaction network between sediment biogeochemical fluxes, productivity, and macrofauna. By demonstrating loss of positive feedbacks and changes in the architecture of the network, we provide mechanistic evidence that stressors lead to break points in dynamics, which theory predicts predispose a system to a critical transition.

A Simulated Environment Experiment on Annoyance Due to Combined Road Traffic and Industrial Noises.

PubMed

Marquis-Favre, Catherine; Morel, Julien

2015-07-21

Total annoyance due to combined noises is still difficult to predict adequately. This scientific gap is an obstacle for noise action planning, especially in urban areas where inhabitants are usually exposed to high noise levels from multiple sources. In this context, this work aims to highlight potential to enhance the prediction of total annoyance. The work is based on a simulated environment experiment where participants performed activities in a living room while exposed to combined road traffic and industrial noises. The first objective of the experiment presented in this paper was to gain further understanding of the effects on annoyance of some acoustical factors, non-acoustical factors and potential interactions between the combined noise sources. The second one was to assess total annoyance models constructed from the data collected during the experiment and tested using data gathered in situ. The results obtained in this work highlighted the superiority of perceptual models. In particular, perceptual models with an interaction term seemed to be the best predictors for the two combined noise sources under study, even with high differences in sound pressure level. Thus, these results reinforced the need to focus on perceptual models and to improve the prediction of partial annoyances.

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

PubMed

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics.

PubMed

Graham, Sarah E; Smith, Richard D; Carlson, Heather A

2018-02-26

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

Heat conduction in a chain of colliding particles with a stiff repulsive potential

NASA Astrophysics Data System (ADS)

Gendelman, Oleg V.; Savin, Alexander V.

2016-11-01

One-dimensional billiards, i.e., a chain of colliding particles with equal masses, is a well-known example of a completely integrable system. Billiards with different particle masses is generically not integrable, but it still exhibits divergence of a heat conduction coefficient (HCC) in the thermodynamic limit. Traditional billiards models imply instantaneous (zero-time) collisions between the particles. We relax this condition of instantaneous impact and consider heat transport in a chain of stiff colliding particles with the power-law potential of the nearest-neighbor interaction. The instantaneous collisions correspond to the limit of infinite power in the interaction potential; for finite powers, the interactions take nonzero time. This modification of the model leads to a profound physical consequence—the probability of multiple (in particular triple) -particle collisions becomes nonzero. Contrary to the integrable billiards of equal particles, the modified model exhibits saturation of the heat conduction coefficient for a large system size. Moreover, the identification of scattering events with triple-particle collisions leads to a simple definition of the characteristic mean free path and a kinetic description of heat transport. This approach allows us to predict both the temperature and density dependencies for the HCC limit values. The latter dependence is quite counterintuitive—the HCC is inversely proportional to the particle density in the chain. Both predictions are confirmed by direct numerical simulations.

Power law tails in phylogenetic systems.

PubMed

Qin, Chongli; Colwell, Lucy J

2018-01-23

Covariance analysis of protein sequence alignments uses coevolving pairs of sequence positions to predict features of protein structure and function. However, current methods ignore the phylogenetic relationships between sequences, potentially corrupting the identification of covarying positions. Here, we use random matrix theory to demonstrate the existence of a power law tail that distinguishes the spectrum of covariance caused by phylogeny from that caused by structural interactions. The power law is essentially independent of the phylogenetic tree topology, depending on just two parameters-the sequence length and the average branch length. We demonstrate that these power law tails are ubiquitous in the large protein sequence alignments used to predict contacts in 3D structure, as predicted by our theory. This suggests that to decouple phylogenetic effects from the interactions between sequence distal sites that control biological function, it is necessary to remove or down-weight the eigenvectors of the covariance matrix with largest eigenvalues. We confirm that truncating these eigenvectors improves contact prediction.

Self-Consistent Monte Carlo Study of the Coulomb Interaction under Nano-Scale Device Structures

NASA Astrophysics Data System (ADS)

Sano, Nobuyuki

2011-03-01

It has been pointed that the Coulomb interaction between the electrons is expected to be of crucial importance to predict reliable device characteristics. In particular, the device performance is greatly degraded due to the plasmon excitation represented by dynamical potential fluctuations in high-doped source and drain regions by the channel electrons. We employ the self-consistent 3D Monte Carlo (MC) simulations, which could reproduce both the correct mobility under various electron concentrations and the collective plasma waves, to study the physical impact of dynamical potential fluctuations on device performance under the Double-gate MOSFETs. The average force experienced by an electron due to the Coulomb interaction inside the device is evaluated by performing the self-consistent MC simulations and the fixed-potential MC simulations without the Coulomb interaction. Also, the band-tailing associated with the local potential fluctuations in high-doped source region is quantitatively evaluated and it is found that the band-tailing becomes strongly dependent of position in real space even inside the uniform source region. This work was partially supported by Grants-in-Aid for Scientific Research B (No. 2160160) from the Ministry of Education, Culture, Sports, Science and Technology in Japan.

A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions

PubMed Central

Hostetler, Jessica B.; Sharma, Sumana; Bartholdson, S. Josefin; Wright, Gavin J.; Fairhurst, Rick M.; Rayner, Julian C.

2015-01-01

Background A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP) and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC), and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion. Methodology/Principal Findings We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further suggesting that the proteins are natively folded and functional. This screen also identified two novel protein-protein interactions, between P12 and PVX_110945, and between MSP3.10 and MSP7.1, the latter of which was confirmed by surface plasmon resonance. Conclusions/Significance We produced a new library of recombinant full-length P. vivax ectodomains, established that the majority of them contain tertiary structure, and used them to identify predicted and novel protein-protein interactions. As well as identifying new interactions for further biological studies, this library will be useful in identifying P. vivax proteins with vaccine potential, and studying P. vivax malaria pathogenesis and immunity. Trial Registration ClinicalTrials.gov NCT00663546 PMID:26701602

Counter ion induced irreversible denaturation of hen egg white lysozyme upon electrostatic interaction with iron oxide nanoparticles: a predicted model.

PubMed

Ghosh, Goutam; Panicker, Lata; Ningthoujam, R S; Barick, K C; Tewari, R

2013-03-01

The effects of electrostatic interaction between the hen egg white lysozyme (HEWL) and the functionalized iron oxide nanoparticles (IONPs) have been investigated using several techniques, e.g., CD, DSC, ζ-potential, UV-visible spectroscopy, DLS, TEM. Nanoparticles (IONPs) were functionalized with three hydrophilic ligands, viz., poly(ethylene glycol) (PEG), trisodium citrate (TSC) and sodium triphosphate (STP); where both TSC and STP contain Na(+) counter ions. It has been observed that the secondary structure of HEWL was not affected by PEG functionalized IONPs, but was partially and almost completely perturbed by TSC and STP functionalized IONPs, respectively. The perturbation of the secondary structure was irreversible. We have predicted an interaction model to explain the origin of perturbation of HEWL structure. We have also investigated the stability of nanoparticles dispersions after interaction with HEWL and used the DLVO theory to explain results. Copyright © 2012 Elsevier B.V. All rights reserved.

High Ringxiety: Attachment Anxiety Predicts Experiences of Phantom Cell Phone Ringing.

PubMed

Kruger, Daniel J; Djerf, Jaikob M

2016-01-01

Mobile cell phone users have reported experiencing ringing and/or vibrations associated with incoming calls and messages, only to find that no call or message had actually registered. We believe this phenomenon can be understood as a human signal detection issue, with potentially important influences from psychological attributes. We hypothesized that individuals higher in attachment anxiety would report more frequent phantom cell phone experiences, whereas individuals higher in attachment avoidance would report less frequent experiences. If these experiences are primarily psychologically related to attributes of interpersonal relationships, associations with attachment style should be stronger than for general sensation seeking. We also predicted that certain contexts would interact with attachment style to increase or decrease the likelihood of experiencing phantom cell phone calls and messages. Attachment anxiety directly predicted the frequency of phantom ringing and notification experiences, whereas attachment avoidance and sensation seeking did not directly predict frequency. Attachment anxiety and attachment avoidance interacted with contextual factors (expectations for a call or message and concerned about an issue that one may be contacted about) in the expected directions for predicting phantom cell phone experiences.

Energetics of protein-DNA interactions.

PubMed

Donald, Jason E; Chen, William W; Shakhnovich, Eugene I

2007-01-01

Protein-DNA interactions are vital for many processes in living cells, especially transcriptional regulation and DNA modification. To further our understanding of these important processes on the microscopic level, it is necessary that theoretical models describe the macromolecular interaction energetics accurately. While several methods have been proposed, there has not been a careful comparison of how well the different methods are able to predict biologically important quantities such as the correct DNA binding sequence, total binding free energy and free energy changes caused by DNA mutation. In addition to carrying out the comparison, we present two important theoretical models developed initially in protein folding that have not yet been tried on protein-DNA interactions. In the process, we find that the results of these knowledge-based potentials show a strong dependence on the interaction distance and the derivation method. Finally, we present a knowledge-based potential that gives comparable or superior results to the best of the other methods, including the molecular mechanics force field AMBER99.

Molecular electrostatics for probing lone pair-π interactions.

PubMed

Mohan, Neetha; Suresh, Cherumuttathu H; Kumar, Anmol; Gadre, Shridhar R

2013-11-14

An electrostatics-based approach has been proposed for probing the weak interactions between lone pair containing molecules and π deficient molecular systems. For electron-rich molecules, the negative minima in molecular electrostatic potential (MESP) topography give the location of electron localization and the MESP value at the minimum (Vmin) quantifies the electron-rich character of that region. Interactive behavior of a lone pair bearing molecule with electron deficient π-systems, such as hexafluorobenzene, 1,3,5-trinitrobenzene, 2,4,6-trifluoro-1,3,5-triazine and 1,2,4,5-tetracyanobenzene explored within DFT brings out good correlation of the lone pair-π interaction energy (E(int)) with the Vmin value of the electron-rich system. Such interaction is found to be portrayed well with the Electrostatic Potential for Intermolecular Complexation (EPIC) model. On the basis of the precise location of MESP minimum, a prediction for the orientation of a lone pair bearing molecule with an electron deficient π-system is possible in the majority of the cases studied.

Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

Crosara, Karla Tonelli Bicalho; Moffa, Eduardo Buozi; Xiao, Yizhi; Siqueira, Walter Luiz

2018-01-16

Protein-protein interaction is a common physiological mechanism for protection and actions of proteins in an organism. The identification and characterization of protein-protein interactions in different organisms is necessary to better understand their physiology and to determine their efficacy. In a previous in vitro study using mass spectrometry, we identified 43 proteins that interact with histatin 1. Six previously documented interactors were confirmed and 37 novel partners were identified. In this tutorial, we aimed to demonstrate the usefulness of the STRING database for studying protein-protein interactions. We used an in-silico approach along with the STRING database (http://string-db.org/) and successfully performed a fast simulation of a novel constructed histatin 1 protein-protein network, including both the previously known and the predicted interactors, along with our newly identified interactors. Our study highlights the advantages and importance of applying bioinformatics tools to merge in-silico tactics with experimental in vitro findings for rapid advancement of our knowledge about protein-protein interactions. Our findings also indicate that bioinformatics tools such as the STRING protein network database can help predict potential interactions between proteins and thus serve as a guide for future steps in our exploration of the Human Interactome. Our study highlights the usefulness of the STRING protein database for studying protein-protein interactions. The STRING database can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct (physical) and indirect (functional) interactions, in an easy-to-use interface. Copyright © 2017 Elsevier B.V. All rights reserved.

User Interaction Design for a Home-Based Telecare System

NASA Astrophysics Data System (ADS)

Raptis, Spyros; Tsiakoulis, Pirros; Chalamandaris, Aimilios; Karabetsos, Sotiris

This paper presents the design of the user-interaction component of a home-based telecare system for congestive heart failure patients. It provides a short overview of the overall system and offers details on the different interaction types supported by the system. Interacting with the user occurs either as part of a scheduled procedure or as a consequence of identifying or predicting a potentially hazardous deterioration of the patients' health state. The overall logic of the interaction is structured around event-scenario associations, where a scenario consists of concrete actions to be performed, some of which may involve the patient. A key objective in this type of interaction that it is very simple, intuitive and short, involving common everyday objects and familiar media such as speech.

Plasma Interaction with International Space Station High Voltage Solar Arrays

NASA Technical Reports Server (NTRS)

Heard, John W.

2002-01-01

The International Space Station (ISS) is presently being assembled in low-earth orbit (LEO) operating high voltage solar arrays (-160 V max, -140 V typical with respect to the ambient atmosphere). At the station's present altitude, there exists substantial ambient plasma that can interact with the solar arrays. The biasing of an object to an electric potential immersed in plasma creates a plasma "sheath" or non-equilibrium plasma around the object to mask out the electric fields. A positively biased object can collect electrons from the plasma sheath and the sheath will draw a current from the surrounding plasma. This parasitic current can enter the solar cells and effectively "short out" the potential across the cells, reducing the power that can be generated by the panels. Predictions of collected current based on previous high voltage experiments (SAMPIE (Solar Array Module Plasma Interactions Experiment), PASP+ (Photovoltaic Array Space Power) were on the order of amperes of current. However, present measurements of parasitic current are on the order of several milliamperes, and the current collection mainly occurs during an "eclipse exit" event, i.e., when the space station comes out of darkness. This collection also has a time scale, t approx. 1000 s, that is much slower than any known plasma interaction time scales. The reason for the discrepancy between predictions and present electron collection is not understood and is under investigation by the PCU (Plasma Contactor Unit) "Tiger" team. This paper will examine the potential structure within and around the solar arrays, and the possible causes and reasons for the electron collection of the array.

Similarity-based modeling in large-scale prediction of drug-drug interactions.

PubMed

Vilar, Santiago; Uriarte, Eugenio; Santana, Lourdes; Lorberbaum, Tal; Hripcsak, George; Friedman, Carol; Tatonetti, Nicholas P

2014-09-01

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

Are Anion/π Interactions Actually a Case of Simple Charge–Dipole Interactions?†

PubMed Central

Wheeler, Steven E.; Houk, K. N.

2011-01-01

Substituent effects in Cl− ••• C6H6−nXn complexes, models for anion/π interactions, have been examined using density functional theory and robust ab initio methods paired with large basis sets. Predicted interaction energies for 83 model Cl− ••• C6H6−nXn complexes span almost 40 kcal mol−1 and show an excellent correlation (r = 0.99) with computed electrostatic potentials. In contrast to prevailing models of anion/π interactions, which rely on substituent-induced changes in the aryl π-system, it is shown that substituent effects in these systems are due mostly to direct interactions between the anion and the substituents. Specifically, interaction energies for Cl− ••• C6H6−nXn complexes are recovered using a model system in which the substituents are isolated from the aromatic ring and π-resonance effects are impossible. Additionally, accurate potential energy curves for Cl− interacting with prototypical anion-binding arenes can be qualitatively reproduced by adding a classical charge–dipole interaction to the Cl− ••• C6H6 interaction potential. In substituted benzenes, binding of anions arises primarily from interactions of the anion with the local dipoles induced by the substituents, not changes in the interaction with the aromatic ring itself. When designing anion-binding motifs, phenyl rings should be viewed as a scaffold upon which appropriate substituents can be placed, because there are no attractive interactions between anions and the aryl π-system of substituted benzenes. PMID:20433187

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs.

PubMed

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-02-28

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.

A cluster expansion model for predicting activation barrier of atomic processes

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

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit, E-mail: achatter@iitk.ac.in

2013-06-15

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEBmore » results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog.« less

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

NASA Astrophysics Data System (ADS)

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane.

PubMed

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S; Gray, C G

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05+/-0.39 kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Adsorption of metal atoms at a buckled graphene grain boundary using model potentials

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

Helgee, Edit E.; Isacsson, Andreas

Two model potentials have been evaluated with regard to their ability to model adsorption of single metal atoms on a buckled graphene grain boundary. One of the potentials is a Lennard-Jones potential parametrized for gold and carbon, while the other is a bond-order potential parametrized for the interaction between carbon and platinum. Metals are expected to adsorb more strongly to grain boundaries than to pristine graphene due to their enhanced adsorption at point defects resembling those that constitute the grain boundary. Of the two potentials considered here, only the bond-order potential reproduces this behavior and predicts the energy of themore » adsorbate to be about 0.8 eV lower at the grain boundary than on pristine graphene. The Lennard-Jones potential predicts no significant difference in energy between adsorbates at the boundary and on pristine graphene. These results indicate that the Lennard-Jones potential is not suitable for studies of metal adsorption on defects in graphene, and that bond-order potentials are preferable.« less

Memory processes of flight situation awareness: interactive roles of working memory capacity, long-term working memory, and expertise.

PubMed

Sohn, Young Woo; Doane, Stephanie M

2004-01-01

This research examined the role of working memory (WM) capacity and long-term working memory (LT-WM) in flight situation awareness (SA). We developed spatial and verbal measures of WM capacity and LT-WM skill and then determined the ability of these measures to predict pilot performance on SA tasks. Although both spatial measures of WM capacity and LT-WM skills were important predictors of SA performance, their importance varied as a function of pilot expertise. Spatial WM capacity was most predictive of SA performance for novices, whereas spatial LT-WM skill based on configurations of control flight elements (attitude and power) was most predictive for experts. Furthermore, evidence for an interactive role of WM and LT-WM mechanisms was indicated. Actual or potential applications of this research include cognitive analysis of pilot expertise and aviation training.

Functional annotation of chemical libraries across diverse biological processes.

PubMed

Piotrowski, Jeff S; Li, Sheena C; Deshpande, Raamesh; Simpkins, Scott W; Nelson, Justin; Yashiroda, Yoko; Barber, Jacqueline M; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Karen; Torres, Nikko P; LeBlanc, Marissa A; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; van Leeuwen, Jolanda; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L; Boone, Charles

2017-09-01

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method

PubMed Central

Dakshanamurthy, Sivanesan; Issa, Naiem T; Assefnia, Shahin; Seshasayee, Ashwini; Peters, Oakland J; Madhavan, Subha; Uren, Aykut; Brown, Milton L; Byers, Stephen W

2012-01-01

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27,000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets. PMID:22780961

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.

Tying dark matter to baryons with self-interactions.

PubMed

Kaplinghat, Manoj; Keeley, Ryan E; Linden, Tim; Yu, Hai-Bo

2014-07-11

Self-interacting dark matter (SIDM) models have been proposed to solve the small-scale issues with the collisionless cold dark matter paradigm. We derive equilibrium solutions in these SIDM models for the dark matter halo density profile including the gravitational potential of both baryons and dark matter. Self-interactions drive dark matter to be isothermal and this ties the core sizes and shapes of dark matter halos to the spatial distribution of the stars, a radical departure from previous expectations and from cold dark matter predictions. Compared to predictions of SIDM-only simulations, the core sizes are smaller and the core densities are higher, with the largest effects in baryon-dominated galaxies. As an example, we find a core size around 0.3 kpc for dark matter in the Milky Way, more than an order of magnitude smaller than the core size from SIDM-only simulations, which has important implications for indirect searches of SIDM candidates.

Strongly Emitting Surfaces Unable to Float below Plasma Potential

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

Campanell, M. D.; Umansky, M. V.

2016-02-25

One important unresolved question in plasma physics concerns the effect of strong electron emission on plasma-surface interactions. Previous papers reported solutions with negative and positive floating potentials relative to the plasma edge. For these two models a very different predictions for particle and energy balance is given. Here we show that the positive potential state is the only possible equilibrium in general. Even if a negative floating potential existed at t=0, the ionization collisions near the surface will force a transition to the positive floating potential state. Moreover, this transition is demonstrated with a new simulation code.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

PubMed

Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

2016-11-01

A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

PubMed Central

Basuino, Li; Chambers, Henry F.; Lee, Deok-Sun; Wiest, Olaf G.; Babbitt, Patricia C.

2009-01-01

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism. PMID:19701464

PL-PatchSurfer: a novel molecular local surface-based method for exploring protein-ligand interactions.

PubMed

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G; Kihara, Daisuke

2014-08-27

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets.

PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions

PubMed Central

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G.; Kihara, Daisuke

2014-01-01

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. PMID:25167137

Evaluation of P-Glycoprotein Inhibitory Potential Using a Rhodamine 123 Accumulation Assay

PubMed Central

Jouan, Elodie; Le Vée, Marc; Mayati, Abdullah; Denizot, Claire; Parmentier, Yannick; Fardel, Olivier

2016-01-01

In vitro evaluation of P-glycoprotein (P-gp) inhibitory potential is now a regulatory issue during drug development, in order to predict clinical inhibition of P-gp and subsequent drug–drug interactions. Assays for this purpose, commonly based on P-gp-expressing cell lines and digoxin as a reference P-gp substrate probe, unfortunately exhibit high variability, raising thus the question of developing alternative or complementary tests for measuring inhibition of P-gp activity. In this context, the present study was designed to investigate the use of the fluorescent dye rhodamine 123 as a reference P-gp substrate probe for characterizing P-gp inhibitory potential of 16 structurally-unrelated drugs known to interact with P-gp. 14/16 of these P-gp inhibitors were found to increase rhodamine 123 accumulation in P-gp-overexpressing MCF7R cells, thus allowing the determination of their P-gp inhibitory potential, i.e., their half maximal inhibitor concentration (IC50) value towards P-gp-mediated transport of the dye. These IC50 values were in the range of variability of previously reported IC50 for P-gp and can be used for the prediction of clinical P-gp inhibition according to Food and Drug Administration (FDA) criteria, with notable sensitivity (80%). Therefore, the data demonstrated the feasibility of the use of rhodamine 123 for evaluating the P-gp inhibitory potential of drugs. PMID:27077878

Exploring the role of water in molecular recognition: predicting protein ligandability using a combinatorial search of surface hydration sites.

PubMed

Vukovic, Sinisa; Brennan, Paul E; Huggins, David J

2016-09-01

The interaction between any two biological molecules must compete with their interaction with water molecules. This makes water the most important molecule in medicine, as it controls the interactions of every therapeutic with its target. A small molecule binding to a protein is able to recognize a unique binding site on a protein by displacing bound water molecules from specific hydration sites. Quantifying the interactions of these water molecules allows us to estimate the potential of the protein to bind a small molecule. This is referred to as ligandability. In the study, we describe a method to predict ligandability by performing a search of all possible combinations of hydration sites on protein surfaces. We predict ligandability as the summed binding free energy for each of the constituent hydration sites, computed using inhomogeneous fluid solvation theory. We compared the predicted ligandability with the maximum observed binding affinity for 20 proteins in the human bromodomain family. Based on this comparison, it was determined that effective inhibitors have been developed for the majority of bromodomains, in the range from 10 to 100 nM. However, we predict that more potent inhibitors can be developed for the bromodomains BPTF and BRD7 with relative ease, but that further efforts to develop inhibitors for ATAD2 will be extremely challenging. We have also made predictions for the 14 bromodomains with no reported small molecule K d values by isothermal titration calorimetry. The calculations predict that PBRM1(1) will be a challenging target, while others such as TAF1L(2), PBRM1(4) and TAF1(2), should be highly ligandable. As an outcome of this work, we assembled a database of experimental maximal K d that can serve as a community resource assisting medicinal chemistry efforts focused on BRDs. Effective prediction of ligandability would be a very useful tool in the drug discovery process.

Exploring the role of water in molecular recognition: predicting protein ligandability using a combinatorial search of surface hydration sites

NASA Astrophysics Data System (ADS)

Vukovic, Sinisa; Brennan, Paul E.; Huggins, David J.

2016-09-01

The interaction between any two biological molecules must compete with their interaction with water molecules. This makes water the most important molecule in medicine, as it controls the interactions of every therapeutic with its target. A small molecule binding to a protein is able to recognize a unique binding site on a protein by displacing bound water molecules from specific hydration sites. Quantifying the interactions of these water molecules allows us to estimate the potential of the protein to bind a small molecule. This is referred to as ligandability. In the study, we describe a method to predict ligandability by performing a search of all possible combinations of hydration sites on protein surfaces. We predict ligandability as the summed binding free energy for each of the constituent hydration sites, computed using inhomogeneous fluid solvation theory. We compared the predicted ligandability with the maximum observed binding affinity for 20 proteins in the human bromodomain family. Based on this comparison, it was determined that effective inhibitors have been developed for the majority of bromodomains, in the range from 10 to 100 nM. However, we predict that more potent inhibitors can be developed for the bromodomains BPTF and BRD7 with relative ease, but that further efforts to develop inhibitors for ATAD2 will be extremely challenging. We have also made predictions for the 14 bromodomains with no reported small molecule K d values by isothermal titration calorimetry. The calculations predict that PBRM1(1) will be a challenging target, while others such as TAF1L(2), PBRM1(4) and TAF1(2), should be highly ligandable. As an outcome of this work, we assembled a database of experimental maximal K d that can serve as a community resource assisting medicinal chemistry efforts focused on BRDs. Effective prediction of ligandability would be a very useful tool in the drug discovery process.

Quantified Gamow shell model interaction for p s d -shell nuclei

NASA Astrophysics Data System (ADS)

Jaganathen, Y.; Betan, R. M. Id; Michel, N.; Nazarewicz, W.; Płoszajczak, M.

2017-11-01

Background: The structure of weakly bound and unbound nuclei close to particle drip lines is one of the major science drivers of nuclear physics. A comprehensive understanding of these systems goes beyond the traditional configuration interaction approach formulated in the Hilbert space of localized states (nuclear shell model) and requires an open quantum system description. The complex-energy Gamow shell model (GSM) provides such a framework as it is capable of describing resonant and nonresonant many-body states on equal footing. Purpose: To make reliable predictions, quality input is needed that allows for the full uncertainty quantification of theoretical results. In this study, we carry out the optimization of an effective GSM (one-body and two-body) interaction in the p s d f -shell-model space. The resulting interaction is expected to describe nuclei with 5 ≤A ≲12 at the p -s d -shell interface. Method: The one-body potential of the 4He core is modeled by a Woods-Saxon + spin-orbit + Coulomb potential, and the finite-range nucleon-nucleon interaction between the valence nucleons consists of central, spin-orbit, tensor, and Coulomb terms. The GSM is used to compute key fit observables. The χ2 optimization is performed using the Gauss-Newton algorithm augmented by the singular value decomposition technique. The resulting covariance matrix enables quantification of statistical errors within the linear regression approach. Results: The optimized one-body potential reproduces nucleon-4He scattering phase shifts up to an excitation energy of 20 MeV. The two-body interaction built on top of the optimized one-body field is adjusted to the bound and unbound ground-state binding energies and selected excited states of the helium, lithium, and beryllium isotopes up to A =9 . A very good agreement with experimental results was obtained for binding energies. First applications of the optimized interaction include predictions for two-nucleon correlation densities and excitation spectra of light nuclei with quantified uncertainties. Conclusion: The new interaction will enable comprehensive and fully quantified studies of structure and reactions aspects of nuclei from the p s d region of the nuclear chart.

Quantified Gamow shell model interaction for p s d -shell nuclei

DOE PAGES

Jaganathen, Y.; Betan, R. M. Id; Michel, N.; ...

2017-11-20

Background: The structure of weakly bound and unbound nuclei close to particle drip lines is one of the major science drivers of nuclear physics. A comprehensive understanding of these systems goes beyond the traditional configuration interaction approach formulated in the Hilbert space of localized states (nuclear shell model) and requires an open quantum system description. The complex-energy Gamow shell model (GSM) provides such a framework as it is capable of describing resonant and nonresonant many-body states on equal footing. Purpose: To make reliable predictions, quality input is needed that allows for the full uncertainty quantification of theoretical results. In thismore » study, we carry out the optimization of an effective GSM (one-body and two-body) interaction in the psdf-shell-model space. The resulting interaction is expected to describe nuclei with 5 ≤ A ≲ 12 at the p-sd-shell interface. Method: The one-body potential of the 4He core is modeled by a Woods-Saxon + spin-orbit + Coulomb potential, and the finite-range nucleon-nucleon interaction between the valence nucleons consists of central, spin-orbit, tensor, and Coulomb terms. The GSM is used to compute key fit observables. The χ 2 optimization is performed using the Gauss-Newton algorithm augmented by the singular value decomposition technique. The resulting covariance matrix enables quantification of statistical errors within the linear regression approach. Results: The optimized one-body potential reproduces nucleon- 4He scattering phase shifts up to an excitation energy of 20 MeV. The two-body interaction built on top of the optimized one-body field is adjusted to the bound and unbound ground-state binding energies and selected excited states of the helium, lithium, and beryllium isotopes up to A = 9 . A very good agreement with experimental results was obtained for binding energies. First applications of the optimized interaction include predictions for two-nucleon correlation densities and excitation spectra of light nuclei with quantified uncertainties. In conclusion: The new interaction will enable comprehensive and fully quantified studies of structure and reactions aspects of nuclei from the psd region of the nuclear chart.« less

Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

PubMed

Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

2017-01-01

Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

PubMed

Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

2016-01-01

While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.

PubMed

Holliday, Jason A; Wang, Tongli; Aitken, Sally

2012-09-01

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm 'Random Forest' to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits--autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.

Gene-gene interaction between COMT and MAOA potentially predicts the intelligence of attention-deficit hyperactivity disorder boys in China.

PubMed

Qian, Qiu-Jin; Yang, Li; Wang, Yu-Feng; Zhang, Hao-Bo; Guan, Li-Li; Chen, Yun; Ji, Ning; Liu, Lu; Faraone, S V

2010-05-01

The catechol-O-methyltransferase (COMT) gene contains a functional polymorphism (Val158Met) affecting the activity of the enzyme, and the monoamine oxidase A (MAOA) gene contains a VNTR polymorphism (MAOA-uVNTR) that affects the transcription of the gene. COMT and MAOA each contribute to the enzymatic degradation of dopamine and noradrenaline. Prefrontal cortical (PFC) function, which plays an important role in individual cognitive abilities, including intelligence, is modulated by dopamine. Since our previous association studies between attention deficit hyperactivity disorder (ADHD) and these two functional polymorphisms consistently showed the low activity alleles were preferentially transmitted to inattentive ADHD boys, the goal of the present study was to test the hypothesis that the interaction between COMT Val158Met and MAOA-uVNTR may affect the intelligence in a clinical sample of Chinese male ADHD subjects (n = 264). We found that the COMT x MAOA interaction significantly predicted full scale (FSIQ) and performance (PIQ) IQ scores (P = 0.039, 0.011); the MAOA-uVNTR significantly predicted FSIQ, PIQ and verbal IQ (VIQ) (P = 0.009, 0.019, 0.038); COMT Val158Met independently had no effect on any of the IQ scores. Only the COMT x MAOA interaction for PIQ remained significant after a Bonferroni correction. Among all combined genotypes, the valval-3R genotype predicted higher intelligence, (average 106.7 +/- 1.6, 95% C.I. 103.7-109.8 for FSIQ), and the valval-4R predicted lower intelligence (average 98.0 +/- 2.3, 95% C.I. 93.5-102.6 for FSIQ). These results suggest that there is an inverted U-shaped relationship between intelligence and dopaminergic activity in our sample. Our finding that gene-gene interaction between COMT and MAOA predicts the intelligence of ADHD boys in China is intriguing but requires replication in other samples.

Interatomic Potentials for Structure Simulation of Alkaline-Earth Cuprates

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

Eremin, N.N.; Leonyuk, L.I.; Urusov, V.S.

2001-05-01

A specific potential model of interionic interactions was derived in which the crystal structures of alkaline-earth cuprates were satisfactorily described and some of their physical properties were predicted. It was found that a harmonic three-particle O-Cu-O potential and some Morse-type contributions to the simple Buckingham-type Cu-O repulsive potential enable one to improve essentially the results of crystal structure modeling for cuprates. The obtained potential set seems to be well transferable for different cuprates, despite the variety in linkages of the CuO{sub 4} groups. In the present work this potential set model was applied in the crystal structure modeling for Ca{submore » 2}CuO{sub 3}, CaCuO{sub 2}, SrCuO{sub 3}, (Sr{sub 1.19}Ca{sub 0.73})Cu{sub 2}O{sub 4}, and BaCuO{sub 2}. Some elastic and energetic properties of the compounds under question were predicted.« less

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.

The dopamine receptor D4 gene and familial loading interact with perceived parenting in predicting externalizing behavior problems in early adolescence: the TRacking Adolescents' Individual Lives Survey (TRAILS).

PubMed

Marsman, Rianne; Oldehinkel, Albertine J; Ormel, Johan; Buitelaar, Jan K

2013-08-30

Although externalizing behavior problems show in general a high stability over time, the course of externalizing behavior problems may vary from individual to individual. Our main goal was to investigate the predictive role of parenting on externalizing behavior problems. In addition, we investigated the potential moderating role of gender and genetic risk (operationalized as familial loading of externalizing behavior problems (FLE), and presence or absence of the dopamine receptor D4 (DRD4) 7-repeat and 4-repeat allele, respectively). Perceived parenting (rejection, emotional warmth, and overprotection) and FLE were assessed in a population-based sample of 1768 10- to 12-year-old adolescents. Externalizing behavior problems were assessed at the same age and 212 years later by parent report (CBCL) and self-report (YSR). DNA was extracted from blood samples. Parental emotional warmth predicted lower, and parental overprotection and rejection predicted higher levels of externalizing behavior problems. Whereas none of the parenting factors interacted with gender and the DRD4 7-repeat allele, we did find interaction effects with FLE and the DRD4 4-repeat allele. That is, the predictive effect of parental rejection was only observed in adolescents from low FLE families and the predictive effect of parental overprotection was stronger in adolescents not carrying the DRD4 4-repeat allele. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Virtual Clinical Trial Toward Polytherapy Safety Assessment: Combination of Physiologically Based Pharmacokinetic/Pharmacodynamic-Based Modeling and Simulation Approach With Drug-Drug Interactions Involving Terfenadine as an Example.

PubMed

Wiśniowska, Barbara; Polak, Sebastian

2016-11-01

A Quantitative Systems Pharmacology approach was utilized to predict the cardiac consequences of drug-drug interaction (DDI) at the population level. The Simcyp in vitro-in vivo correlation and physiologically based pharmacokinetic platform was used to predict the pharmacokinetic profile of terfenadine following co-administration of the drug. Electrophysiological effects were simulated using the Cardiac Safety Simulator. The modulation of ion channel activity was dependent on the inhibitory potential of drugs on the main cardiac ion channels and a simulated free heart tissue concentration. ten Tusscher's human ventricular cardiomyocyte model was used to simulate the pseudo-ECG traces and further predict the pharmacodynamic consequences of DDI. Consistent with clinical observations, predicted plasma concentration profiles of terfenadine show considerable intra-subject variability with recorded C max values below 5 ng/mL for most virtual subjects. The pharmacokinetic and pharmacodynamic effects of inhibitors were predicted with reasonable accuracy. In all cases, a combination of the physiologically based pharmacokinetic and physiology-based pharmacodynamic models was able to differentiate between the terfenadine alone and terfenadine + inhibitor scenario. The range of QT prolongation was comparable in the clinical and virtual studies. The results indicate that mechanistic in vitro-in vivo correlation can be applied to predict the clinical effects of DDI even without comprehensive knowledge on all mechanisms contributing to the interaction. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

How Structure Defines Affinity in Protein-Protein Interactions

PubMed Central

Erijman, Ariel; Rosenthal, Eran; Shifman, Julia M.

2014-01-01

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins. PMID:25329579

Multiaxial and Thermomechanical Fatigue of Materials: A Historical Perspective and Some Future Challenges

NASA Technical Reports Server (NTRS)

Kalluri, Sreeramesh

2013-01-01

Structural materials used in engineering applications routinely subjected to repetitive mechanical loads in multiple directions under non-isothermal conditions. Over past few decades, several multiaxial fatigue life estimation models (stress- and strain-based) developed for isothermal conditions. Historically, numerous fatigue life prediction models also developed for thermomechanical fatigue (TMF) life prediction, predominantly for uniaxial mechanical loading conditions. Realistic structural components encounter multiaxial loads and non-isothermal loading conditions, which increase potential for interaction of damage modes. A need exists for mechanical testing and development verification of life prediction models under such conditions.

Potential consequences of climate change for primary production and fish production in large marine ecosystems.

PubMed

Blanchard, Julia L; Jennings, Simon; Holmes, Robert; Harle, James; Merino, Gorka; Allen, J Icarus; Holt, Jason; Dulvy, Nicholas K; Barange, Manuel

2012-11-05

Existing methods to predict the effects of climate change on the biomass and production of marine communities are predicated on modelling the interactions and dynamics of individual species, a very challenging approach when interactions and distributions are changing and little is known about the ecological mechanisms driving the responses of many species. An informative parallel approach is to develop size-based methods. These capture the properties of food webs that describe energy flux and production at a particular size, independent of species' ecology. We couple a physical-biogeochemical model with a dynamic, size-based food web model to predict the future effects of climate change on fish biomass and production in 11 large regional shelf seas, with and without fishing effects. Changes in potential fish production are shown to most strongly mirror changes in phytoplankton production. We project declines of 30-60% in potential fish production across some important areas of tropical shelf and upwelling seas, most notably in the eastern Indo-Pacific, the northern Humboldt and the North Canary Current. Conversely, in some areas of the high latitude shelf seas, the production of pelagic predators was projected to increase by 28-89%.

Novel Design Strategy for Checkpoint Kinase 2 Inhibitors Using Pharmacophore Modeling, Combinatorial Fusion, and Virtual Screening

PubMed Central

Wang, Yen-Ling

2014-01-01

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the BesttrainBesttest and FasttrainFasttest prediction results. The potential inhibitors were selected from NCI database by screening according to BesttrainBesttest + FasttrainFasttest prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study. PMID:24864236

Eldercare responsibilities, interrole conflict, and employee absence: a daily study.

PubMed

Hepburn, C G; Barling, J

1996-07-01

A model was developed specifying that the number of hours employees spend providing care to or interacting with elderly parents predicts conflict between the roles of employee and caregiver. Interrole conflict was subsequently expected to predict partial absence from work (e.g., arriving late). Seventeen employed eldercare providers completed a daily questionnaire for 20 work days. The data were standardized and pooled, and the proposed model was tested by using structural equation modeling. The proposed model provided a good fit to the data. A competing model that added the direct effects of hours of interacting with and hours of providing care to parents on partial absence provided a significantly better fit. The potential impact of the findings on employees and organizations is discussed.

Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins

PubMed Central

Gunner, MR; Baker, Nathan A.

2017-01-01

Proteins change their charge state through protonation and redox reactions as well as through binding charged ligands. The free energy of these reactions are dominated by solvation and electrostatic energies and modulated by protein conformational relaxation in response to the ionization state changes. Although computational methods for calculating these interactions can provide very powerful tools for predicting protein charge states, they include several critical approximations of which users should be aware. This chapter discusses the strengths, weaknesses, and approximations of popular computational methods for predicting charge states and understanding their underlying electrostatic interactions. The goal of this chapter is to inform users about applications and potential caveats of these methods as well as outline directions for future theoretical and computational research. PMID:27497160

A numerical tool for reproducing driver behaviour: experiments and predictive simulations.

PubMed

Casucci, M; Marchitto, M; Cacciabue, P C

2010-03-01

This paper presents the simulation tool called SDDRIVE (Simple Simulation of Driver performance), which is the numerical computerised implementation of the theoretical architecture describing Driver-Vehicle-Environment (DVE) interactions, contained in Cacciabue and Carsten [Cacciabue, P.C., Carsten, O. A simple model of driver behaviour to sustain design and safety assessment of automated systems in automotive environments, 2010]. Following a brief description of the basic algorithms that simulate the performance of drivers, the paper presents and discusses a set of experiments carried out in a Virtual Reality full scale simulator for validating the simulation. Then the predictive potentiality of the tool is shown by discussing two case studies of DVE interactions, performed in the presence of different driver attitudes in similar traffic conditions.

An entropy and viscosity corrected potential method for rotor performance prediction

NASA Technical Reports Server (NTRS)

Bridgeman, John O.; Strawn, Roger C.; Caradonna, Francis X.

1988-01-01

An unsteady Full-Potential Rotor code (FPR) has been enhanced with modifications directed at improving its drag prediction capability. The shock generated entropy has been included to provide solutions comparable to the Euler equations. A weakly interacted integral boundary layer has also been coupled to FPR in order to estimate skin-friction drag. Pressure distributions, shock positions, and drag comparisons are made with various data sets derived from two-dimensional airfoil, hovering, and advancing high speed rotor tests. In all these comparisons, the effect of the nonisentropic modification improves (i.e., weakens) the shock strength and wave drag. In addition, the boundary layer method yields reasonable estimates of skin-friction drag. Airfoil drag and hover torque data comparisons are excellent, as are predicted shock strength and positions for a high speed advancing rotor.

Experimental validation of predicted cancer genes using FRET

NASA Astrophysics Data System (ADS)

Guala, Dimitri; Bernhem, Kristoffer; Ait Blal, Hammou; Jans, Daniel; Lundberg, Emma; Brismar, Hjalmar; Sonnhammer, Erik L. L.

2018-07-01

Huge amounts of data are generated in genome wide experiments, designed to investigate diseases with complex genetic causes. Follow up of all potential leads produced by such experiments is currently cost prohibitive and time consuming. Gene prioritization tools alleviate these constraints by directing further experimental efforts towards the most promising candidate targets. Recently a gene prioritization tool called MaxLink was shown to outperform other widely used state-of-the-art prioritization tools in a large scale in silico benchmark. An experimental validation of predictions made by MaxLink has however been lacking. In this study we used Fluorescence Resonance Energy Transfer, an established experimental technique for detection of protein-protein interactions, to validate potential cancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink for selection of new targets in the battle with polygenic diseases.

Electrostatic potential of B-DNA: effect of interionic correlations.

PubMed Central

Gavryushov, S; Zielenkiewicz, P

1998-01-01

Modified Poisson-Boltzmann (MPB) equations have been numerically solved to study ionic distributions and mean electrostatic potentials around a macromolecule of arbitrarily complex shape and charge distribution. Results for DNA are compared with those obtained by classical Poisson-Boltzmann (PB) calculations. The comparisons were made for 1:1 and 2:1 electrolytes at ionic strengths up to 1 M. It is found that ion-image charge interactions and interionic correlations, which are neglected by the PB equation, have relatively weak effects on the electrostatic potential at charged groups of the DNA. The PB equation predicts errors in the long-range electrostatic part of the free energy that are only approximately 1.5 kJ/mol per nucleotide even in the case of an asymmetrical electrolyte. In contrast, the spatial correlations between ions drastically affect the electrostatic potential at significant separations from the macromolecule leading to a clearly predicted effect of charge overneutralization. PMID:9826596

A methodology to enhance electromagnetic compatibility in joint military operations

NASA Astrophysics Data System (ADS)

Buckellew, William R.

The development and validation of an improved methodology to identify, characterize, and prioritize potential joint EMI (electromagnetic interference) interactions and identify and develop solutions to reduce the effects of the interference are discussed. The methodology identifies potential EMI problems using results from field operations, historical data bases, and analytical modeling. Operational expertise, engineering analysis, and testing are used to characterize and prioritize the potential EMI problems. Results can be used to resolve potential EMI during the development and acquisition of new systems and to develop engineering fixes and operational workarounds for systems already employed. The analytic modeling portion of the methodology is a predictive process that uses progressive refinement of the analysis and the operational electronic environment to eliminate noninterfering equipment pairs, defer further analysis on pairs lacking operational significance, and resolve the remaining EMI problems. Tests are conducted on equipment pairs to ensure that the analytical models provide a realistic description of the predicted interference.

Drug-micronutrient interactions: food for thought and thought for action.

PubMed

Karadima, Vasiliki; Kraniotou, Christina; Bellos, George; Tsangaris, George Th

2016-01-01

Micronutrients are indispensable for a variety of vital functions. Micronutrient deficiencies are a global problem concerning two billion people. In most cases, deficiencies are treatable with supplementation of the elements in lack. Drug-nutrient interactions can also lead to micronutrient reduce or depletion by various pathways. Supplementation of the elements and long-term fortification programs for populations at risk can prevent and restore the related deficiencies. Within the context of Predictive, Preventive, and Personalized Medicine, a multi-professional network should be developed in order to identify, manage, and prevent drug-micronutrient interactions that can potentially result to micronutrient deficiencies.

Prediction of binary nanoparticle superlattices from soft potentials

DOE PAGES

Horst, Nathan; Travesset, Alex

2016-01-07

Driven by the hypothesis that a sufficiently continuous short-ranged potential is able to account for shell flexibility and phonon modes and therefore provides a more realistic description of nanoparticle interactions than a hard sphere model, we compute the solid phase diagram of particles of different radii interacting with an inverse power law potential. From a pool of 24 candidate lattices, the free energy is optimized with respect to additional internal parameters and the p-exponent, determining the short-range properties of the potential, is varied between p = 12 and p = 6. The phase diagrams contain the phases found in ongoingmore » self-assembly experiments, including DNA programmable self-assembly and nanoparticles with capping ligands assembled by evaporation from an organic solvent. Thus, the resulting phase diagrams can be mapped quantitatively to existing experiments as a function of only two parameters: Nanoparticle radius ratio (γ) and softness asymmetry.« less

Prediction of Binary Nanoparticle Superlattices from Soft Potentials

NASA Astrophysics Data System (ADS)

Horst, Nathan; Travesset, Alex

Driven by the hypothesis that a sufficiently continuous short-ranged potential is able to account for shell flexibility and phonon modes and therefore provides a more realistic description of nanoparticle interactions than a hard sphere model, we compute the solid phase diagram of particles of different radii interacting with an inverse power law potential. We explore 24 candidate lattices where the p-exponent, determining the short-range properties of the potential, is varied between p=12 and p=6, and optimize the free energy with respect to additional internal parameters. The phase diagrams contain the phases found in ongoing self-assembly experiments, including DNA programmable self-assembly and nanoparticles with capping ligands assembled by evaporation from an organic solvent. The resulting phase diagrams can be mapped quantitatively to existing experiments as a function of only two parameters: nanoparticle radius ratio (γ) and softness asymmetry (SA). Supported by DOE under Contract Number DE-AC02-07CH11358.

Prediction of binary nanoparticle superlattices from soft potentials

NASA Astrophysics Data System (ADS)

Horst, Nathan; Travesset, Alex

2016-01-01

Driven by the hypothesis that a sufficiently continuous short-ranged potential is able to account for shell flexibility and phonon modes and therefore provides a more realistic description of nanoparticle interactions than a hard sphere model, we compute the solid phase diagram of particles of different radii interacting with an inverse power law potential. From a pool of 24 candidate lattices, the free energy is optimized with respect to additional internal parameters and the p-exponent, determining the short-range properties of the potential, is varied between p = 12 and p = 6. The phase diagrams contain the phases found in ongoing self-assembly experiments, including DNA programmable self-assembly and nanoparticles with capping ligands assembled by evaporation from an organic solvent. The resulting phase diagrams can be mapped quantitatively to existing experiments as a function of only two parameters: Nanoparticle radius ratio (γ) and softness asymmetry.

Solvent-mediated nonelectrostatic ion-ion interactions predicting anomalies in electrophoresis.

PubMed

Goswami, Prakash; Dhar, Jayabrata; Ghosh, Uddipta; Chakraborty, Suman

2017-03-01

We study the effects of solvent-mediated nonelectrostatic ion-ion interactions on electrophoretic mobility of a charged spherical particle. To this end, we consider the case of low surface electrostatic potential resulting in the linearization of the governing equations, which enables us to deduce a closed-form analytical solution to the electrophoretic mobility. We subsequently compare our results to the standard model using Henry's approach and report the changes brought about by the nonelectrostatic potential. The classical approach to determine the electrophoretic mobility underpredicts the particle velocity when compared with experiments. We show that this issue can be resolved by taking into account nonelectrostatic interactions. Our analysis further reveals the phenomenon of electrophoretic mobility reversal that has been experimentally observed in numerous previous studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Crystallization tendencies of modelled Lennard-Jones liquids with different attractions

NASA Astrophysics Data System (ADS)

Valdès, L.-C.; Gerges, J.; Mizuguchi, T.; Affouard, F.

2018-01-01

Molecular dynamics simulations are performed on simple models composed of monoatomic Lennard-Jones atoms for which the repulsive interaction is the same but the attractive part is tuned. We investigate the precise role of the attractive part of the interaction potential on different structural, dynamical, and thermodynamical properties of these systems in the liquid and crystalline states. It includes crystallization trends for which the main physical ingredients involved have been computed: the diffusion coefficient, the Gibbs energy difference between the liquid and the crystalline state, and the crystal-liquid interfacial free energy. Results are compared with predictions from the classical nucleation theory including transient and steady-state regimes at moderate and deeper undercooling. The question of the energetic and entropic impact of the repulsive and attractive part of the interaction potential towards crystallization is also addressed.

A comparison of biophysical characterization techniques in predicting monoclonal antibody stability.

PubMed

Thiagarajan, Geetha; Semple, Andrew; James, Jose K; Cheung, Jason K; Shameem, Mohammed

2016-01-01

With the rapid growth of biopharmaceutical product development, knowledge of therapeutic protein stability has become increasingly important. We evaluated assays that measure solution-mediated interactions and key molecular characteristics of 9 formulated monoclonal antibody (mAb) therapeutics, to predict their stability behavior. Colloidal interactions, self-association propensity and conformational stability were measured using effective surface charge via zeta potential, diffusion interaction parameter (kD) and differential scanning calorimetry (DSC), respectively. The molecular features of all 9 mAbs were compared to their stability at accelerated (25°C and 40°C) and long-term storage conditions (2-8°C) as measured by size exclusion chromatography. At accelerated storage conditions, the majority of the mAbs in this study degraded via fragmentation rather than aggregation. Our results show that colloidal stability, self-association propensity and conformational characteristics (exposed tryptophan) provide reasonable prediction of accelerated stability, with limited predictive value at 2-8°C stability. While no correlations to stability behavior were observed with onset-of-melting temperatures or domain unfolding temperatures, by DSC, melting of the Fab domain with the CH2 domain suggests lower stability at stressed conditions. The relevance of identifying appropriate biophysical assays based on the primary degradation pathways is discussed.

3dRPC: a web server for 3D RNA-protein structure prediction.

PubMed

Huang, Yangyu; Li, Haotian; Xiao, Yi

2018-04-01

RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. yxiao@hust.edu.cn.

Transmembrane Domains of Attraction on the TSH Receptor

PubMed Central

Ali, M. Rejwan; Mezei, Mihaly; Davies, Terry F.

2015-01-01

The TSH receptor (TSHR) has the propensity to form dimers and oligomers. Our data using ectodomain-truncated TSHRs indicated that the predominant interfaces for oligomerization reside in the transmembrane (TM) domain. To map the potentially interacting residues, we first performed in silico studies of the TSHR transmembrane domain using a homology model and using Brownian dynamics (BD). The cluster of dimer conformations obtained from BD analysis indicated that TM1 made contact with TM4 and two residues in TM2 made contact with TM5. To confirm the proximity of these contact residues, we then generated cysteine mutants at all six contact residues predicted by the BD analysis and performed cysteine cross-linking studies. These results showed that the predicted helices in the protomer were indeed involved in proximity interactions. Furthermore, an alternative experimental approach, receptor truncation experiments and LH receptor sequence substitution experiments, identified TM1 harboring a major region involved in TSHR oligomerization, in agreement with the conclusion from the cross-linking studies. Point mutations of the predicted interacting residues did not yield a substantial decrease in oligomerization, unlike the truncation of the TM1, so we concluded that constitutive oligomerization must involve interfaces forming domains of attraction in a cooperative manner that is not dominated by interactions between specific residues. PMID:25406938

Prediction of cassava protein interactome based on interolog method.

PubMed

Thanasomboon, Ratana; Kalapanulak, Saowalak; Netrphan, Supatcharee; Saithong, Treenut

2017-12-08

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi ). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective.

PubMed

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-02-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The 'evolving metacommunity' framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats.

Mechanical Model of Dexterous Continuum Manipulators with Compliant Joints and Tendon/External Force Interactions

PubMed Central

Murphy, Ryan J.; Liu, Hao; Iordachita, Iulian I.; Armand, Mehran

2017-01-01

Dexterous continuum manipulators (DCMs) have been widely adopted for minimally- and less-invasive surgery. During the operation, these DCMs interact with surrounding anatomy actively or passively. The interaction force will inevitably affect the tip position and shape of DCMs, leading to potentially inaccurate control near critical anatomy. In this paper, we demonstrated a 2D mechanical model for a tendon actuated, notched DCM with compliant joints. The model predicted deformation of the DCM accurately in the presence of tendon force, friction force, and external force. A partition approach was proposed to describe the DCM as a series of interconnected rigid and flexible links. Beam mechanics, taking into consideration tendon interaction and external force on the tip and the body, was applied to obtain the deformation of each flexible link of the DCM. The model results were compared with experiments for free bending as well as bending in the presence of external forces acting at either the tip or body of the DCM. The overall mean error of tip position between model predictions and all of the experimental results was 0.62±0.41mm. The results suggest that the proposed model can effectively predict the shape of the DCM. PMID:28989273

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective

PubMed Central

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-01-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The ‘evolving metacommunity’ framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats. PMID:25568038

Understanding the Potential of PARO for Healthy Older Adults

PubMed Central

McGlynn, Sean A.; Kemple, Shawn; Mitzner, Tracy L.; King, Chih-Hung Aaron; Rogers, Wendy A.

2017-01-01

As the population ages, there is an increasing need for socio-emotional support for older adults. A potential way to meet this need is through interacting with pet-type robots such as the seal robot, PARO. There was a need to extend research on PARO’s potential benefits beyond cognitively impaired and dependently living older adults. Because independently living, cognitively intact older adults may also have socio-emotional needs, the primary goal of this study was to investigate their attitudes, emotions, and engagement with PARO to identify its potential applicability to this demographic. Thirty older adults participated in an interaction period with PARO, and their attitudes and emotions toward PARO were assessed before and after using a multi-method approach. Video of the interaction was coded to determine the types and frequency of engagements participants initiated with PARO. Overall, there were no pre-post interaction differences on these measures. However, semi-structured interviews suggested that these older adults had positive attitudes towards PARO’s attributes, thought it would be easy to use, and perceived potential uses for both themselves and others. Participants varied in their frequency of engagement with PARO. A novel finding is that this active engagement frequency uniquely predicted post-interaction period positive affect. This study advances understanding of healthy older adults’ attitudes, emotions, and engagement with PARO and of possible ways in which PARO could provide social and emotional support to healthy older adults. The results are informative for future research and design of pet-type robots. PMID:28943748

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

PubMed Central

Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

2016-01-01

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

A tungsten-rhenium interatomic potential for point defect studies

DOE PAGES

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-28

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

A tungsten-rhenium interatomic potential for point defect studies

NASA Astrophysics Data System (ADS)

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-01

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures in the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancies and self-interstitial defects sufficiently accurately and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).

A tungsten-rhenium interatomic potential for point defect studies

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

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

Potential job facilitation benefits of "water cooler" conversations: the importance of social interactions in the workplace.

PubMed

Lin, Iris Y; Kwantes, Catherine T

2015-01-01

This study looked at the extent to which personality and cultural factors predicted participants' perceptions of the importance private interactions played in the workplace. The 134 participants read a vignette (where a new employee socially interacted at low or high levels with co-workers) and completed the Big Five Inventory, Social Axioms Survey, and questions concerning expected workplace experiences. Results indicated employees who engaged in high levels of private interaction with co-workers were expected to be better liked, to receive better performance evaluations, were more likely to receive co-worker assistance, and were thought to be more likely chosen for future projects. However, the personality and social axiom variables studied did not significantly interact with social interaction to influence expectations of workplace outcomes.

IMPORTANCE OF THE DYNAMICS OF BACTERIOPHAGE-HOST INTERACTIONS TO BACTERIAL ABUNDANCE AND GENETIC DIVERSITY IN AQUATIC ENVIRONMENTS (RESEARCH BRIEF)

EPA Science Inventory

Using Pseudomonas aeruginosa and its bacteriophages as a model system, we have clearly demonstrated a significant potential for viral-mediated gene transfer (transduction) of both plasmid and chromosomal DNA in freshwater microbial populations. These investigations have predicted...

Environmental interactions that influence secondary metabolism and development in the saprophytic crop pathogen Aspergillus flavus

USDA-ARS?s Scientific Manuscript database

Aspergillus flavus is a pathogenic and opportunistic fungus that can infect several crops of agricultural importance and has the potential to produce carcinogenic mycotoxins such as aflatoxin. Predicted changes in global temperatures, precipitation patterns and carbon dioxide levels are expected to ...

From metabolism to ecology: cross-feeding interactions shape the balance between polymicrobial conflict and mutualism

PubMed Central

Estrela, Sylvie; Trisos, Christopher H.; Brown, Sam P.

2012-01-01

Polymicrobial interactions are widespread in nature, and play a major role in maintaining human health and ecosystems. Whenever one organism uses metabolites produced by another organism as energy or nutrient sources, this is called cross-feeding. The ecological outcomes of cross-feeding interactions are poorly understood and potentially diverse: mutualism, competition, exploitation or commensalism. A major reason for this uncertainty is the lack of theoretical approaches linking microbial metabolism to microbial ecology. To address this issue, we explore the dynamics of a one-way interspecific cross-feeding interaction, in which food can be traded for a service (detoxification). Our results show that diverse ecological interactions (competition, mutualism, exploitation) can emerge from this simple cross-feeding interaction, and can be predicted by the metabolic, demographic and environmental parameters that govern the balance of the costs and benefits of association. In particular, our model predicts stronger mutualism for intermediate by-product toxicity because the resource-service exchange is constrained to the service being neither too vital (high toxicity impairs resource provision) nor dispensable (low toxicity reduces need for service). These results support the idea that bridging microbial ecology and metabolism is a critical step towards a better understanding of the factors governing the emergence and dynamics of polymicrobial interactions. PMID:23070318

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.

Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Tsvetkova, Olga

2011-06-01

SummaryLand use change is influenced by spatial and temporal factors that interact with watershed resources. Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in water quantity and quality. The objective of this study is to model the nature and emergence of spatial patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simulation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results show high potential for rapid urbanization that could result in lowering of groundwater recharge and increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that affect open space and pervious cover of the watershed system. Water quality deteriorated due to increased runoff which can also impact stream morphology. While overland erosion decreased, instream erosion increased from increased runoff from urban areas. Use of urban best management practices (BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful in sustaining the watershed system.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

Cancer.gov

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

Enhanced Chiral Discriminatory van der Waals Interactions Mediated by Chiral Surfaces

NASA Astrophysics Data System (ADS)

Barcellona, Pablo; Safari, Hassan; Salam, A.; Buhmann, Stefan Yoshi

2017-05-01

We predict a discriminatory interaction between a chiral molecule and an achiral molecule which is mediated by a chiral body. To achieve this, we generalize the van der Waals interaction potential between two ground-state molecules with electric, magnetic, and chiral response to nontrivial environments. The force is evaluated using second-order perturbation theory with an effective Hamiltonian. Chiral media enhance or reduce the free interaction via many-body interactions, making it possible to measure the chiral contributions to the van der Waals force with current technology. The van der Waals interaction is discriminatory with respect to enantiomers of different handedness and could be used to separate enantiomers. We also suggest a specific geometric configuration where the electric contribution to the van der Waals interaction is zero, making the chiral component the dominant effect.

Thermal Change and the Dynamics of Multi-Host Parasite Life Cycles in Aquatic Ecosystems.

PubMed

Barber, Iain; Berkhout, Boris W; Ismail, Zalina

2016-10-01

Altered thermal regimes associated with climate change are impacting significantly on the physical, chemical, and biological characteristics of the Earth's natural ecosystems, with important implications for the biology of aquatic organisms. As well as impacting the biology of individual species, changing thermal regimes have the capacity to mediate ecological interactions between species, and the potential for climate change to impact host-parasite interactions in aquatic ecosystems is now well recognized. Predicting what will happen to the prevalence and intensity of infection of parasites with multiple hosts in their life cycles is especially challenging because the addition of each additional host dramatically increases the potential permutations of response. In this short review, we provide an overview of the diverse routes by which altered thermal regimes can impact the dynamics of multi-host parasite life cycles in aquatic ecosystems. In addition, we examine how experimentally amenable host-parasite systems are being used to determine the consequences of changing environmental temperatures for these different types of mechanism. Our overarching aim is to examine the potential of changing thermal regimes to alter not only the biology of hosts and parasites, but also the biology of interactions between hosts and parasites. We also hope to illustrate the complexity that is likely to be involved in making predictions about the dynamics of infection by multi-host parasites in thermally challenged aquatic ecosystems. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.

Electrostatic roles in electron transfer from [NiFe] hydrogenase to cytochrome c3 from Desulfovibrio vulgaris Miyazaki F.

PubMed

Sugimoto, Yu; Kitazumi, Yuki; Shirai, Osamu; Nishikawa, Koji; Higuchi, Yoshiki; Yamamoto, Masahiro; Kano, Kenji

2017-05-01

Electrostatic interactions between proteins are key factors that govern the association and reaction rate. We spectroscopically determine the second-order reaction rate constant (k) of electron transfer from [NiFe] hydrogenase (H 2 ase) to cytochrome (cyt) c 3 at various ionic strengths (I). The k value decreases with I. To analyze the results, we develop a semi-analytical formula for I dependence of k based on the assumptions that molecules are spherical and the reaction proceeds via a transition state. Fitting of the formula to the experimental data reveals that the interaction occurs in limited regions with opposite charges and with radii much smaller than those estimated from crystal structures. This suggests that local charges in H 2 ase and cyt c 3 play important roles in the reaction. Although the crystallographic data indicate a positive electrostatic potential over almost the entire surface of the proteins, there exists a small region with negative potential on H 2 ase at which the electron transfer from H 2 ase to cyt c 3 may occur. This local negative potential region is identical to the hypothetical interaction sphere predicted by the analysis. Furthermore, I dependence of k is predicted by the Adaptive Poisson-Boltzmann Solver considering all charges of the amino acids in the proteins and the configuration of H 2 ase/cyt c 3 complex. The calculation reproduces the experimental results except at extremely low I. These results indicate that the stabilization derived from the local electrostatic interaction in the H 2 ase/cyt c 3 complex overcomes the destabilization derived from the electrostatic repulsion of the overall positive charge of both proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation

PubMed Central

Pagliaccio, David; Luby, Joan L.; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S.; Belden, Andrew C.; Botteron, Kelly N.; Harms, Michael P.; Barch, Deanna M.

2015-01-01

Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within four hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9–14 year olds; N=120). Whole-brain regression analyses indicated that increasing genetic ‘risk’ predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic ‘risk’ and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. PMID:26595470

Amygdala functional connectivity, HPA axis genetic variation, and life stress in children and relations to anxiety and emotion regulation.

PubMed

Pagliaccio, David; Luby, Joan L; Bogdan, Ryan; Agrawal, Arpana; Gaffrey, Michael S; Belden, Andrew C; Botteron, Kelly N; Harms, Michael P; Barch, Deanna M

2015-11-01

Internalizing pathology is related to alterations in amygdala resting state functional connectivity, potentially implicating altered emotional reactivity and/or emotion regulation in the etiological pathway. Importantly, there is accumulating evidence that stress exposure and genetic vulnerability impact amygdala structure/function and risk for internalizing pathology. The present study examined whether early life stress and genetic profile scores (10 single nucleotide polymorphisms within 4 hypothalamic-pituitary-adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predicted individual differences in amygdala functional connectivity in school-age children (9- to 14-year-olds; N = 120). Whole-brain regression analyses indicated that increasing genetic "risk" predicted alterations in amygdala connectivity to the caudate and postcentral gyrus. Experience of more stressful and traumatic life events predicted weakened amygdala-anterior cingulate cortex connectivity. Genetic "risk" and stress exposure interacted to predict weakened connectivity between the amygdala and the inferior and middle frontal gyri, caudate, and parahippocampal gyrus in those children with the greatest genetic and environmental risk load. Furthermore, amygdala connectivity longitudinally predicted anxiety symptoms and emotion regulation skills at a later follow-up. Amygdala connectivity mediated effects of life stress on anxiety and of genetic variants on emotion regulation. The current results suggest that considering the unique and interacting effects of biological vulnerability and environmental risk factors may be key to understanding the development of altered amygdala functional connectivity, a potential factor in the risk trajectory for internalizing pathology. (c) 2015 APA, all rights reserved).

Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments.

PubMed

Zhuang, Kai; Izallalen, Mounir; Mouser, Paula; Richter, Hanno; Risso, Carla; Mahadevan, Radhakrishnan; Lovley, Derek R

2011-02-01

The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.

Can the vector space model be used to identify biological entity activities?

PubMed Central

2011-01-01

Background Biological systems are commonly described as networks of entity interactions. Some interactions are already known and integrate the current knowledge in life sciences. Others remain unknown for long periods of time and are frequently discovered by chance. In this work we present a model to predict these unknown interactions from a textual collection using the vector space model (VSM), a well known and established information retrieval model. We have extended the VSM ability to retrieve information using a transitive closure approach. Our objective is to use the VSM to identify the known interactions from the literature and construct a network. Based on interactions established in the network our model applies the transitive closure in order to predict and rank new interactions. Results We have tested and validated our model using a collection of patent claims issued from 1976 to 2005. From 266,528 possible interactions in our network, the model identified 1,027 known interactions and predicted 3,195 new interactions. Iterating the model according to patent issue dates, interactions found in a given past year were often confirmed by patent claims not in the collection and issued in more recent years. Most confirmation patent claims were found at the top 100 new interactions obtained from each subnetwork. We have also found papers on the Web which confirm new inferred interactions. For instance, the best new interaction inferred by our model relates the interaction between the adrenaline neurotransmitter and the androgen receptor gene. We have found a paper that reports the partial dependence of the antiapoptotic effect of adrenaline on androgen receptor. Conclusions The VSM extended with a transitive closure approach provides a good way to identify biological interactions from textual collections. Specifically for the context of literature-based discovery, the extended VSM contributes to identify and rank relevant new interactions even if these interactions occcur in only a few documents in the collection. Consequently, we have developed an efficient method for extracting and restricting the best potential results to consider as new advances in life sciences, even when indications of these results are not easily observed from a mass of documents. PMID:22369514

The adiabatic energy change of plasma electrons and the frame dependence of the cross-shock potential at collisionless magnetosonic shock waves

NASA Technical Reports Server (NTRS)

Goodrich, C. C.; Scudder, J. D.

1984-01-01

The adiabatic energy gain of electrons in the stationary electric and magnetic field structure of collisionless shock waves was examined analytically in reference to conditions of the earth's bow shock. The study was performed to characterize the behavior of electrons interacting with the cross-shock potential. A normal incidence frame (NIF) was adopted in order to calculate the reversible energy change across a time stationary shock, and comparisons were made with predictions made by the de Hoffman-Teller (HT) model (1950). The electron energy gain, about 20-50 eV, is demonstrated to be consistent with a 200-500 eV potential jump in the bow shock quasi-perpendicular geometry. The electrons lose energy working against the solar wind motional electric field. The reversible energy process is close to that modeled by HT, which predicts that the motional electric field vanishes and the electron energy gain from the electric potential is equated to the ion energy loss to the potential.

Interactive effects of an insecticide and a fungicide on different organism groups and ecosystem functioning in a stream detrital food web.

PubMed

Dawoud, Mohab; Bundschuh, Mirco; Goedkoop, Willem; McKie, Brendan G

2017-05-01

Freshwater ecosystems are often affected by cocktails of multiple pesticides targeting different organism groups. Prediction and evaluation of the ecosystem-level effects of these mixtures is complicated by the potential not only for interactions among the pesticides themselves, but also for the pesticides to alter biotic interactions across trophic levels. In a stream microcosm experiment, we investigated the effects of two pesticides targeting two organism groups (the insecticide lindane and fungicide azoxystrobin) on the functioning of a model stream detrital food web consisting of a detritivore (Ispoda: Asellus aquaticus) and microbes (an assemblage of fungal hyphomycetes) consuming leaf litter. We assessed how these pesticides interacted with the presence and absence of the detritivore to affect three indicators of ecosystem functioning - leaf decomposition, fungal biomass, fungal sporulation - as well as detritivore mortality. Leaf decomposition rates were more strongly impacted by the fungicide than the insecticide, reflecting especially negative effects on leaf processing by detritivores. This result most like reflects reduced fungal biomass and increased detritivore mortality under the fungicide treatment. Fungal sporulation was elevated by exposure to both the insecticide and fungicide, possibly representing a stress-induced increase in investment in propagule dispersal. Stressor interactions were apparent in the impacts of the combined pesticide treatment on fungal sporulation and detritivore mortality, which were reduced and elevated relative to the single stressor treatments, respectively. These results demonstrate the potential of trophic and multiple stressor interactions to modulate the ecosystem-level impacts of chemicals, highlighting important challenges in predicting, understanding and evaluating the impacts of multiple chemical stressors on more complex food webs in situ. Copyright © 2017 Elsevier B.V. All rights reserved.

Plasma Flowfields Around Low Earth Orbit Objects: Aerodynamics to Underpin Orbit Predictions

NASA Astrophysics Data System (ADS)

Capon, Christopher; Boyce, Russell; Brown, Melrose

2016-07-01

Interactions between orbiting bodies and the charged space environment are complex. The large variation in passive body parameters e.g. size, geometry and materials, makes the plasma-body interaction in Low Earth Orbit (LEO) a region rich in fundamental physical phenomena. The aerodynamic interaction of LEO orbiting bodies with the neutral environment constitutes the largest non-conservative force on the body. However in general, study of the LEO plasma-body interaction has not been concerned with external flow physics, but rather with the effects on surface charging. The impact of ionospheric flow physics on the forces on space debris (and active objects) is not well understood. The work presented here investigates the contribution that plasma-body interactions have on the flow structure and hence on the total atmospheric force vector experienced by a polar orbiting LEO body. This work applies a hybrid Particle-in-Cell (PIC) - Direct Simulation Monte Carlo (DSMC) code, pdFoam, to self-consistently model the electrostatic flowfield about a cylinder with a uniform, fixed surface potential. Flow conditions are representative of the mean conditions experienced by the Earth Observing Satellite (EOS) based on the International Reference Ionosphere model (IRI-86). The electron distribution function is represented by a non-linear Boltzmann electron fluid and ion gas-surface interactions are assumed to be that of a neutralising, conducting, thermally accommodating solid wall with diffuse reflections. The variation in flowfield and aerodynamic properties with surface potential at a fixed flow condition is investigated, and insight into the relative contributions of charged and neutral species to the flow physics experienced by a LEO orbiting body is provided. This in turn is intended to help improve the fidelity of physics-based orbit predictions for space debris and other near-Earth space objects.

Effective interactions in lysozyme aqueous solutions: a small-angle neutron scattering and computer simulation study.

PubMed

Abramo, M C; Caccamo, C; Costa, D; Pellicane, G; Ruberto, R; Wanderlingh, U

2012-01-21

We report protein-protein structure factors of aqueous lysozyme solutions at different pH and ionic strengths, as determined by small-angle neutron scattering experiments. The observed upturn of the structure factor at small wavevectors, as the pH increases, marks a crossover between two different regimes, one dominated by repulsive forces, and another one where attractive interactions become prominent, with the ensuing development of enhanced density fluctuations. In order to rationalize such experimental outcome from a microscopic viewpoint, we have carried out extensive simulations of different coarse-grained models. We have first studied a model in which macromolecules are described as soft spheres interacting through an attractive r(-6) potential, plus embedded pH-dependent discrete charges; we show that the uprise undergone by the structure factor is qualitatively predicted. We have then studied a Derjaguin-Landau-Verwey-Overbeek (DLVO) model, in which only central interactions are advocated; we demonstrate that this model leads to a protein-rich/protein-poor coexistence curve that agrees quite well with the experimental counterpart; experimental correlations are instead reproduced only at low pH and ionic strengths. We have finally investigated a third, "mixed" model in which the central attractive term of the DLVO potential is imported within the distributed-charge approach; it turns out that the different balance of interactions, with a much shorter-range attractive contribution, leads in this latter case to an improved agreement with the experimental crossover. We discuss the relationship between experimental correlations, phase coexistence, and features of effective interactions, as well as possible paths toward a quantitative prediction of structural properties of real lysozyme solutions. © 2012 American Institute of Physics

A Simulated Environment Experiment on Annoyance Due to Combined Road Traffic and Industrial Noises

PubMed Central

Marquis-Favre, Catherine; Morel, Julien

2015-01-01

Total annoyance due to combined noises is still difficult to predict adequately. This scientific gap is an obstacle for noise action planning, especially in urban areas where inhabitants are usually exposed to high noise levels from multiple sources. In this context, this work aims to highlight potential to enhance the prediction of total annoyance. The work is based on a simulated environment experiment where participants performed activities in a living room while exposed to combined road traffic and industrial noises. The first objective of the experiment presented in this paper was to gain further understanding of the effects on annoyance of some acoustical factors, non-acoustical factors and potential interactions between the combined noise sources. The second one was to assess total annoyance models constructed from the data collected during the experiment and tested using data gathered in situ. The results obtained in this work highlighted the superiority of perceptual models. In particular, perceptual models with an interaction term seemed to be the best predictors for the two combined noise sources under study, even with high differences in sound pressure level. Thus, these results reinforced the need to focus on perceptual models and to improve the prediction of partial annoyances. PMID:26197326

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

Jaganathen, Y.; Betan, R. M. Id; Michel, N.

Background: The structure of weakly bound and unbound nuclei close to particle drip lines is one of the major science drivers of nuclear physics. A comprehensive understanding of these systems goes beyond the traditional configuration interaction approach formulated in the Hilbert space of localized states (nuclear shell model) and requires an open quantum system description. The complex-energy Gamow shell model (GSM) provides such a framework as it is capable of describing resonant and nonresonant many-body states on equal footing. Purpose: To make reliable predictions, quality input is needed that allows for the full uncertainty quantification of theoretical results. In thismore » study, we carry out the optimization of an effective GSM (one-body and two-body) interaction in the psdf-shell-model space. The resulting interaction is expected to describe nuclei with 5 ≤ A ≲ 12 at the p-sd-shell interface. Method: The one-body potential of the 4He core is modeled by a Woods-Saxon + spin-orbit + Coulomb potential, and the finite-range nucleon-nucleon interaction between the valence nucleons consists of central, spin-orbit, tensor, and Coulomb terms. The GSM is used to compute key fit observables. The χ 2 optimization is performed using the Gauss-Newton algorithm augmented by the singular value decomposition technique. The resulting covariance matrix enables quantification of statistical errors within the linear regression approach. Results: The optimized one-body potential reproduces nucleon- 4He scattering phase shifts up to an excitation energy of 20 MeV. The two-body interaction built on top of the optimized one-body field is adjusted to the bound and unbound ground-state binding energies and selected excited states of the helium, lithium, and beryllium isotopes up to A = 9 . A very good agreement with experimental results was obtained for binding energies. First applications of the optimized interaction include predictions for two-nucleon correlation densities and excitation spectra of light nuclei with quantified uncertainties. In conclusion: The new interaction will enable comprehensive and fully quantified studies of structure and reactions aspects of nuclei from the psd region of the nuclear chart.« less

The influence of coarse-scale environmental features on current and predicted future distributions of narrow-range endemic crayfish populations

USGS Publications Warehouse

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

2013-01-01

1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario, whereas two of four species would be severely restricted in range under moderatehigh emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species-distribution models. 6.These model predictions illustrate possible impacts of climate change on narrow-range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.

Interatomic potential at small internuclear distances. A simple formula for the screening constant

NASA Astrophysics Data System (ADS)

Zinoviev, A. N.

2017-09-01

A simple formula for estimating the screening constant has been proposed. This formula fits well experimental data on the interaction potentials. Quantitative description of the experiment for the effect of electronic screening on the nuclear synthesis reaction cross-section for the D+-D system has been obtained. A conclusion has been made that the differences between the measured cross-sections and their theoretically predicted values, which take place in more complicated cases nuclear synthesis reactions, are not caused by uncertainties in the knowledge of potentials.

Functional Coupling of Human Microphysiology Systems: Intestine, Liver, Kidney Proximal Tubule, Blood-Brain Barrier and Skeletal Muscle

PubMed Central

Vernetti, Lawrence; Gough, Albert; Baetz, Nicholas; Blutt, Sarah; Broughman, James R.; Brown, Jacquelyn A.; Foulke-Abel, Jennifer; Hasan, Nesrin; In, Julie; Kelly, Edward; Kovbasnjuk, Olga; Repper, Jonathan; Senutovitch, Nina; Stabb, Janet; Yeung, Catherine; Zachos, Nick C.; Donowitz, Mark; Estes, Mary; Himmelfarb, Jonathan; Truskey, George; Wikswo, John P.; Taylor, D. Lansing

2017-01-01

Organ interactions resulting from drug, metabolite or xenobiotic transport between organs are key components of human metabolism that impact therapeutic action and toxic side effects. Preclinical animal testing often fails to predict adverse outcomes arising from sequential, multi-organ metabolism of drugs and xenobiotics. Human microphysiological systems (MPS) can model these interactions and are predicted to dramatically improve the efficiency of the drug development process. In this study, five human MPS models were evaluated for functional coupling, defined as the determination of organ interactions via an in vivo-like sequential, organ-to-organ transfer of media. MPS models representing the major absorption, metabolism and clearance organs (the jejunum, liver and kidney) were evaluated, along with skeletal muscle and neurovascular models. Three compounds were evaluated for organ-specific processing: terfenadine for pharmacokinetics (PK) and toxicity; trimethylamine (TMA) as a potentially toxic microbiome metabolite; and vitamin D3. We show that the organ-specific processing of these compounds was consistent with clinical data, and discovered that trimethylamine-N-oxide (TMAO) crosses the blood-brain barrier. These studies demonstrate the potential of human MPS for multi-organ toxicity and absorption, distribution, metabolism and excretion (ADME), provide guidance for physically coupling MPS, and offer an approach to coupling MPS with distinct media and perfusion requirements. PMID:28176881

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

PubMed

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy*

PubMed Central

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-01-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r 2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. PMID:15682498

Climate Change and a Global City: An Assessment of the Metropolitan East Coast Region

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia; Solecki, William

1999-01-01

The objective of the research is to derive an assessment of the potential climate change impacts on a global city - in this case the 31 county region that comprises the New York City metropolitan area. This study comprises one of the regional components that contribute to the ongoing U.S. National Assessment: The Potential Consequences of Climate Variability and Change and is an application of state-of-the-art climate change science to a set of linked sectoral assessment analyses for the Metro East Coast (MEC) region. We illustrate how three interacting elements of global cities react and respond to climate variability and change with a broad conceptual model. These elements include: people (e.g., socio- demographic conditions), place (e.g., physical systems), and pulse (e.g., decision-making and economic activities). The model assumes that a comprehensive assessment of potential climate change can be derived from examining the impacts within each of these elements and at their intersections. Thus, the assessment attempts to determine the within-element and the inter-element effects. Five interacting sector studies representing the three intersecting elements are evaluated. They include the Coastal Zone, Infrastructure, Water Supply, Public Health, and Institutional Decision-making. Each study assesses potential climate change impacts on the sector and on the intersecting elements, through the analysis of the following parts: 1. Current conditions of sector in the region; 2. Lessons and evidence derived from past climate variability; 3. Scenario predictions affecting sector; potential impacts of scenario predictions; 4. Knowledge/information gaps and critical issues including identification of additional research questions, effectiveness of modeling efforts, equity of impacts, potential non-local interactions, and policy recommendations; and 5. Identification of coping strategies - i.e., resilience building, mitigation strategies, new technologies, education that affects decision-making, and better preparedness for contingencies.

Self-diffusion in a system of interacting Langevin particles

NASA Astrophysics Data System (ADS)

Dean, D. S.; Lefèvre, A.

2004-06-01

The behavior of the self-diffusion constant of Langevin particles interacting via a pairwise interaction is considered. The diffusion constant is calculated approximately within a perturbation theory in the potential strength about the bare diffusion constant. It is shown how this expansion leads to a systematic double expansion in the inverse temperature β and the particle density ρ . The one-loop diagrams in this expansion can be summed exactly and we show that this result is exact in the limit of small β and ρβ constants. The one-loop result can also be resummed using a semiphenomenological renormalization group method which has proved useful in the study of diffusion in random media. In certain cases the renormalization group calculation predicts the existence of a diverging relaxation time signaled by the vanishing of the diffusion constant, possible forms of divergence coming from this approximation are discussed. Finally, at a more quantitative level, the results are compared with numerical simulations, in two dimensions, of particles interacting via a soft potential recently used to model the interaction between coiled polymers.

Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment.

PubMed

Burns, Michael B; Montassier, Emmanuel; Abrahante, Juan; Priya, Sambhawa; Niccum, David E; Khoruts, Alexander; Starr, Timothy K; Knights, Dan; Blekhman, Ran

2018-06-20

Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. In conclusion, our study shows that CRC microbiomes are correlated with tumor mutational profiles, pointing towards possible mechanisms of molecular interaction.

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

PubMed

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

2014-03-01

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

Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

PubMed Central

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

2014-01-01

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

Poverty, AIDS and child health: identifying highest-risk children in South Africa.

PubMed

Cluver, Lucie; Boyes, Mark; Orkin, Mark; Sherr, Lorraine

2013-10-11

Identifying children at the highest risk of negative health effects is a prerequisite to effective public health policies in Southern Africa. A central ongoing debate is whether poverty, orphanhood or parental AIDS most reliably indicates child health risks. Attempts to address this key question have been constrained by a lack of data allowing distinction of AIDS-specific parental death or morbidity from other causes of orphanhood and chronic illness. To examine whether household poverty, orphanhood and parental illness (by AIDS or other causes) independently or interactively predict child health, developmental and HIV-infection risks. We interviewed 6 002 children aged 10 - 17 years in 2009 - 2011, using stratified random sampling in six urban and rural sites across three South African provinces. Outcomes were child mental health risks, educational risks and HIV-infection risks. Regression models that controlled for socio-demographic co-factors tested potential impacts and interactions of poverty, AIDS-specific and other orphanhood and parental illness status. Household poverty independently predicted child mental health and educational risks, AIDS orphanhood independently predicted mental health risks and parental AIDS illness independently predicted mental health, educational and HIV-infection risks. Interaction effects of poverty with AIDS orphanhood and parental AIDS illness were found across all outcomes. No effects, or interactions with poverty, were shown by AIDS-unrelated orphanhood or parental illness. The identification of children at highest risk requires recognition and measurement of both poverty and parental AIDS. This study shows negative impacts of poverty and AIDS-specific vulnerabilities distinct from orphanhood and adult illness more generally. Additionally, effects of interaction between family AIDS and poverty suggest that, where these co-exist, children are at highest risk of all.

A Computational and Experimental Study of Nonlinear Aspects of Induced Drag

NASA Technical Reports Server (NTRS)

Smith, Stephen C.

1996-01-01

Despite the 80-year history of classical wing theory, considerable research has recently been directed toward planform and wake effects on induced drag. Nonlinear interactions between the trailing wake and the wing offer the possibility of reducing drag. The nonlinear effect of compressibility on induced drag characteristics may also influence wing design. This thesis deals with the prediction of these nonlinear aspects of induced drag and ways to exploit them. A potential benefit of only a few percent of the drag represents a large fuel savings for the world's commercial transport fleet. Computational methods must be applied carefully to obtain accurate induced drag predictions. Trefftz-plane drag integration is far more reliable than surface pressure integration, but is very sensitive to the accuracy of the force-free wake model. The practical use of Trefftz plane drag integration was extended to transonic flow with the Tranair full-potential code. The induced drag characteristics of a typical transport wing were studied with Tranair, a full-potential method, and A502, a high-order linear panel method to investigate changes in lift distribution and span efficiency due to compressibility. Modeling the force-free wake is a nonlinear problem, even when the flow governing equation is linear. A novel method was developed for computing the force-free wake shape. This hybrid wake-relaxation scheme couples the well-behaved nature of the discrete vortex wake with viscous-core modeling and the high-accuracy velocity prediction of the high-order panel method. The hybrid scheme produced converged wake shapes that allowed accurate Trefftz-plane integration. An unusual split-tip wing concept was studied for exploiting nonlinear wake interaction to reduced induced drag. This design exhibits significant nonlinear interactions between the wing and wake that produced a 12% reduction in induced drag compared to an equivalent elliptical wing at a lift coefficient of 0.7. The performance of the split-tip wing was also investigated by wing tunnel experiments. Induced drag was determined from force measurements by subtracting the estimated viscous drag, and from an analytical drag-decomposition method using a wake survey. The experimental results confirm the computational prediction.

Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins.

PubMed

Gunner, M R; Baker, N A

2016-01-01

Proteins change their charge state through protonation and redox reactions as well as through binding charged ligands. The free energy of these reactions is dominated by solvation and electrostatic energies and modulated by protein conformational relaxation in response to the ionization state changes. Although computational methods for calculating these interactions can provide very powerful tools for predicting protein charge states, they include several critical approximations of which users should be aware. This chapter discusses the strengths, weaknesses, and approximations of popular computational methods for predicting charge states and understanding the underlying electrostatic interactions. The goal of this chapter is to inform users about applications and potential caveats of these methods as well as outline directions for future theoretical and computational research. © 2016 Elsevier Inc. All rights reserved.

Molecular epidemiology, and possible real-world applications in breast cancer.

PubMed

Ito, Hidemi; Matsuo, Keitaro

2016-01-01

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Expensive Egos: Narcissistic Males Have Higher Cortisol

PubMed Central

Reinhard, David A.; Konrath, Sara H.; Lopez, William D.; Cameron, Heather G.

2012-01-01

Background Narcissism is characterized by grandiosity, low empathy, and entitlement. There has been limited research regarding the hormonal correlates of narcissism, despite the potential health implications. This study examined the role of participant narcissism and sex on basal cortisol concentrations in an undergraduate population. Methods and Findings Participants were 106 undergraduate students (79 females, 27 males, mean age 20.1 years) from one Midwestern and one Southwestern American university. Narcissism was assessed using the Narcissistic Personality Inventory, and basal cortisol concentrations were collected from saliva samples in a laboratory setting. Regression analyses examined the effect of narcissism and sex on cortisol (log). There were no sex differences in basal cortisol, F(1,97) = .20, p = .65, and narcissism scores, F(1,97) = .00, p = .99. Stepwise linear regression models of sex and narcissism and their interaction predicting cortisol concentrations showed no main effects when including covariates, but a significant interaction, β = .27, p = .04. Narcissism was not related to cortisol in females, but significantly predicted cortisol in males. Examining the effect of unhealthy versus healthy narcissism on cortisol found that unhealthy narcissism was marginally related to cortisol in females, β = .27, p = .06, but significantly predicted higher basal cortisol in males, β = .72, p = .01, even when controlling for potential confounds. No relationship was found between sex, narcissism, or their interaction on self-reported stress. Conclusions Our findings suggest that the HPA axis is chronically activated in males with unhealthy narcissism. This constant activation of the HPA axis may have important health implications. PMID:22292062

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs

PubMed Central

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-01-01

Abstract We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\mathcal {E}$\\end{document}SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base–phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation. PMID:29272539

Predicting Parent-Child Aggression Risk: Cognitive Factors and Their Interaction With Anger.

PubMed

Rodriguez, Christina M

2018-02-01

Several cognitive elements have previously been proposed to elevate risk for physical child abuse. To predict parent-child aggression risk, the current study evaluated the role of approval of parent-child aggression, perceptions of children as poorly behaved, and discipline attributions. Several dimensions of attributions specifically tied to parents' discipline practices were targeted. In addition, anger experienced during discipline episodes was considered a potential moderator of these cognitive processes. Using a largely multiple-indicator approach, a sample of 110 mothers reported on these cognitive and affective aspects that may occur when disciplining their children as well as responding to measures of parent-child aggression risk. Findings suggest that greater approval of parent-child aggression, negative perceptions of their child's behavior, and discipline attributions independently predicted parent-child aggression risk, with anger significantly interacting with mothers' perception of their child as more poorly behaved to exacerbate their parent-child aggression risk. Of the discipline attribution dimensions evaluated, mothers' sense of external locus of control and believing their child deserved their discipline were related to increase parent-child aggression risk. Future work is encouraged to comprehensively evaluate how cognitive and affective components contribute and interact to increase risk for parent-child aggression.

Effect of Amphiphiles on the Rheology of Triglyceride Networks

NASA Astrophysics Data System (ADS)

Seth, Jyoti

2014-11-01

Networks of aggregated crystallites form the structural backbone of many products from the food, cosmetic and pharmaceutical industries. Such materials are generally formulated by cooling a saturated solution to yield the desired solid fraction. Crystal nucleation and growth followed by aggregation leads to formation of a space percolating fractal-network. It is understood that microstructural hierarchy and particle-particle interactions determine material behavior during processing, storage and use. In this talk, rheology of suspensions of triglycerides (TAG, like tristearin) will be explored. TAGs exhibit a rich assortment of polymorphs and form suspensions that are evidently sensitive to surface modifying additives like surfactants and polymers. Here, a theoretical framework will be presented for suspensions containing TAG crystals interacting via pairwise potentials. The work builds on existing models of fractal aggregates to understand microstructure and its correlation with material rheology. Effect of amphiphilic additives is derived through variation of particle-particle interactions. Theoretical predictions for storage modulus will be compared against experimental observations and data from the literature and micro structural predictions against microscopy. Such a theory may serve as a step towards predicting short and long-term behavior of aggregated suspensions formulated via crystallization.

Effect of matrix chemical heterogeneity on effective filler interactions in model polymer nanocomposites

NASA Astrophysics Data System (ADS)

Hall, Lisa; Schweizer, Kenneth

2010-03-01

The microscopic Polymer Reference Interaction Site Model theory has been applied to spherical and rodlike fillers dissolved in three types of chemically heterogeneous polymer melts: alternating AB copolymer, random AB copolymers, and an equimolar blend of two homopolymers. In each case, one monomer species adsorbs more strongly on the filler mimicking a specific attraction, while all inter-monomer potentials are hard core which precludes macrophase or microphase separation. Qualitative differences in the filler potential-of-mean force are predicted relative to the homopolymer case. The adsorbed bound layer for alternating copolymers exhibits a spatial moduluation or layering effect but is otherwise similar to that of the homopolymer system. Random copolymers and the polymer blend mediate a novel strong, long-range bridging interaction between fillers at moderate to high adsorption strengths. The bridging strength is a non-monotonic function of random copolymer composition, reflecting subtle competing enthalpic and entropic considerations.

Transferable Pseudo-Classical Electrons for Aufbau of Atomic Ions

PubMed Central

Ekesan, Solen; Kale, Seyit; Herzfeld, Judith

2014-01-01

Generalizing the LEWIS reactive force field from electron pairs to single electrons, we present LEWIS• in which explicit valence electrons interact with each other and with nuclear cores via pairwise interactions. The valence electrons are independently mobile particles, following classical equations of motion according to potentials modified from Coulombic as required to capture quantum characteristics. As proof of principle, the aufbau of atomic ions is described for diverse main group elements from the first three rows of the periodic table, using a single potential for interactions between electrons of like spin and another for electrons of unlike spin. The electrons of each spin are found to distribute themselves in a fashion akin to the major lobes of the hybrid atomic orbitals, suggesting a pointillist description of the electron density. The broader validity of the LEWIS• force field is illustrated by predicting the vibrational frequencies of diatomic and triatomic hydrogen species. PMID:24752384

Transferable pseudoclassical electrons for aufbau of atomic ions.

PubMed

Ekesan, Solen; Kale, Seyit; Herzfeld, Judith

2014-06-05

Generalizing the LEWIS reactive force field from electron pairs to single electrons, we present LEWIS• in which explicit valence electrons interact with each other and with nuclear cores via pairwise interactions. The valence electrons are independently mobile particles, following classical equations of motion according to potentials modified from Coulombic as required to capture quantum characteristics. As proof of principle, the aufbau of atomic ions is described for diverse main group elements from the first three rows of the periodic table, using a single potential for interactions between electrons of like spin and another for electrons of unlike spin. The electrons of each spin are found to distribute themselves in a fashion akin to the major lobes of the hybrid atomic orbitals, suggesting a pointillist description of the electron density. The broader validity of the LEWIS• force field is illustrated by predicting the vibrational frequencies of diatomic and triatomic hydrogen species. Copyright © 2014 Wiley Periodicals, Inc.

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

A Prediction Method of Binding Free Energy of Protein and Ligand

NASA Astrophysics Data System (ADS)

Yang, Kun; Wang, Xicheng

2010-05-01

Predicting the binding free energy is an important problem in bimolecular simulation. Such prediction would be great benefit in understanding protein functions, and may be useful for computational prediction of ligand binding strengths, e.g., in discovering pharmaceutical drugs. Free energy perturbation (FEP)/thermodynamics integration (TI) is a classical method to explicitly predict free energy. However, this method need plenty of time to collect datum, and that attempts to deal with some simple systems and small changes of molecular structures. Another one for estimating ligand binding affinities is linear interaction energy (LIE) method. This method employs averages of interaction potential energy terms from molecular dynamics simulations or other thermal conformational sampling techniques. Incorporation of systematic deviations from electrostatic linear response, derived from free energy perturbation studies, into the absolute binding free energy expression significantly enhances the accuracy of the approach. However, it also is time-consuming work. In this paper, a new prediction method based on steered molecular dynamics (SMD) with direction optimization is developed to compute binding free energy. Jarzynski's equality is used to derive the PMF or free-energy. The results for two numerical examples are presented, showing that the method has good accuracy and efficiency. The novel method can also simulate whole binding proceeding and give some important structural information about development of new drugs.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Discovery and replication of a peripheral tissue DNA methylation biosignature to augment a suicide prediction model.

PubMed

Clive, Makena L; Boks, Marco P; Vinkers, Christiaan H; Osborne, Lauren M; Payne, Jennifer L; Ressler, Kerry J; Smith, Alicia K; Wilcox, Holly C; Kaminsky, Zachary

2016-01-01

Suicide is the second leading cause of death among adolescents in the USA, and rates are rising. Methods to identify individuals at risk are essential for implementing prevention strategies, and the development of a biomarker can potentially improve prediction of suicidal behaviors. Prediction of our previously reported SKA2 biomarker for suicide and PTSD is substantially improved by questionnaires assessing perceived stress or anxiety and is therefore reliant on psychological assessment. However, such stress-related states may also leave a biosignature that could equally improve suicide prediction. In genome-wide DNA methylation data, we observed significant overlap between waking cortisol-associated and suicide-associated DNA methylation in blood and the brain, respectively. Using a custom bioinformatic brain to blood discovery algorithm, we derived a DNA methylation biosignature that interacts with SKA2 methylation to improve the prediction of suicidal ideation in our existing suicide prediction model across both blood and saliva data sets. This biosignature was independently validated in the Grady Trauma Project cohort and interacted with HPA axis metrics in the same cohort. The biosignature showed a relationship with immune status by its correlation with myeloid-derived cell proportions in all data sets and with IL-6 measures in a prospective postpartum depression cohort. Three probes showed significant correlations with the biosignature: cg08469255 ( DDR1 ), cg22029879 ( ARHGEF10 ), and cg24437859 ( SHP1 ), of which SHP1 methylation correlated with immune measures. We conclude that this biosignature interacts with SKA2 methylation to improve suicide prediction and may represent a biological state of immune and HPA axis modulation that mediates suicidal behavior.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

The role of electrostatics in protein-protein interactions of a monoclonal antibody.

PubMed

Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

2014-07-07

Understanding how protein-protein interactions depend on the choice of buffer, salt, ionic strength, and pH is needed to have better control over protein solution behavior. Here, we have characterized the pH and ionic strength dependence of protein-protein interactions in terms of an interaction parameter kD obtained from dynamic light scattering and the osmotic second virial coefficient B22 measured by static light scattering. A simplified protein-protein interaction model based on a Baxter adhesive potential and an electric double layer force is used to separate out the contributions of longer-ranged electrostatic interactions from short-ranged attractive forces. The ionic strength dependence of protein-protein interactions for solutions at pH 6.5 and below can be accurately captured using a Deryaguin-Landau-Verwey-Overbeek (DLVO) potential to describe the double layer forces. In solutions at pH 9, attractive electrostatics occur over the ionic strength range of 5-275 mM. At intermediate pH values (7.25 to 8.5), there is a crossover effect characterized by a nonmonotonic ionic strength dependence of protein-protein interactions, which can be rationalized by the competing effects of long-ranged repulsive double layer forces at low ionic strength and a shorter ranged electrostatic attraction, which dominates above a critical ionic strength. The change of interactions from repulsive to attractive indicates a concomitant change in the angular dependence of protein-protein interaction from isotropic to anisotropic. In the second part of the paper, we show how the Baxter adhesive potential can be used to predict values of kD from fitting to B22 measurements, thus providing a molecular basis for the linear correlation between the two protein-protein interaction parameters.

A multiscale model for charge inversion in electric double layers

NASA Astrophysics Data System (ADS)

Mashayak, S. Y.; Aluru, N. R.

2018-06-01

Charge inversion is a widely observed phenomenon. It is a result of the rich statistical mechanics of the molecular interactions between ions, solvent, and charged surfaces near electric double layers (EDLs). Electrostatic correlations between ions and hydration interactions between ions and water molecules play a dominant role in determining the distribution of ions in EDLs. Due to highly polar nature of water, near a surface, an inhomogeneous and anisotropic arrangement of water molecules gives rise to pronounced variations in the electrostatic and hydration energies of ions. Classical continuum theories fail to accurately describe electrostatic correlations and molecular effects of water in EDLs. In this work, we present an empirical potential based quasi-continuum theory (EQT) to accurately predict the molecular-level properties of aqueous electrolytes. In EQT, we employ rigorous statistical mechanics tools to incorporate interatomic interactions, long-range electrostatics, correlations, and orientation polarization effects at a continuum-level. Explicit consideration of atomic interactions of water molecules is both theoretically and numerically challenging. We develop a systematic coarse-graining approach to coarse-grain interactions of water molecules and electrolyte ions from a high-resolution atomistic scale to the continuum scale. To demonstrate the ability of EQT to incorporate the water orientation polarization, ion hydration, and electrostatic correlations effects, we simulate confined KCl aqueous electrolyte and show that EQT can accurately predict the distribution of ions in a thin EDL and also predict the complex phenomenon of charge inversion.

Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae.

PubMed

Gao, Yongfei; Feng, Jianfeng; Kang, Lili; Xu, Xin; Zhu, Lin

2018-01-01

The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures. Copyright © 2017 Elsevier B.V. All rights reserved.

Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Cai, X. M.; Ancell, B. C.; Fan, Y. R.

2017-11-01

The ensemble Kalman filter (EnKF) is recognized as a powerful data assimilation technique that generates an ensemble of model variables through stochastic perturbations of forcing data and observations. However, relatively little guidance exists with regard to the proper specification of the magnitude of the perturbation and the ensemble size, posing a significant challenge in optimally implementing the EnKF. This paper presents a robust data assimilation system (RDAS), in which a multi-factorial design of the EnKF experiments is first proposed for hydrologic ensemble predictions. A multi-way analysis of variance is then used to examine potential interactions among factors affecting the EnKF experiments, achieving optimality of the RDAS with maximized performance of hydrologic predictions. The RDAS is applied to the Xiangxi River watershed which is the most representative watershed in China's Three Gorges Reservoir region to demonstrate its validity and applicability. Results reveal that the pairwise interaction between perturbed precipitation and streamflow observations has the most significant impact on the performance of the EnKF system, and their interactions vary dynamically across different settings of the ensemble size and the evapotranspiration perturbation. In addition, the interactions among experimental factors vary greatly in magnitude and direction depending on different statistical metrics for model evaluation including the Nash-Sutcliffe efficiency and the Box-Cox transformed root-mean-square error. It is thus necessary to test various evaluation metrics in order to enhance the robustness of hydrologic prediction systems.

Climate change can alter predator-prey dynamics and population viability of prey.

PubMed

Bastille-Rousseau, Guillaume; Schaefer, James A; Peers, Michael J L; Ellington, E Hance; Mumma, Matthew A; Rayl, Nathaniel D; Mahoney, Shane P; Murray, Dennis L

2018-01-01

For many organisms, climate change can directly drive population declines, but it is less clear how such variation may influence populations indirectly through modified biotic interactions. For instance, how will climate change alter complex, multi-species relationships that are modulated by climatic variation and that underlie ecosystem-level processes? Caribou (Rangifer tarandus), a keystone species in Newfoundland, Canada, provides a useful model for unravelling potential and complex long-term implications of climate change on biotic interactions and population change. We measured cause-specific caribou calf predation (1990-2013) in Newfoundland relative to seasonal weather patterns. We show that black bear (Ursus americanus) predation is facilitated by time-lagged higher summer growing degree days, whereas coyote (Canis latrans) predation increases with current precipitation and winter temperature. Based on future climate forecasts for the region, we illustrate that, through time, coyote predation on caribou calves could become increasingly important, whereas the influence of black bear would remain unchanged. From these predictions, demographic projections for caribou suggest long-term population limitation specifically through indirect effects of climate change on calf predation rates by coyotes. While our work assumes limited impact of climate change on other processes, it illustrates the range of impact that climate change can have on predator-prey interactions. We conclude that future efforts to predict potential effects of climate change on populations and ecosystems should include assessment of both direct and indirect effects, including climate-predator interactions.

Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance

EPA Science Inventory

Evaluating the potential human health and ecological risks associated with exposures to complex chemical mixtures in the environment is one of the main challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrat...

Chemical-gene interaction networks and causal reasoning for biological effects prediction and prioritization of contaminants for environmental monitoring and surveillance (poster)

EPA Science Inventory

Product Description:Evaluation of the potential effects of complex mixtures of chemicals in the environment is challenged by the lack of extensive toxicity data for many chemicals. However, there are growing sources of online information that curate and compile literature reports...

The Interactive Effects of Temperament and Maternal Parenting on Toddlers' Externalizing Behaviours

ERIC Educational Resources Information Center

van Aken, C.; Junger, M.; Verhoeven, M.; van Aken, M. A. G.; Dekovic, M.

2007-01-01

The present study aimed to determine the potential moderating effects of temperamental traits on the relation between parenting and toddlers' externalizing behaviours. For that purpose, this study examined the interplay between temperament and maternal parenting behaviours in predicting the level as well as the development of toddlers'…

Situational Evidence: Strategies for Causal Reasoning From Observational Field Notes

ERIC Educational Resources Information Center

Katz, Jack

2015-01-01

There is unexamined potential for developing and testing rival causal explanations in the type of data that participant observation is best suited to create: descriptions of in situ social interaction crafted from the participants' perspectives. By intensively examining a single ethnography, we can see how multiple predictions can be derived from…

Accounting for genotype–by-environment interactions and non-additive genetic variation in genomic selection for water-soluble carbohydrate concentration in wheat

USDA-ARS?s Scientific Manuscript database

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat un...

Numerical Investigation on the Effects of Self-Excited Tip Flow Unsteadiness and Blade Row Interactions on the Performance Predictions of Low Speed and Transonic Compressor Rotors

NASA Astrophysics Data System (ADS)

Lee, Daniel H.

The impact blade row interactions can have on the performance of compressor rotors has been well documented. It is also well known that rotor tip clearance flows can have a large effect on compressor performance and stall margin and recent research has shown that tip leakage flows can exhibit self-excited unsteadiness at near stall conditions. However, the impact of tip leakage flow on the performance and operating range of a compressor rotor, relative to other important flow features such as upstream stator wakes or downstream potential effects, has not been explored. To this end, a numerical investigation has been conducted to determine the effects of self-excited tip flow unsteadiness, upstream stator wakes, and downstream blade row interactions on the performance prediction of low speed and transonic compressor rotors. Calculations included a single blade-row rotor configuration as well as two multi-blade row configurations: one where the rotor was modeled with an upstream stator and a second where the rotor was modeled with a downstream stator. Steady-state and time accurate calculations were performed using a RANS solver and the results were compared with detailed experimental data obtained in the GE Low Speed Research Compressor and the Notre Dame Transonic Rig at several operating conditions including near stall. Differences in the performance predictions between the three configurations were then used to determine the effect of the upstream stator wakes and the downstream blade row interactions. Results obtained show that for both the low speed and transonic research compressors used in this investigation time-accurate RANS analysis is necessary to accurately predict the stalling character of the rotor. Additionally, for the first time it is demonstrated that capturing the unsteady tip flow can have a larger impact on rotor performance predictions than adjacent blade row interactions.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1*

PubMed Central

Hernandez, Anna; Buch, Anna; Sodeik, Beate; Cristea, Ileana Mihaela

2016-01-01

Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of targeted experiments for the discovery of novel protein-protein interactions. PMID:27384951

Maternal attitudinal inflexibility: longitudinal relations with mother-infant disrupted interaction and childhood hostile-aggressive behavior problems.

PubMed

Najmi, Sadia; Bureau, Jean-Francois; Chen, Diyu; Lyons-Ruth, Karlen

2009-12-01

: The Personal Attitude Scale (PAS; Hooley, 2000) is a method that is under development for identifying individuals high in Expressed Emotion based on personality traits of inflexibility, intolerance, and norm-forming. In the current study, the goal was to measure the association between this maternal attitudinal inflexibility, early hostile or disrupted mother-infant interactions, and hostile-aggressive behavior problems in the child. In a prospective longitudinal study of 76 low-income mothers and their infants, it was predicted that maternal PAS scores, assessed at child age 20, would be related to difficulties in early observed mother-infant interaction and to hostile-aggressive behavioral difficulties in the child. Results indicated that maternal difficulties in interacting with the infant in the laboratory were associated with maternal PAS scores assessed 20 years later. Hostile-aggressive behavior problems in the child at age five were also predictive of PAS scores of mothers. However, contrary to prediction, these behavior problems did not mediate the association between mother-infant interaction difficulties and maternal PAS scores, indicating that the child's hostile-aggressive behavior problems did not produce the link between quality of early interaction and later maternal attitudinal inflexibility. The current results validate the PAS against observable mother-child interactions and child hostile-aggressive behavior problems and indicate the importance of future work investigating the maternal attitudes that are associated with, and may potentially precede, parent-infant interactive difficulties. These findings regarding the inflexible attitudes of mothers whose interactions with their infants are also disrupted have important clinical implications. First, once the stability of the PAS has been established, this measure may offer a valuable screening tool for the prenatal identification of parents at risk for difficult interactions with their children. Second, it suggests routes for more cognitive interventions around helping less flexible parents shift perspectives to better take account of their child's outlooks and needs.

Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions

PubMed Central

Yip, Kevin Y.; Gerstein, Mark

2009-01-01

Motivation: An important problem in systems biology is reconstructing complete networks of interactions between biological objects by extrapolating from a few known interactions as examples. While there are many computational techniques proposed for this network reconstruction task, their accuracy is consistently limited by the small number of high-confidence examples, and the uneven distribution of these examples across the potential interaction space, with some objects having many known interactions and others few. Results: To address this issue, we propose two computational methods based on the concept of training set expansion. They work particularly effectively in conjunction with kernel approaches, which are a popular class of approaches for fusing together many disparate types of features. Both our methods are based on semi-supervised learning and involve augmenting the limited number of gold-standard training instances with carefully chosen and highly confident auxiliary examples. The first method, prediction propagation, propagates highly confident predictions of one local model to another as the auxiliary examples, thus learning from information-rich regions of the training network to help predict the information-poor regions. The second method, kernel initialization, takes the most similar and most dissimilar objects of each object in a global kernel as the auxiliary examples. Using several sets of experimentally verified protein–protein interactions from yeast, we show that training set expansion gives a measurable performance gain over a number of representative, state-of-the-art network reconstruction methods, and it can correctly identify some interactions that are ranked low by other methods due to the lack of training examples of the involved proteins. Contact: mark.gerstein@yale.edu Availability: The datasets and additional materials can be found at http://networks.gersteinlab.org/tse. PMID:19015141

Social Interaction in Infants' Learning of Second-Language Phonetics: An Exploration of Brain-Behavior Relations.

PubMed

Conboy, Barbara T; Brooks, Rechele; Meltzoff, Andrew N; Kuhl, Patricia K

2015-01-01

Infants learn phonetic information from a second language with live-person presentations, but not television or audio-only recordings. To understand the role of social interaction in learning a second language, we examined infants' joint attention with live, Spanish-speaking tutors and used a neural measure of phonetic learning. Infants' eye-gaze behaviors during Spanish sessions at 9.5-10.5 months of age predicted second-language phonetic learning, assessed by an event-related potential measure of Spanish phoneme discrimination at 11 months. These data suggest a powerful role for social interaction at the earliest stages of learning a new language.

High-temperature atomic superfluidity in lattice Bose-Fermi mixtures.

PubMed

Illuminati, Fabrizio; Albus, Alexander

2004-08-27

We consider atomic Bose-Fermi mixtures in optical lattices and study the superfluidity of fermionic atoms due to s-wave pairing induced by boson-fermion interactions. We prove that the induced fermion-fermion coupling is always attractive if the boson-boson on-site interaction is repulsive, and predict the existence of an enhanced BEC-BCS crossover as the strength of the lattice potential is varied. We show that for direct on-site fermion-fermion repulsion, the induced attraction can give rise to superfluidity via s-wave pairing at striking variance with the case of pure systems of fermionic atoms with direct repulsive interactions.

Asymmetric scoring functions for proteins

NASA Astrophysics Data System (ADS)

Lezon, Timothy; Holter, Neal; Maritan, Amos; Banavar, Jayanth

2003-03-01

The protein folding problem entails the prediction of the native state structure of a protein given the sequence of amino acids. In a coarse-grained description of a protein, an important ingredient for attempting this task is the determination of the effective energies of interaction between amino acids. We will discuss a simple approach for determining such interaction potentials from a training set of protein sequences and their experimentally determined native state structures. The key new ingredient in our study is the incorporation of the lack of symmetry in the effective interactions between amino acids. Our results, obtained using a set of 513 proteins, and their implications will be discussed.

N-Ω Interaction from High-Energy Heavy Ion Collisions

NASA Astrophysics Data System (ADS)

Morita, Kenji; Ohnishi, Akira; Hatsuda, Tetsuo

We discuss possible observation of the N-Ω interaction from intensity correlation function in high energy heavy ion collisions. Recently a lattice QCD simulation by the HAL QCD collaboration predicts the existence of a N-Ω bound state in the 5S2 channel. We adopt the N-Ω interaction potential obtained by the lattice simulation and use it to calculate the N-Ω correlation function. We also study the variation of the correlation function with respect to the change of the binding energy and scattering parameters. Our result indicates that heavy ion collisions at RHIC and LHC may provide information on the possible existence of the N-Ω dibaryon.

Long-range empirical potential model: extension to hexagonal close-packed metals.

PubMed

Dai, Y; Li, J H; Liu, B X

2009-09-23

An n-body potential is developed and satisfactorily applied to hcp metals, Co, Hf, Mg, Re, Ti, and Zr, in the form of long-range empirical potential. The potential can well reproduce the lattice constants, c/a ratios, cohesive energies, and the bulk modulus for their stable structures (hcp) and metastable structures (bcc or fcc). Meanwhile, the potential can correctly predict the order of structural stability and distinguish the energy differences between their stable hcp structure and other structures. The energies and forces derived by the potential can smoothly go to zero at cutoff radius, thus completely avoiding the unphysical behaviors in the simulations. The developed potential is applied to study the vacancy, surface fault, stacking fault and self-interstitial atom in the hcp metals. The calculated formation energies of vacancy and divacancy and activation energies of self-diffusion by vacancies are in good agreement with the values in experiments and in other works. The calculated surface energies and stacking fault energies are also consistent with the experimental data and those obtained in other theoretical works. The calculated formation energies generally agree with the results in other works, although the stable configurations of self-interstitial atoms predicted in this work somewhat contrast with those predicted by other methods. The proposed potential is shown to be relevant for describing the interaction of bcc, fcc and hcp metal systems, bringing great convenience for researchers in constructing potentials for metal systems constituted by any combination of bcc, fcc and hcp metals.

Electrophysiological correlates of forming memories for faces, names, and face-name associations.

PubMed

Guo, Chunyan; Voss, Joel L; Paller, Ken A

2005-02-01

The ability to put a name to a face is a vital aspect of human interaction, but many people find this extremely difficult, especially after being introduced to someone for the first time. Creating enduring associations between arbitrary stimuli in this manner is also a prime example of what patients with amnesia find most difficult. To help develop a better understanding of this type of memory, we sought to obtain measures of the neural events responsible for successfully forming a new face-name association. We used event-related potentials (ERPs) extracted from high-density scalp EEG recordings in order to compare (1) memory for faces, (2) memory for names, and (3) memory for face-name associations. Each visual face appeared simultaneously with a unique spoken name. Signals observed 200-800 ms after the onset of face-name pairs predicted subsequent memory for faces, names, or face-name associations. Difference potentials observed as a function of subsequent memory performance were not identical for these three memory tests, nor were potentials predicting associative memory equivalent to the sum of potentials predicting item memory, suggesting that different neural events at the time of encoding are relevant for these distinct aspects of remembering people.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Monoamine transporter and receptor interaction profiles in vitro predict reported human doses of novel psychoactive stimulants and psychedelics.

PubMed

Luethi, Dino; Liechti, Matthias E

2018-05-29

Pharmacological profiles of new psychoactive substances (NPSs) can be established rapidly in vitro and provide information on potential psychoactive effects in humans. The present study investigated whether specific in vitro monoamine transporter and receptor interactions can predict effective psychoactive doses in humans. We correlated previously assessed in vitro data of stimulants and psychedelics with human doses that are reported on the Internet and in books. For stimulants, dopamine and norepinephrine transporter inhibition potency was positively correlated with human doses, whereas serotonin transporter inhibition potency was inversely correlated with human doses. Serotonin 5-hydroxytryptamine-2A (5-HT2A) and 5-HT2C receptor affinity was significantly correlated with psychedelic doses, but 5-HT1A receptor affinity and 5-HT2A and 5-HT2B receptor activation potency were not. The rapid assessment of in vitro pharmacological profiles of NPSs can help to predict psychoactive doses and effects in humans and facilitate the appropriate scheduling of NPSs.

Tuning the effective fine structure constant in graphene: opposing effects of dielectric screening on short- and long-range potential scattering.

PubMed

Jang, C; Adam, S; Chen, J-H; Williams, E D; Das Sarma, S; Fuhrer, M S

2008-10-03

We reduce the dimensionless interaction strength alpha in graphene by adding a water overlayer in ultrahigh vacuum, thereby increasing dielectric screening. The mobility limited by long-range impurity scattering is increased over 30%, due to the background dielectric constant enhancement leading to a reduced interaction of electrons with charged impurities. However, the carrier-density-independent conductivity due to short-range impurities is decreased by almost 40%, due to reduced screening of the impurity potential by conduction electrons. The minimum conductivity is nearly unchanged, due to canceling contributions from the electron-hole puddle density and long-range impurity mobility. Experimental data are compared with theoretical predictions with excellent agreement.

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

Callear, Samantha K.; Imberti, Silvia; Johnston, Andrew

The aqueous solution of dopamine hydrochloride has been investigated using neutron and X-ray total scattering data together with Monte-Carlo based modelling using Empirical Potential Structure Refinement. The conformation of the protonated dopamine molecule is presented and the results compared to the conformations found in crystal structures, dopamine-complexed protein crystal structures and predicted from theoretical calculations and pharmacophoric models. It is found that protonated dopamine adopts a range of conformations in solution, highlighting the low rotational energy barrier between different conformations, with the preferred conformation being trans-perpendicular. The interactions between each of the species present (protonated dopamine molecules, water molecules, andmore » chloride anions) have been determined and are discussed with reference to interactions observed in similar systems both in the liquid and crystalline state, and predicted from theoretical calculations. The expected strong hydrogen bonds between the strong hydrogen bond donors and acceptors are observed, together with evidence of weaker CH hydrogen bonds and π interactions also playing a significant role in determining the arrangement of adjacent molecules.« less

Engineering nanometre-scale coherence in soft matter

NASA Astrophysics Data System (ADS)

Liu, Chaoren; Xiang, Limin; Zhang, Yuqi; Zhang, Peng; Beratan, David N.; Li, Yueqi; Tao, Nongjian

2016-10-01

Electronic delocalization in redox-active polymers may be disrupted by the heterogeneity of the environment that surrounds each monomer. When the differences in monomer redox-potential induced by the environment are small (as compared with the monomer-monomer electronic interactions), delocalization persists. Here we show that guanine (G) runs in double-stranded DNA support delocalization over 4-5 guanine bases. The weak interaction between delocalized G blocks on opposite DNA strands is known to support partially coherent long-range charge transport. The molecular-resolution model developed here finds that the coherence among these G blocks follows an even-odd orbital-symmetry rule and predicts that weakening the interaction between G blocks exaggerates the resistance oscillations. These findings indicate how sequence can be exploited to change the balance between coherent and incoherent transport. The predictions are tested and confirmed using break-junction experiments. Thus, tailored orbital symmetry and structural fluctuations may be used to produce coherent transport with a length scale of multiple nanometres in soft-matter assemblies, a length scale comparable to that of small proteins.

Potential Energy Curves and Transport Properties for the Interaction of He with Other Ground-state Atoms

NASA Technical Reports Server (NTRS)

Partridge, Harry; Stallcop, James R.; Levin, Eugene; Arnold, Jim (Technical Monitor)

2001-01-01

The interactions of a He atom with a heavier atom are examined for 26 different elements, which are consecutive members selected from three rows (Li - Ne, Na - Ar, and K,Ca, Ga - Kr) and column 12 (Zn,Cd) of the periodic table. Interaction energies are determined wing high-quality ab initio calculations for the states of the molecule that would be formed from each pair of atoms in their ground states. Potential energies are tabulated for a broad range of Interatomic separation distances. The results show, for example, that the energy of an alkali interaction at small separations is nearly the same as that of a rare-gas interaction with the same electron configuration for the dosed shells. Furthermore, the repulsive-range parameter for this region is very short compared to its length for the repulsion dominated by the alkali-valence electron at large separations (beyond about 3-4 a(sub 0)). The potential energies in the region of the van der Waals minimum agree well with the most accurate results available. The ab initio energies are applied to calculate scattering cross sections and obtain the collision integrals that are needed to determine transport properties to second order. The theoretical values of Li-He total scattering cross sections and the rare-gas atom-He transport properties agree well (to within about 1%) with the corresponding measured data. Effective potential energies are constructed from the ab initio energies; the results have been shown to reproduce known transport data and can be readily applied to predict unknown transport properties for like-atom interactions.

Soft Wall Ion Channel in Continuum Representation with Application to Modeling Ion Currents in α-Hemolysin

PubMed Central

Simakov, Nikolay A.

2010-01-01

A soft repulsion (SR) model of short range interactions between mobile ions and protein atoms is introduced in the framework of continuum representation of the protein and solvent. The Poisson-Nernst-Plank (PNP) theory of ion transport through biological channels is modified to incorporate this soft wall protein model. Two sets of SR parameters are introduced: the first is parameterized for all essential amino acid residues using all atom molecular dynamic simulations; the second is a truncated Lennard – Jones potential. We have further designed an energy based algorithm for the determination of the ion accessible volume, which is appropriate for a particular system discretization. The effects of these models of short-range interaction were tested by computing current-voltage characteristics of the α-hemolysin channel. The introduced SR potentials significantly improve prediction of channel selectivity. In addition, we studied the effect of choice of some space-dependent diffusion coefficient distributions on the predicted current-voltage properties. We conclude that the diffusion coefficient distributions largely affect total currents and have little effect on rectifications, selectivity or reversal potential. The PNP-SR algorithm is implemented in a new efficient parallel Poisson, Poisson-Boltzman and PNP equation solver, also incorporated in a graphical molecular modeling package HARLEM. PMID:21028776

Progression towards optimization of viscosity of highly concentrated carbonaceous solid-water slurries by incorporating and modifying surface chemistry parameters with and without additives

NASA Astrophysics Data System (ADS)

Mukherjee, Amrita

Carbonaceous solid-water slurries (CSWS) are concentrated suspensions of coal, petcoke bitumen, pitch etc. in water which are used as feedstock for gasifiers. The high solid loading (60-75 wt.%) in the slurry increases CSWS viscosity. For easier handling and pumping of these highly loaded mixtures, low viscosities are desirable. Depending on the nature of the carbonaceous solid, solids loading in the slurry and the particle size distribution, viscosity of a slurry can vary significantly. Ability to accurately predict the viscosity of a slurry will provide a better control over the design of slurry transport system and for viscosity optimization. The existing viscosity prediction models were originally developed for hard-sphere suspensions and therefore do not take into account surface chemistry. As a result, the viscosity predictions using these models for CSWS are not very accurate. Additives are commonly added to decrease viscosity of the CSWS by altering the surface chemistry. Since additives are specific to CSWS, selection of appropriate additives is crucial. The goal of this research was to aid in optimization of CSWS viscosity through improved prediction and selection of appropriate additive. To incorporate effect of surface chemistry in the models predicting suspension viscosity, the effect of the different interfacial interactions caused by different surface chemistries has to be accounted for. Slurries of five carbonaceous solids with varying O/C ratio (to represent different surface chemistry parameters) were used for the study. To determine the interparticle interactions of the carbonaceous solids in water, interfacial energies were calculated on the basis of surface chemistries, characterized by contact angles and zeta potential measurements. The carbonaceous solid particles in the slurries were assumed to be spherical. Polar interaction energy (hydrophobic/hydrophilic interaction energy), which was observed to be 5-6 orders of magnitude higher than the electrostatic interaction energy, and the van der Waals interaction energy, was clearly the dominant interaction energy for such a system. Hydrophobic interactions lead to the formation of aggregation networks of solids in the suspensions, entrapping a part of the bulk water, whereas hydrophilic interactions result in the formation of hydration layers around carbonaceous solids. Both of these phenomena cause a loss of bulk water from the slurry and increase the effective solid volume fraction, resulting in an increase in slurry viscosity. The water in the bulk of the slurry, responsible for the fluidity of the slurry is called free water. The amount of free water was determined using thermogravimetric analysis and was observed to increase with an increase in the O/C ratio of a carbonaceous solid (up to ˜20%). The free water to total water ratio was observed to be constant for the slurry of a particular carbonaceous solid for various loadings of solids (44 wt.% to 67 wt.%). The increase in the effective solid volume fractions of slurries was determined using viscosity measurements. A relationship between the effective solid volume fraction and the O/C ratio of the carbonaceous solid was developed. This correlation was then incorporated into the existing equation for viscosity prediction (developed based on particle size distribution and solid volume fraction), to account for the surface chemistry of the carbonaceous solid and hence improve the predictive capabilities. This modified equation was validated using three concentrated carbonaceous slurries with different particle size distributions and was observed to significantly improve accuracy of prediction (deviation of predicted results decreased from up to 96% to 25%). The validation was performed with a lignite, bituminous coal and a petcoke-all with low ash yield. Additives modify the surface chemistry of the carbonaceous solids, thereby affecting the interfacial interactions. Through this research, the effects of additives on the interfacial interactions and hence on slurry viscosity were determined. Since the additives used are specific to the surface chemistry of the solids in the slurry, this knowledge aids in the selection of the appropriate additive. The study was conducted using three carbonaceous solids with different O/C ratios and an anionic and a non-ionic additive. The adsorption of the additives on the carbonaceous solids, the change in the zeta potential and hydrophobicity/hydrophilicity of the solids and the change in the free water content of the slurries were determined. The adsorption of the additives increased with an increase in the mineral matter content of the carbonaceous solids. There was also an increase in the zeta potential of the carbonaceous solids in water upon the addition of the anionic additive (up to ˜30%). However, the calculated resultant electrostatic repulsion energy upon the addition of the anionic additive was 5-6 orders of magnitude lower than the polar interaction energy of the carbonaceous solids in water. Contact angle measurements indicated that both additives changed the hydrophobicity/hydrophilicity of the solid surface (by up to 70°). This resulted in the release of bound water into the bulk slurries (up to 6%), resulting in greater fluidity. The increase in free water content of the slurries with additives was confirmed by thermogravimetric analysis (TGA). A correlation predicting the slurry viscosity on the basis of the weight fraction of free water in the slurries with additives was also developed.

Software reliability studies

NASA Technical Reports Server (NTRS)

Hoppa, Mary Ann; Wilson, Larry W.

1994-01-01

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.

Fan broadband interaction noise modeling using a low-order method

NASA Astrophysics Data System (ADS)

Grace, S. M.

2015-06-01

A low-order method for simulating broadband interaction noise downstream of the fan stage in a turbofan engine is explored in this paper. The particular noise source of interest is due to the interaction of the fan rotor wake with the fan exit guide vanes (FEGVs). The vanes are modeled as flat plates and the method utilizes strip theory relying on unsteady aerodynamic cascade theory at each strip. This paper shows predictions for 6 of the 9 cases from NASA's Source Diagnostic Test (SDT) and all 4 cases from the 2014 Fan Broadband Workshop Fundamental Case 2 (FC2). The turbulence in the rotor wake is taken from hot-wire data for the low speed SDT cases and the FC2 cases. Additionally, four different computational simulations of the rotor wake flow for all of the SDT rotor speeds have been used to determine the rotor wake turbulence parameters. Comparisons between predictions based on the different inputs highlight the possibility of a potential effect present in the hot-wire data for the SDT as well as the importance of accurately describing the turbulence length scale when using this model. The method produces accurate predictions of the spectral shape for all of the cases. It also predicts reasonably well all of the trends that can be considered based on the included cases such as vane geometry, vane count, turbulence level, and rotor speed.

StaRProtein, A Web Server for Prediction of the Stability of Repeat Proteins

PubMed Central

Xu, Yongtao; Zhou, Xu; Huang, Meilan

2015-01-01

Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins. PMID:25807112

The parity-violating asymmetry in the 3He(n,p)3H reaction

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

M. Viviani, R. Schiavilla, L. Girlanda, A. Kievsky, L.E. Marcucci

2010-10-01

The longitudinal asymmetry induced by parity-violating (PV) components in the nucleon-nucleon potential is studied in the charge-exchange reaction 3He(n,p)3H at vanishing incident neutron energies. An expression for the PV observable is derived in terms of T-matrix elements for transitions from the {2S+1}L_J=1S_0 and 3S_1 states in the incoming n-3He channel to states with J=0 and 1 in the outgoing p-3H channel. The T-matrix elements involving PV transitions are obtained in first-order perturbation theory in the hadronic weak-interaction potential, while those connecting states of the same parity are derived from solutions of the strong-interaction Hamiltonian with the hyperspherical-harmonics method. The coupled-channelmore » nature of the scattering problem is fully accounted for. Results are obtained corresponding to realistic or chiral two- and three-nucleon strong-interaction potentials in combination with either the DDH or pionless EFT model for the weak-interaction potential. The asymmetries, predicted with PV pion and vector-meson coupling constants corresponding (essentially) to the DDH "best values" set, range from -9.44 to -2.48 in units of 10^{-8}, depending on the input strong-interaction Hamiltonian. This large model dependence is a consequence of cancellations between long-range (pion) and short-range (vector-meson) contributions, and is of course sensitive to the assumed values for the PV coupling constants.« less

Respiratory Sinus Arrhythmia Moderates the Relation between Parent-Adolescent Relationship Quality and Adolescents' Social Adjustment.

PubMed

Van der Graaff, Jolien; Meeus, Wim; de Wied, Minet; van Boxtel, Anton; van Lier, Pol; Branje, Susan

2016-02-01

This 2-wave longitudinal study aimed (1) to investigate whether high resting RSA predicted adolescents' lower externalizing behavior and higher empathic concern, and (2) to address the potential moderating role of resting RSA in the association between parent-adolescent relationship quality and adolescents' externalizing behavior and empathic concern. In a sample of 379 adolescents (212 boys, 167 girls), resting RSA was assessed during a laboratory session, and adolescents reported on parental support, negative interaction with parents, empathic concern and externalizing behavior during a home visit. We found no support for high resting RSA predicting low externalizing behavior or high empathic concern. However, in line with our hypotheses, we did find several instances of RSA functioning as a moderator, although the interaction patterns varied. First, negative interaction with parents was a negative predictor of externalizing behavior for girls low in resting RSA, whereas the association was non-significant for girls with high RSA. Second, higher negative interaction with parents predicted lower empathic concern for boys high in resting RSA, whereas the association was reversed for boys with low resting RSA. Third, parental support was a positive predictor of empathic concern for girls high in resting RSA, whereas the association was non-significant for girls low in resting RSA. The findings suggest that adolescents with different levels of resting RSA respond differentially to relationship quality with parents.

Monoamine Oxidase-A Genetic Variants and Childhood Abuse Predict Impulsiveness in Borderline Personality Disorder.

PubMed

Kolla, Nathan J; Meyer, Jeffrey; Sanches, Marcos; Charbonneau, James

2017-11-30

Impulsivity is a core feature of borderline personality disorder (BPD) and antisocial personality disorder (ASPD) that likely arises from combined genetic and environmental influences. The interaction of the low activity variant of the monoamine oxidase-A (MAOA-L) gene and early childhood adversity has been shown to predict aggression in clinical and non-clinical populations. Although impulsivity is a risk factor for aggression in BPD and ASPD, little research has investigated potential gene-environment (G×E) influences impacting its expression in these conditions. Moreover, G×E interactions may differ by diagnosis. Full factorial analysis of variance was employed to investigate the influence of monoamine oxidase-A (MAO-A) genotype, childhood abuse, and diagnosis on Barratt Impulsiveness Scale-11 (BIS-11) scores in 61 individuals: 20 subjects with BPD, 18 subjects with ASPD, and 23 healthy controls. A group×genotype×abuse interaction was present (F(2,49)=4.4, p =0.018), such that the interaction of MAOA-L and childhood abuse predicted greater BIS-11 motor impulsiveness in BPD. Additionally, BPD subjects reported higher BIS-11 attentional impulsiveness versus ASPD participants (t(1,36)=2.3, p =0.025). These preliminary results suggest that MAOA-L may modulate the impact of childhood abuse on impulsivity in BPD. Results additionally indicate that impulsiveness may be expressed differently in BPD and ASPD.

Simulation of the effect of hydrogen bonds on water activity of glucose and dextran using the Veytsman model.

PubMed

De Vito, Francesca; Veytsman, Boris; Painter, Paul; Kokini, Jozef L

2015-03-06

Carbohydrates exhibit either van der Waals and ionic interactions or strong hydrogen bonding interactions. The prominence and large number of hydrogen bonds results in major contributions to phase behavior. A thermodynamic framework that accounts for hydrogen bonding interactions is therefore necessary. We have developed an extension of the thermodynamic model based on the Veytsman association theory to predict the contribution of hydrogen bonds to the behavior of glucose-water and dextran-water systems and we have calculated the free energy of mixing and its derivative leading to chemical potential and water activity. We compared our calculations with experimental data of water activity for glucose and dextran and found excellent agreement far superior to the Flory-Huggins theory. The validation of our calculations using experimental data demonstrated the validity of the Veytsman model in properly accounting for the hydrogen bonding interactions and successfully predicting water activity of glucose and dextran. Our calculations of the concentration of hydrogen bonds using the Veytsman model were instrumental in our ability to explain the difference between glucose and dextran and the role that hydrogen bonds play in contributing to these differences. The miscibility predictions showed that the Veytsman model is also able to correctly describe the phase behavior of glucose and dextran. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling and simulation studies of human β3 adrenergic receptor and its interactions with agonists.

PubMed

Sahi, Shakti; Tewatia, Parul; Malik, Balwant K

2012-12-01

β3 adrenergic receptor (β3AR) is known to mediate various pharmacological and physiological effects such as thermogenesis in brown adipocytes, lipolysis in white adipocytes, glucose homeostasis and intestinal smooth muscle relaxation. Several efforts have been made in this field to understand their function and regulation in different human tissues and they have emerged as potential attractive targets in drug discovery for the treatment of diabetes, depression, obesity etc. Although the crystal structures of Bovine Rhodopsin and β2 adrenergic receptor have been resolved, to date there is no three dimensional structural information on β3AR. Our aim in this study was to model 3D structure of β3AR by various molecular modeling and simulation techniques. In this paper, we describe a refined predicted model of β3AR using different algorithms for structure prediction. The structural refinement and minimization of the generated 3D model of β3AR were done by Schrodinger suite 9.1. Docking studies of β3AR model with the known agonists enabled us to identify specific residues, viz, Asp 117, Ser 208, Ser 209, Ser 212, Arg 315, Asn 332, within the β3AR binding pocket, which might play an important role in ligand binding. Receptor ligand interaction studies clearly indicated that these five residues showed strong hydrogen bonding interactions with the ligands. The results have been correlated with the experimental data available. The predicted ligand binding interactions and the simulation studies validate the methods used to predict the 3D-structure.

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India.

PubMed

Kumar, Rajesh; Dash, Chinmaya; Rani, Khushbu

2017-09-01

Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover ( p  < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

Novel design strategy for checkpoint kinase 2 inhibitors using pharmacophore modeling, combinatorial fusion, and virtual screening.

PubMed

Lin, Chun-Yuan; Wang, Yen-Ling

2014-01-01

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the Best(train)Best(test) and Fast(train)Fast(test) prediction results. The potential inhibitors were selected from NCI database by screening according to Best(train)Best(test) + Fast(train)Fast(test) prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.

Computationally Discovered Potentiating Role of Glycans on NMDA Receptors

NASA Astrophysics Data System (ADS)

Sinitskiy, Anton V.; Stanley, Nathaniel H.; Hackos, David H.; Hanson, Jesse E.; Sellers, Benjamin D.; Pande, Vijay S.

2017-04-01

N-methyl-D-aspartate receptors (NMDARs) are glycoproteins in the brain central to learning and memory. The effects of glycosylation on the structure and dynamics of NMDARs are largely unknown. In this work, we use extensive molecular dynamics simulations of GluN1 and GluN2B ligand binding domains (LBDs) of NMDARs to investigate these effects. Our simulations predict that intra-domain interactions involving the glycan attached to residue GluN1-N440 stabilize closed-clamshell conformations of the GluN1 LBD. The glycan on GluN2B-N688 shows a similar, though weaker, effect. Based on these results, and assuming the transferability of the results of LBD simulations to the full receptor, we predict that glycans at GluN1-N440 might play a potentiator role in NMDARs. To validate this prediction, we perform electrophysiological analysis of full-length NMDARs with a glycosylation-preventing GluN1-N440Q mutation, and demonstrate an increase in the glycine EC50 value. Overall, our results suggest an intramolecular potentiating role of glycans on NMDA receptors.

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS.

PubMed

Cresswell, Alexander J; Wheatley, Richard J; Wilkinson, Richard D; Graham, Richard S

2016-10-20

Impurities from the CCS chain can greatly influence the physical properties of CO 2 . This has important design, safety and cost implications for the compression, transport and storage of CO 2 . There is an urgent need to understand and predict the properties of impure CO 2 to assist with CCS implementation. However, CCS presents demanding modelling requirements. A suitable model must both accurately and robustly predict CO 2 phase behaviour over a wide range of temperatures and pressures, and maintain that predictive power for CO 2 mixtures with numerous, mutually interacting chemical species. A promising technique to address this task is molecular simulation. It offers a molecular approach, with foundations in firmly established physical principles, along with the potential to predict the wide range of physical properties required for CCS. The quality of predictions from molecular simulation depends on accurate force-fields to describe the interactions between CO 2 and other molecules. Unfortunately, there is currently no universally applicable method to obtain force-fields suitable for molecular simulation. In this paper we present two methods of obtaining force-fields: the first being semi-empirical and the second using ab initio quantum-chemical calculations. In the first approach we optimise the impurity force-field against measurements of the phase and pressure-volume behaviour of CO 2 binary mixtures with N 2 , O 2 , Ar and H 2 . A gradient-free optimiser allows us to use the simulation itself as the underlying model. This leads to accurate and robust predictions under conditions relevant to CCS. In the second approach we use quantum-chemical calculations to produce ab initio evaluations of the interactions between CO 2 and relevant impurities, taking N 2 as an exemplar. We use a modest number of these calculations to train a machine-learning algorithm, known as a Gaussian process, to describe these data. The resulting model is then able to accurately predict a much broader set of ab initio force-field calculations at comparatively low numerical cost. Although our method is not yet ready to be implemented in a molecular simulation, we outline the necessary steps here. Such simulations have the potential to deliver first-principles simulation of the thermodynamic properties of impure CO 2 , without fitting to experimental data.

Hydrophobic potential of mean force as a solvation function for protein structure prediction.

PubMed

Lin, Matthew S; Fawzi, Nicolas Lux; Head-Gordon, Teresa

2007-06-01

We have developed a solvation function that combines a Generalized Born model for polarization of protein charge by the high dielectric solvent, with a hydrophobic potential of mean force (HPMF) as a model for hydrophobic interaction, to aid in the discrimination of native structures from other misfolded states in protein structure prediction. We find that our energy function outperforms other reported scoring functions in terms of correct native ranking for 91% of proteins and low Z scores for a variety of decoy sets, including the challenging Rosetta decoys. This work shows that the stabilizing effect of hydrophobic exposure to aqueous solvent that defines the HPMF hydration physics is an apparent improvement over solvent-accessible surface area models that penalize hydrophobic exposure. Decoys generated by thermal sampling around the native-state basin reveal a potentially important role for side-chain entropy in the future development of even more accurate free energy surfaces.

Is love colorblind? Political orientation and interracial romantic desire.

PubMed

Eastwick, Paul W; Richeson, Jennifer A; Son, Deborah; Finkel, Eli J

2009-09-01

The present research examined the association of political orientation with ingroup favoritism in two live romantic contexts. In Study 1, White participants had sequential interactions with both a White and Black confederate and reported their romantic desire for each. In Study 2, both White and Black participants speed-dated multiple potential romantic partners and reported whether they would be interested in meeting each speed-dating partner again. In both studies, White participants' political conservatism positively predicted the strength of the ingroup-favoring bias: White conservatives were less likely than White liberals to desire Black (interracial) relative to White potential romantic partners. In contrast, Black participants' political conservatism negatively predicted the strength of the ingroup-favoring bias: Consistent with system-justification theory, Black conservatives were more likely than Black liberals to desire White (interracial) relative to Black potential romantic partners. Political orientation may be a key factor that influences the initiation of interracial romantic relationships.

Experimental Simulation of Solar Wind Interaction with MagneticDipole Fields above Insulating Surfaces

NASA Astrophysics Data System (ADS)

Yeo, L. H.; Han, J.; Wang, X.; Werner, G.; Deca, J.; Munsat, T.; Horanyi, M.

2017-12-01

Magnetic anomalies on the surfaces of airless bodies such as the Moon interact with the solar wind, resulting in both magnetic and electrostatic deflection/reflection of thecharged particles. Consequently, surface charging in these regions will be modified. Using the Colorado Solar Wind Experiment facility, this interaction is investigated with high-energy flowing plasmas (100-800 eV beam ions) that are incident upon a magnetic dipole (0.13 T) embedded under various insulating surfaces. The dipole moment is perpendicular to the surface. Using an emissive probe, 2D plasma potential profiles are obtained above the surface. In the dipole lobe regions, the surfaces are charged to significantly positive potentials due to the impingement of the unmagnetized ions while the electrons are magnetically shielded. At low ion beam energies, the results agree with the theoretical predictions, i.e., the surface potential follows the energy of the beam ions in eV. However, at high energies, the surface potentials in the electron-shielded regions are significantly lower than the beam energies. A series of investigations have been conducted and indicate that the surface properties (e.g., modified surface conductance, ion induced secondary electrons and electron-neutral collision at the surface) are likely to play a role in determining the surface potential.

Perpendicular blade vortex interaction and its implications for helicopter noise prediction: Wave-number frequency spectra in a trailing vortex for BWI noise prediction

NASA Technical Reports Server (NTRS)

Devenport, William J.; Glegg, Stewart A. L.

1993-01-01

Perpendicular blade vortex interactions are a common occurrence in helicopter rotor flows. Under certain conditions they produce a substantial proportion of the acoustic noise. However, the mechanism of noise generation is not well understood. Specifically, turbulence associated with the trailing vortices shed from the blade tips appears insufficient to account for the noise generated. The hypothesis that the first perpendicular interaction experienced by a trailing vortex alters its turbulence structure in such a way as to increase the acoustic noise generated by subsequent interactions is examined. To investigate this hypothesis a two-part investigation was carried out. In the first part, experiments were performed to examine the behavior of a streamwise vortex as it passed over and downstream of a spanwise blade in incompressible flow. Blade vortex separations between +/- one eighth chord were studied for at a chord Reynolds number of 200,000. Three-component velocity and turbulence measurements were made in the flow from 4 chord lengths upstream to 15 chordlengths downstream of the blade using miniature 4-sensor hot wire probes. These measurements show that the interaction of the vortex with the blade and its wake causes the vortex core to loose circulation and diffuse much more rapidly than it otherwise would. Core radius increases and peak tangential velocity decreases with distance downstream of the blade. True turbulence levels within the core are much larger downstream than upstream of the blade. The net result is a much larger and more intense region of turbulent flow than that presented by the original vortex and thus, by implication, a greater potential for generating acoustic noise. In the second part, the turbulence measurements described above were used to derive the necessary inputs to a Blade Wake Interaction (BWI) noise prediction scheme. This resulted in significantly improved agreement between measurements and calculations of the BWI noise spectrum especially for the spectral peak at low frequencies, which previously was poorly predicted.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

PubMed

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

Dislocation core structures of tungsten with dilute solute hydrogen

NASA Astrophysics Data System (ADS)

Wang, Yinan; Li, Qiulin; Li, Chengliang; Shu, Guogang; Xu, Ben; Liu, Wei

2017-12-01

In this paper, a combination of quantum mechanical and interatomic potential-based atomistic calculations are used to predict the core structures of screw and edge dislocations in tungsten in the presence of a particular concentration of hydrogen atoms. These configurations of the core structures are the results of two competing energies: the interaction between the partial dislocations and the corresponding generalized stacking fault energy in between the two partial dislocations, which are presented in this work. With this, we can precisely predict the configurations of the hydrogen-doped dislocation core structures.

Ideal glass transitions in thin films: An energy landscape perspective

NASA Astrophysics Data System (ADS)

Truskett, Thomas M.; Ganesan, Venkat

2003-07-01

We introduce a mean-field model for the potential energy landscape of a thin fluid film confined between parallel substrates. The model predicts how the number of accessible basins on the energy landscape and, consequently, the film's ideal glass transition temperature depend on bulk pressure, film thickness, and the strength of the fluid-fluid and fluid-substrate interactions. The predictions are in qualitative agreement with the experimental trends for the kinetic glass transition temperature of thin films, suggesting the utility of landscape-based approaches for studying the behavior of confined fluids.

A review on the removal of antibiotics by carbon nanotubes.

PubMed

Cong, Qiao; Yuan, Xing; Qu, Jiao

2013-01-01

Increasing concerns have been raised regarding the potential risks of antibiotics to human and ecological health due to their extensive use. Carbon nanotubes (CNTs) have drawn special research attention because of their unique properties and potential applications as a kind of adsorbents. This review summarizes the currently available research on the adsorption of antibiotics on CNTs, and will provide useful information for CNT application and risk assessment. Four different models, the Freundlich model (FM), Langmuir model (LM), Polanyi-Mane model (PMM), and Dubinin-Ashtakhov model (DAM), are often used to fit the adsorption isotherms. Because different mechanisms may act simultaneously, including electrostatic interactions, hydrophobic interactions, π-π bonds, and hydrogen bonds, the prediction of organic chemical adsorption on CNTs is not straightforward. Properties of CNTs, such as specific surface area, adsorption sites, and oxygen content, may influence the adsorption of antibiotics on CNTs. Adsorption heterogeneity and hysteresis are two features of antibiotic-CNT interactions. In addition, CNTs with adsorbed antibiotics may have potential risks for human health. So, further research examining how to reduce such risks is needed.

Optimal aluminum/zirconium: Protein interactions for predicting antiperspirant efficacy using zeta potential measurements.

PubMed

Yuan, Shaotang; Vaughn, John; Pappas, Iraklis; Fitzgerald, Michael; Masters, James G; Pan, Long

2015-01-01

The interactions between commercial antiperspirant (AP) salts [aluminum chlorohydrate (ACH), activated ACH, aluminum sesquichlorohydrate (ASCH), zirconium aluminum glycine (ZAG), activated ZAG), pure aluminum polyoxocations (Al13-mer, Al30-mer), and the zirconium(IV)-glycine complex Zr6 (O)4 (OH)4 (H2O)8 (Gly)8]12+(-) (CP-2 or ZG) with Bovine serum albumin (BSA) were studied using zeta potential and turbidity measurements. The maximal turbidity, which revealed the optimal interactions between protein and metal salts, for all protein-metal salt samples was observed at the isoelectric point (IEP), where the zeta potential of the solution was zero. Efficacy of AP salts was determined via three parameters: the amount of salt required to flocculate BSA to reach IEP, the turbidity of solution at the IEP, and the pH range over which the turbidity of the solution remains sufficiently high. By comparing active salt performance from this work to traditional prescreening methods, this methodology was able to provide a consistent efficacy assessment for metal actives in APs or in water treatment.

Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug–Drug Interaction Between Clopidogrel and Dasabuvir

PubMed Central

Fu, W; Badri, P; Bow, DAJ; Fischer, V

2017-01-01

Dasabuvir, a nonnucleoside NS5B polymerase inhibitor, is a sensitive substrate of cytochrome P450 (CYP) 2C8 with a potential for drug–drug interaction (DDI) with clopidogrel. A physiologically based pharmacokinetic (PBPK) model was developed for dasabuvir to evaluate the DDI potential with clopidogrel, the acyl‐β‐D glucuronide metabolite of which has been reported as a strong mechanism‐based inhibitor of CYP2C8 based on an interaction with repaglinide. In addition, the PBPK model for clopidogrel and its metabolite were updated with additional in vitro data. Sensitivity analyses using these PBPK models suggested that CYP2C8 inhibition by clopidogrel acyl‐β‐D glucuronide may not be as potent as previously suggested. The dasabuvir and updated clopidogrel PBPK models predict a moderate increase of 1.5–1.9‐fold for Cmax and 1.9–2.8‐fold for AUC of dasabuvir when coadministered with clopidogrel. While the PBPK results suggest there is a potential for DDI between dasabuvir and clopidogrel, the magnitude is not expected to be clinically relevant. PMID:28411400

Evaluation Of Ion Exchange For Fabrication Of Rare-Earth Doped Waveguides

NASA Astrophysics Data System (ADS)

Howell, Brian P.; Beerling, Timothy

1987-01-01

Rare earth ions are frequently incorporated into lasers by doping common glasses with the ions in the glass melt. This paper describes the potential of using diffusion of the rare earth ion from molten salt baths to incorporate it in the glass. The paper discusses the molten salts, the rare earths as a group, the diffusion phenomena, the glasses, and finally the interaction of all these to produce the process. General predictions of the waveguide profile and potential problems are presented.

Properties of solid and gaseous hydrogen, based upon anisotropic pair interactions

NASA Technical Reports Server (NTRS)

Etters, R. D.; Danilowicz, R.; England, W.

1975-01-01

Properties of H2 are studied on the basis of an analytic anisotropic potential deduced from atomic orbital and perturbation calculations. The low-pressure solid results are based on a spherical average of the anisotropic potential. The ground state energy and the pressure-volume relation are calculated. The metal-insulator phase transition pressure is predicted. Second virial coefficients are calculated for H2 and D2, as is the difference in second virial coefficients between ortho and para H2 and D2.

Elastic and inelastic collisions of swarms

NASA Astrophysics Data System (ADS)

Armbruster, Dieter; Martin, Stephan; Thatcher, Andrea

2017-04-01

Scattering interactions of swarms in potentials that are generated by an attraction-repulsion model are studied. In free space, swarms in this model form a well-defined steady state describing the translation of a stable formation of the particles whose shape depends on the interaction potential. Thus, the collision between a swarm and a boundary or between two swarms can be treated as (quasi)-particle scattering. Such scattering experiments result in internal excitations of the swarm or in bound states, respectively. In addition, varying a parameter linked to the relative importance of damping and potential forces drives transitions between elastic and inelastic scattering of the particles. By tracking the swarm's center of mass, a refraction rule is derived via simulations relating the incoming and outgoing directions of a swarm hitting the wall. Iterating the map derived from the refraction law allows us to predict and understand the dynamics and bifurcations of swarms in square boxes and in channels.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks.

PubMed

Lepoivre, Cyrille; Bergon, Aurélie; Lopez, Fabrice; Perumal, Narayanan B; Nguyen, Catherine; Imbert, Jean; Puthier, Denis

2012-01-31

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change

USGS Publications Warehouse

McCluney, Kevin E.; Belnap, Jayne; Collins, Scott L.; González, Angélica L.; Hagen, Elizabeth M.; Holland, J. Nathaniel; Kotler, Burt P.; Maestre, Fernando T.; Smith, Stanley D.; Wolf, Blair O.

2012-01-01

Species interactions play key roles in linking the responses of populations, communities, and ecosystems to environmental change. For instance, species interactions are an important determinant of the complexity of changes in trophic biomass with variation in resources. Water resources are a major driver of terrestrial ecology and climate change is expected to greatly alter the distribution of this critical resource. While previous studies have documented strong effects of global environmental change on species interactions in general, responses can vary from region to region. Dryland ecosystems occupy more than one-third of the Earth's land mass, are greatly affected by changes in water availability, and are predicted to be hotspots of climate change. Thus, it is imperative to understand the effects of environmental change on these globally significant ecosystems. Here, we review studies of the responses of population-level plant-plant, plant-herbivore, and predator-prey interactions to changes in water availability in dryland environments in order to develop new hypotheses and predictions to guide future research. To help explain patterns of interaction outcomes, we developed a conceptual model that views interaction outcomes as shifting between (1) competition and facilitation (plant-plant), (2) herbivory, neutralism, or mutualism (plant-herbivore), or (3) neutralism and predation (predator-prey), as water availability crosses physiological, behavioural, or population-density thresholds. We link our conceptual model to hypothetical scenarios of current and future water availability to make testable predictions about the influence of changes in water availability on species interactions. We also examine potential implications of our conceptual model for the relative importance of top-down effects and the linearity of patterns of change in trophic biomass with changes in water availability. Finally, we highlight key research needs and some possible broader impacts of our findings. Overall, we hope to stimulate and guide future research that links changes in water availability to patterns of species interactions and the dynamics of populations and communities in dryland ecosystems.

Interactions between temperature and nutrients across levels of ecological organization.

PubMed

Cross, Wyatt F; Hood, James M; Benstead, Jonathan P; Huryn, Alexander D; Nelson, Daniel

2015-03-01

Temperature and nutrient availability play key roles in controlling the pathways and rates at which energy and materials move through ecosystems. These factors have also changed dramatically on Earth over the past century as human activities have intensified. Although significant effort has been devoted to understanding the role of temperature and nutrients in isolation, less is known about how these two factors interact to influence ecological processes. Recent advances in ecological stoichiometry and metabolic ecology provide a useful framework for making progress in this area, but conceptual synthesis and review are needed to help catalyze additional research. Here, we examine known and potential interactions between temperature and nutrients from a variety of physiological, community, and ecosystem perspectives. We first review patterns at the level of the individual, focusing on four traits--growth, respiration, body size, and elemental content--that should theoretically govern how temperature and nutrients interact to influence higher levels of biological organization. We next explore the interactive effects of temperature and nutrients on populations, communities, and food webs by synthesizing information related to community size spectra, biomass distributions, and elemental composition. We use metabolic theory to make predictions about how population-level secondary production should respond to interactions between temperature and resource supply, setting up qualitative predictions about the flows of energy and materials through metazoan food webs. Last, we examine how temperature-nutrient interactions influence processes at the whole-ecosystem level, focusing on apparent vs. intrinsic activation energies of ecosystem processes, how to represent temperature-nutrient interactions in ecosystem models, and patterns with respect to nutrient uptake and organic matter decomposition. We conclude that a better understanding of interactions between temperature and nutrients will be critical for developing realistic predictions about ecological responses to multiple, simultaneous drivers of global change, including climate warming and elevated nutrient supply. © 2014 John Wiley & Sons Ltd.

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

Schreck, S. J.; Schepers, J. G.

Continued inquiry into rotor and blade aerodynamics remains crucial for achieving accurate, reliable prediction of wind turbine power performance under yawed conditions. To exploit key advantages conferred by controlled inflow conditions, we used EU-JOULE DATA Project and UAE Phase VI experimental data to characterize rotor power production under yawed conditions. Anomalies in rotor power variation with yaw error were observed, and the underlying fluid dynamic interactions were isolated. Unlike currently recognized influences caused by angled inflow and skewed wake, which may be considered potential flow interactions, these anomalies were linked to pronounced viscous and unsteady effects.

Strength of the interatomic potential derived from angular scans in LEIS

NASA Astrophysics Data System (ADS)

Primetzhofer, D.; Markin, S. N.; Draxler, M.; Beikler, R.; Taglauer, E.; Bauer, P.

2008-09-01

Angular scans were performed for a Cu(1 0 0) single crystal and He + ions. The results were compared to MARLOWE, KALYPSO and FAN simulations to obtain information on the interaction potential. The influence of the used evaluation procedure on the deduced scattering potential was investigated. The scattering potential is found to be weaker than what is predicted by an uncorrected TFM potential. It was found that the use of a single screening correction factor is applicable in a wide range of impact parameters. It is further shown that selection of single scattering trajectories and a limitation of information depth to the surface layers is possible for neutral and charge integrated spectra.

Predicting Brook Trout occurrence in stream reaches throughout their native range in the eastern United States

USGS Publications Warehouse

DeWeber, Jefferson Tyrell; Wagner, Tyler

2015-01-01

The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We developed a model to predict Brook Trout population status within individual stream reaches throughout the species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples based on the area under the receiver operating curve (∼0.78) and Cohen's kappa statistic (0.44). Predicted water temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased. Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature. Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing conservation and management efforts. These decision support tools can be used to identify the extent of potentially suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream reaches for a given action. Future work could extend the model to account for additional landscape or habitat characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use changes.

Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments

PubMed Central

Zhuang, Kai; Izallalen, Mounir; Mouser, Paula; Richter, Hanno; Risso, Carla; Mahadevan, Radhakrishnan; Lovley, Derek R

2011-01-01

The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies. PMID:20668487

Predictive and Experimental Approaches for Elucidating Protein–Protein Interactions and Quaternary Structures

PubMed Central

Nealon, John Oliver; Philomina, Limcy Seby

2017-01-01

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development. PMID:29206185

Optimizing Noble Gas-Water Interactions via Monte Carlo Simulations.

PubMed

Warr, Oliver; Ballentine, Chris J; Mu, Junju; Masters, Andrew

2015-11-12

In this work we present optimized noble gas-water Lennard-Jones 6-12 pair potentials for each noble gas. Given the significantly different atomic nature of water and the noble gases, the standard Lorentz-Berthelot mixing rules produce inaccurate unlike molecular interactions between these two species. Consequently, we find simulated Henry's coefficients deviate significantly from their experimental counterparts for the investigated thermodynamic range (293-353 K at 1 and 10 atm), due to a poor unlike potential well term (εij). Where εij is too high or low, so too is the strength of the resultant noble gas-water interaction. This observed inadequacy in using the Lorentz-Berthelot mixing rules is countered in this work by scaling εij for helium, neon, argon, and krypton by factors of 0.91, 0.8, 1.1, and 1.05, respectively, to reach a much improved agreement with experimental Henry's coefficients. Due to the highly sensitive nature of the xenon εij term, coupled with the reasonable agreement of the initial values, no scaling factor is applied for this noble gas. These resulting optimized pair potentials also accurately predict partitioning within a CO2-H2O binary phase system as well as diffusion coefficients in ambient water. This further supports the quality of these interaction potentials. Consequently, they can now form a well-grounded basis for the future molecular modeling of multiphase geological systems.

Analysis of the Interactions Between Thioredoxin and 20 Selenoproteins in Chicken.

PubMed

Liu, Qi; Yang, Jie; Cai, Jingzeng; Luan, Yilin; Sattar, Hamid; Liu, Man; Xu, Shiwen; Zhang, Ziwei

2017-10-01

Thioredoxin (Trx) is a small molecular protein with complicated functions in a number of processes, including inflammation, apoptosis, embryogenesis, cardiovascular disease, and redox regulation. Some selenoproteins, such as glutathione peroxidase (Gpx), iodothyronine deiodinase (Dio), and thioredoxin reductase (TR), are involved in redox regulation. However, whether there are interactions between Trx and selenoproteins is still not known. In the present paper, we used a Modeller, Hex 8.0.0, and the KFC2 Server to predict the interactions between Trx and selenoproteins. We used the Modeller to predict the target protein in objective format and assess the accuracy of the results. Molecular interaction studies with Trx and selenoproteins were performed using the molecular docking tools in Hex 8.0.0. Next, we used the KFC2 Server to further test the protein binding sites. In addition to the selenoprotein physiological functions, we also explored potential relationships between Trx and selenoproteins beyond all the results we got. The results demonstrate that Trx has the potential to interact with 19 selenoproteins, including iodothyronine deiodinase 1 (Dio1), iodothyronine deiodinase 3 (Dio3), glutathione peroxidase 1 (Gpx1), glutathione peroxidase 2 (Gpx2), glutathione peroxidase 3 (Gpx3), glutathione peroxidase 4 (Gpx4), selenoprotein H (SelH), selenoprotein I (SelI), selenoprotein M (SelM), selenoprotein N (SelN), selenoprotein T (SelT), selenoprotein U (SelU), selenoprotein W (SelW), selenoprotein 15 (Sep15), methionine sulfoxide reductase B (Sepx1), selenophosphate synthetase 1 (SPS1), TR1, TR2, and TR3, among which TR1, TR2, TR3, SPS1, Sep15, SelN, SelM, SelI, Gpx2, Gpx3, Gpx4, and Dio3 exhibited intense correlations with Trx. However, additional experiments are needed to verify them.

Fingerstroke time estimates for touchscreen-based mobile gaming interaction.

PubMed

Lee, Ahreum; Song, Kiburm; Ryu, Hokyoung Blake; Kim, Jieun; Kwon, Gyuhyun

2015-12-01

The growing popularity of gaming applications and ever-faster mobile carrier networks have called attention to an intriguing issue that is closely related to command input performance. A challenging mirroring game service, which simultaneously provides game service to both PC and mobile phone users, allows them to play games against each other with very different control interfaces. Thus, for efficient mobile game design, it is essential to apply a new predictive model for measuring how potential touch input compares to the PC interfaces. The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smartphone interface (i.e., tapping, pointing, dragging, and flicking). A modified KLM, tentatively called the fingerstroke-level model (FLM), is proposed using time estimates on regression models. Copyright © 2015 Elsevier B.V. All rights reserved.

Symmetry breaking gives rise to energy spectra of three states of matter

PubMed Central

Bolmatov, Dima; Musaev, Edvard T.; Trachenko, K.

2013-01-01

A fundamental task of statistical physics is to start with a microscopic Hamiltonian, predict the system's statistical properties and compare them with observable data. A notable current fundamental challenge is to tell whether and how an interacting Hamiltonian predicts different energy spectra, including solid, liquid and gas phases. Here, we propose a new idea that enables a unified description of all three states of matter. We introduce a generic form of an interacting phonon Hamiltonian with ground state configurations minimising the potential. Symmetry breaking SO(3) to SO(2), from the group of rotations in reciprocal space to its subgroup, leads to emergence of energy gaps of shear excitations as a consequence of the Goldstone theorem, and readily results in the emergence of energy spectra of solid, liquid and gas phases. PMID:24077388

Observation of coherent elastic neutrino-nucleus scattering

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

Akimov, D.; Albert, J. B.; An, P.

The coherent elastic scattering of neutrinos off nuclei has eluded detection for four decades, even though its predicted cross section is by far the largest of all low-energy neutrino couplings. This mode of interaction offers new opportunities to study neutrino properties and leads to a miniaturization of detector size, with potential technological applications. In this paper, we observed this process at a 6.7σ confidence level, using a low-background, 14.6-kilogram CsI[Na] scintillator exposed to the neutrino emissions from the Spallation Neutron Source at Oak Ridge National Laboratory. Characteristic signatures in energy and time, predicted by the standard model for this process,more » were observed in high signal-to-background conditions. Finally, improved constraints on nonstandard neutrino interactions with quarks are derived from this initial data set.« less

Observation of coherent elastic neutrino-nucleus scattering

DOE PAGES

Akimov, D.; Albert, J. B.; An, P.; ...

2017-08-03

The coherent elastic scattering of neutrinos off nuclei has eluded detection for four decades, even though its predicted cross section is by far the largest of all low-energy neutrino couplings. This mode of interaction offers new opportunities to study neutrino properties and leads to a miniaturization of detector size, with potential technological applications. In this paper, we observed this process at a 6.7σ confidence level, using a low-background, 14.6-kilogram CsI[Na] scintillator exposed to the neutrino emissions from the Spallation Neutron Source at Oak Ridge National Laboratory. Characteristic signatures in energy and time, predicted by the standard model for this process,more » were observed in high signal-to-background conditions. Finally, improved constraints on nonstandard neutrino interactions with quarks are derived from this initial data set.« less

Exploring the Potential Relationship between Eye Gaze and English L2 Speakers' Responses to Recasts

ERIC Educational Resources Information Center

McDonough, Kim; Crowther, Dustin; Kielstra, Paula; Trofimovich, Pavel

2015-01-01

This exploratory study investigated whether joint attention through eye gaze was predictive of second language (L2) speakers' responses to recasts. L2 English learners (N = 20) carried out communicative tasks with research assistants who provided feedback in response to non-targetlike (non-TL) forms. Their interaction was audio-recorded and their…

Getting to the root of the matter: landscape implications of plant-fungal interactions for tree migration in Alaska

Treesearch

Rebecca E. Hewitt; Alec P. Bennett; Amy L. Breen; Teresa N. Hollingsworth; D. Lee Taylor; F. Stuart Chapin; T. Scott Rupp

2016-01-01

Context   Forecasting the expansion of forest into Alaska tundra is critical to predicting regional ecosystem services, including climate feedbacks such as carbon storage. Controls over seedling establishment govern forest development and migration potential. Ectomycorrhizal fungi (EMF), obligate symbionts of all Alaskan tree species, are...

Psychosocial Correlates of Adolescent Drug Dealing in the Inner City: Potential Roles of Opportunity, Conventional Commitments, and Maturity

ERIC Educational Resources Information Center

Little, Michelle; Steinberg, Laurence

2006-01-01

This study examined a model of the simultaneous and interactive influence of social context, psychosocial attitudes, and individual maturity on the prediction of urban adolescent drug dealing. Five factors were found to significantly increase adolescents' opportunity for drug selling: low parental monitoring, poor neighborhood conditions, low…

Short-time dynamics of lysozyme solutions with competing short-range attraction and long-range repulsion: Experiment and theory

NASA Astrophysics Data System (ADS)

Riest, Jonas; Nägele, Gerhard; Liu, Yun; Wagner, Norman J.; Godfrin, P. Douglas

2018-02-01

Recently, atypical static features of microstructural ordering in low-salinity lysozyme protein solutions have been extensively explored experimentally and explained theoretically based on a short-range attractive plus long-range repulsive (SALR) interaction potential. However, the protein dynamics and the relationship to the atypical SALR structure remain to be demonstrated. Here, the applicability of semi-analytic theoretical methods predicting diffusion properties and viscosity in isotropic particle suspensions to low-salinity lysozyme protein solutions is tested. Using the interaction potential parameters previously obtained from static structure factor measurements, our results of Monte Carlo simulations representing seven experimental lysoyzme samples indicate that they exist either in dispersed fluid or random percolated states. The self-consistent Zerah-Hansen scheme is used to describe the static structure factor, S(q), which is the input to our calculation schemes for the short-time hydrodynamic function, H(q), and the zero-frequency viscosity η. The schemes account for hydrodynamic interactions included on an approximate level. Theoretical predictions for H(q) as a function of the wavenumber q quantitatively agree with experimental results at small protein concentrations obtained using neutron spin echo measurements. At higher concentrations, qualitative agreement is preserved although the calculated hydrodynamic functions are overestimated. We attribute the differences for higher concentrations and lower temperatures to translational-rotational diffusion coupling induced by the shape and interaction anisotropy of particles and clusters, patchiness of the lysozyme particle surfaces, and the intra-cluster dynamics, features not included in our simple globular particle model. The theoretical results for the solution viscosity, η, are in qualitative agreement with our experimental data even at higher concentrations. We demonstrate that semi-quantitative predictions of diffusion properties and viscosity of solutions of globular proteins are possible given only the equilibrium structure factor of proteins. Furthermore, we explore the effects of changing the attraction strength on H(q) and η.

Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

PubMed

Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

2013-01-27

A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

Liquid drops attract or repel by the inverted Cheerios effect.

PubMed

Karpitschka, Stefan; Pandey, Anupam; Lubbers, Luuk A; Weijs, Joost H; Botto, Lorenzo; Das, Siddhartha; Andreotti, Bruno; Snoeijer, Jacco H

2016-07-05

Solid particles floating at a liquid interface exhibit a long-ranged attraction mediated by surface tension. In the absence of bulk elasticity, this is the dominant lateral interaction of mechanical origin. Here, we show that an analogous long-range interaction occurs between adjacent droplets on solid substrates, which crucially relies on a combination of capillarity and bulk elasticity. We experimentally observe the interaction between droplets on soft gels and provide a theoretical framework that quantitatively predicts the interaction force between the droplets. Remarkably, we find that, although on thick substrates the interaction is purely attractive and leads to drop-drop coalescence, for relatively thin substrates a short-range repulsion occurs, which prevents the two drops from coming into direct contact. This versatile interaction is the liquid-on-solid analog of the "Cheerios effect." The effect will strongly influence the condensation and coarsening of drops on soft polymer films, and has potential implications for colloidal assembly and mechanobiology.

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation.

PubMed

Levy, Roie; Carr, Rogan; Kreimer, Anat; Freilich, Shiri; Borenstein, Elhanan

2015-05-17

Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm. NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms' niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds. The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.

GLADIATOR: a global approach for elucidating disease modules.

PubMed

Silberberg, Yael; Kupiec, Martin; Sharan, Roded

2017-05-26

Understanding the genetic basis of disease is an important challenge in biology and medicine. The observation that disease-related proteins often interact with one another has motivated numerous network-based approaches for deciphering disease mechanisms. In particular, protein-protein interaction networks were successfully used to illuminate disease modules, i.e., interacting proteins working in concert to drive a disease. The identification of these modules can further our understanding of disease mechanisms. We devised a global method for the prediction of multiple disease modules simultaneously named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction). GLADIATOR relies on a gold-standard disease phenotypic similarity to obtain a pan-disease view of the underlying modules. To traverse the search space of potential disease modules, we applied a simulated annealing algorithm aimed at maximizing the correlation between module similarity and the gold-standard phenotypic similarity. Importantly, this optimization is employed over hundreds of diseases simultaneously. GLADIATOR's predicted modules highly agree with current knowledge about disease-related proteins. Furthermore, the modules exhibit high coherence with respect to functional annotations and are highly enriched with known curated pathways, outperforming previous methods. Examination of the predicted proteins shared by similar diseases demonstrates the diverse role of these proteins in mediating related processes across similar diseases. Last, we provide a detailed analysis of the suggested molecular mechanism predicted by GLADIATOR for hyperinsulinism, suggesting novel proteins involved in its pathology. GLADIATOR predicts disease modules by integrating knowledge of disease-related proteins and phenotypes across multiple diseases. The predicted modules are functionally coherent and are more in line with current biological knowledge compared to modules obtained using previous disease-centric methods. The source code for GLADIATOR can be downloaded from http://www.cs.tau.ac.il/~roded/GLADIATOR.zip .

Nonverbal components of Theory of Mind in typical and atypical development.

PubMed

Kampis, Dora; Fogd, Dóra; Kovács, Ágnes Melinda

2017-08-01

To successfully navigate the human social world one needs to realize that behavior is guided by mental states such as goals and beliefs. Humans are highly proficient in using mental states to explain and predict their conspecific's behavior, which enables adjusting one's own behavior in online social interactions. Whereas according to recent studies even young infants seem to integrate others' beliefs into their own behavior, it is unclear what processes contribute to such competencies and how they may develop. Here we analyze a set of possible nonverbal components of theory of mind that may be involved in taking into account others' mental states, and discuss findings from typical and atypical development. To track an agent's belief one needs to (i) pay attention to agents that might be potential belief holders, and identify their focus of attention and their potential belief contents; (ii) keep track of their different experiences and their consequent beliefs, and (iii) to make behavioral predictions based on such beliefs. If an individual fails to predict an agent's behavior depending on the agent's beliefs, this may be due to a problem at any stage in the above processes. An analysis of the possible nonverbal processes contributing to belief tracking and their functioning in typical and atypical development aims to provide new insights into the possible mechanisms that make human social interactions uniquely rich. Copyright © 2016 Elsevier Inc. All rights reserved.

Active sites prediction and binding analysis E1-E2 protein human papillomavirus with biphenylsulfonacetic acid

NASA Astrophysics Data System (ADS)

Iryani, I.; Amelia, F.; Iswendi, I.

2018-04-01

Cervix cancer triggered by Human papillomavirus infection is the second cause to woman death in worldwide. The binding site of E1-E2 protein of HPV 16 is not known from a 3-D structure yet, so in this study we address this issue to study the structure of E1-E2 protein from Human papillomavirus type 16 and to find its potential binding sites using biphenylsulfonacetic acid as inhibitor. Swiss model was used for 3D structure prediction and PDB: 2V9P (E1 protein) and 2NNU (E2 protein) having 52.32% and 100% identity respectively was selected as a template. The 3D model structure developed of E1 and E2 in the core and allowed regions were 99.2% and 99.5%. The ligand binding sites were predicted using online server meta pocket 2.0 and MOE 2009.10 was used for docking. E1-and E2 protein of HPV-16 has three potential binding site that can interact with the inhibitors. The Docking biphenylsulfonacetic acid using these binding sites shows that ligand interact with the protein through hydrogen bonds on Lys 403, Arg 410, His 551 in the first pocket, on Tyr 32, Leu 99 in the second pocket, and Lys 558m Lys 517 in the third pocket.

Predicting Interactions between Common Dolphins and the Pole-and-Line Tuna Fishery in the Azores

PubMed Central

Cruz, Maria João; Menezes, Gui; Machete, Miguel; Silva, Mónica A.

2016-01-01

Common dolphins (Delphinus delphis) are responsible for the large majority of interactions with the pole-and-line tuna fishery in the Azores but the underlying drivers remain poorly understood. In this study we investigate the influence of various environmental and fisheries-related factors in promoting the interaction of common dolphins with this fishery and estimate the resultant catch losses. We analysed 15 years of fishery and cetacean interaction data (1998–2012) collected by observers placed aboard tuna fishing vessels. Dolphins interacted in less than 3% of the fishing events observed during the study period. The probability of dolphin interaction varied significantly between years with no evident trend over time. Generalized additive modeling results suggest that fishing duration, sea surface temperature and prey abundance in the region were the most important factors explaining common dolphin interaction. Dolphin interaction had no impact on the catches of albacore, skipjack and yellowfin tuna but resulted in significantly lower catches of bigeye tuna, with a predicted median annual loss of 13.5% in the number of fish captured. However, impact on bigeye catches varied considerably both by year and fishing area. Our work shows that rates of common dolphin interaction with the pole-and-line tuna fishery in the Azores are low and showed no signs of increase over the study period. Although overall economic impact was low, the interaction may lead to significant losses in some years. These findings emphasize the need for continued monitoring and for further research into the consequences and economic viability of potential mitigation measures. PMID:27851763

Predicting Interactions between Common Dolphins and the Pole-and-Line Tuna Fishery in the Azores.

PubMed

Cruz, Maria João; Menezes, Gui; Machete, Miguel; Silva, Mónica A

2016-01-01

Common dolphins (Delphinus delphis) are responsible for the large majority of interactions with the pole-and-line tuna fishery in the Azores but the underlying drivers remain poorly understood. In this study we investigate the influence of various environmental and fisheries-related factors in promoting the interaction of common dolphins with this fishery and estimate the resultant catch losses. We analysed 15 years of fishery and cetacean interaction data (1998-2012) collected by observers placed aboard tuna fishing vessels. Dolphins interacted in less than 3% of the fishing events observed during the study period. The probability of dolphin interaction varied significantly between years with no evident trend over time. Generalized additive modeling results suggest that fishing duration, sea surface temperature and prey abundance in the region were the most important factors explaining common dolphin interaction. Dolphin interaction had no impact on the catches of albacore, skipjack and yellowfin tuna but resulted in significantly lower catches of bigeye tuna, with a predicted median annual loss of 13.5% in the number of fish captured. However, impact on bigeye catches varied considerably both by year and fishing area. Our work shows that rates of common dolphin interaction with the pole-and-line tuna fishery in the Azores are low and showed no signs of increase over the study period. Although overall economic impact was low, the interaction may lead to significant losses in some years. These findings emphasize the need for continued monitoring and for further research into the consequences and economic viability of potential mitigation measures.

Topology association analysis in weighted protein interaction network for gene prioritization

NASA Astrophysics Data System (ADS)

Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

2016-11-01

Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

PubMed

Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

2017-07-24

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

Probing nanomechanical interaction at the interface between biological membrane and potentially toxic chemical.

PubMed

Lim, Chanoong; Park, Sohee; Park, Jinwoo; Ko, Jina; Lee, Dong Woog; Hwang, Dong Soo

2018-04-12

Various xenobiotics interact with biological membranes, and precise evaluations of the molecular interactions between them are essential to foresee the toxicity and bioavailability of existing or newly synthesized molecules. In this study, surface forces apparatus (SFA) measurement and Langmuir trough based tensiometry are performed to reveal nanomechanical interaction mechanisms between potential toxicants and biological membranes for ex vivo toxicity evaluation. As a toxicant, polyhexamethylene guanidine (PHMG) was selected because PHMG containing humidifier disinfectant and Vodka caused lots of victims in both S. Korea and Russia, respectively, due to the lack of holistic toxicity evaluation of PHMG. Here, we measured strong attraction (Wad ∼4.2 mJ/m 2 ) between PHMG and head group of biological membranes while no detectable adhesion force between the head group and control molecules was measured. Moreover, significant changes in π-A isotherm of 1,2-Dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) monolayers were measured upon PHMG adsorption. These results indicate PHMG strongly binds to hydrophilic group of lipid membranes and alters the structural and phase behavior of them. More importantly, complementary utilization of SFA and Langmuir trough techniques are found to be useful to predict the potential toxicity of a chemical by evaluating the molecular interaction with biological membranes, the primary protective barrier for living organisms. Copyright © 2018 Elsevier B.V. All rights reserved.

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Suarez, M. J.; Heiser, M.

1998-01-01

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

Hexatic smectic phase with algebraically decaying bond-orientational order

NASA Astrophysics Data System (ADS)

Agosta, Lorenzo; Metere, Alfredo; Dzugutov, Mikhail

2018-05-01

The hexatic phase predicted by the theories of two-dimensional melting is characterized by the power-law decay of the orientational correlations, whereas the in-layer bond orientational order in all the hexatic smectic phases observed so far was found to be long range. We report a hexatic smectic phase where the in-layer bond orientational correlations decay algebraically, in quantitative agreement with the hexatic ordering predicted by the theory for two dimensions. The phase was formed in a molecular dynamics simulation of a one-component system of particles interacting via a spherically symmetric potential. The present results thus demonstrate that the theoretically predicted two-dimensional hexatic order can exist in a three-dimensional system.

Earthquake prediction: the interaction of public policy and science.

PubMed Central

Jones, L M

1996-01-01

Earthquake prediction research has searched for both informational phenomena, those that provide information about earthquake hazards useful to the public, and causal phenomena, causally related to the physical processes governing failure on a fault, to improve our understanding of those processes. Neither informational nor causal phenomena are a subset of the other. I propose a classification of potential earthquake predictors of informational, causal, and predictive phenomena, where predictors are causal phenomena that provide more accurate assessments of the earthquake hazard than can be gotten from assuming a random distribution. Achieving higher, more accurate probabilities than a random distribution requires much more information about the precursor than just that it is causally related to the earthquake. PMID:11607656

Structural Dynamic Behavior of Wind Turbines

NASA Technical Reports Server (NTRS)

Thresher, Robert W.; Mirandy, Louis P.; Carne, Thomas G.; Lobitz, Donald W.; James, George H. III

2009-01-01

The structural dynamicist s areas of responsibility require interaction with most other members of the wind turbine project team. These responsibilities are to predict structural loads and deflections that will occur over the lifetime of the machine, ensure favorable dynamic responses through appropriate design and operational procedures, evaluate potential design improvements for their impact on dynamic loads and stability, and correlate load and control test data with design predictions. Load prediction has been a major concern in wind turbine designs to date, and it is perhaps the single most important task faced by the structural dynamics engineer. However, even if we were able to predict all loads perfectly, this in itself would not lead to an economic system. Reduction of dynamic loads, not merely a "design to loads" policy, is required to achieve a cost-effective design. The two processes of load prediction and structural design are highly interactive: loads and deflections must be known before designers and stress analysts can perform structural sizing, which in turn influences the loads through changes in stiffness and mass. Structural design identifies "hot spots" (local areas of high stress) that would benefit most from dynamic load alleviation. Convergence of this cycle leads to a turbine structure that is neither under-designed (which may result in structural failure), nor over-designed (which will lead to excessive weight and cost).

Toward a Unified Sub-symbolic Computational Theory of Cognition

PubMed Central

Butz, Martin V.

2016-01-01

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development toward the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification, and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper. PMID:27445895

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy.

PubMed

Rahaman, Obaidur; Estrada, Trilce P; Doren, Douglas J; Taufer, Michela; Brooks, Charles L; Armen, Roger S

2011-09-26

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.

De novo Transcriptome Analysis of Rhizoctonia solani AG1 IA Strain Early Invasion in Zoysia japonica Root.

PubMed

Zhu, Chen; Ai, Lin; Wang, Li; Yin, Pingping; Liu, Chenglan; Li, Shanshan; Zeng, Huiming

2016-01-01

Zoysia japonica brown spot was caused by necrotrophic fungus Rhizoctonia solani invasion, which led to severe financial loss in city lawn and golf ground maintenance. However, little was known about the molecular mechanism of R. solani pathogenicity in Z. japonica. In this study we examined early stage interaction between R. solani AG1 IA strain and Z. japonica cultivar "Zenith" root by cell ultra-structure analysis, pathogenesis-related proteins assay and transcriptome analysis to explore molecular clues for AG1 IA strain pathogenicity in Z. japonica. No obvious cell structure damage was found in infected roots and most pathogenesis-related protein activities showedg a downward trend especially in 36 h post inoculation, which exhibits AG1 IA strain stealthy invasion characteristic. According to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database classification, most DEGs in infected "Zenith" roots dynamically changed especially in three aspects, signal transduction, gene translation, and protein synthesis. Total 3422 unigenes of "Zenith" root were predicted into 14 kinds of resistance (R) gene class. Potential fungal resistance related unigenes of "Zenith" root were involved in ligin biosynthesis, phytoalexin synthesis, oxidative burst, wax biosynthesis, while two down-regulated unigenes encoding leucine-rich repeat receptor protein kinase and subtilisin-like protease might be important for host-derived signal perception to AG1 IA strain invasion. According to Pathogen Host Interaction (PHI) database annotation, 1508 unigenes of AG1 IA strain were predicted and classified into 37 known pathogen species, in addition, unigenes encoding virulence, signaling, host stress tolerance, and potential effector were also predicted. This research uncovered transcriptional profiling during the early phase interaction between R. solani AG1 IA strain and Z. japonica, and will greatly help identify key pathogenicity of AG1 IA strain.

Social Interaction in Infants’ Learning of Second-Language Phonetics: An Exploration of Brain-Behavior Relations

PubMed Central

Conboy, Barbara T.; Brooks, Rechele; Meltzoff, Andrew N.; Kuhl, Patricia K.

2015-01-01

Infants learn phonetic information from a second language with live-person presentations, but not television or audio-only recordings. To understand the role of social interaction in learning a second language, we examined infants’ joint attention with live, Spanish-speaking tutors and used a neural measure of phonetic learning. Infants’ eye-gaze behaviors during Spanish sessions at 9.5 – 10.5 months of age predicted second-language phonetic learning, assessed by an event-related potential (ERP) measure of Spanish phoneme discrimination at 11 months. These data suggest a powerful role for social interaction at the earliest stages of learning a new language. PMID:26179488

Physical function and self-rated health status as predictors of mortality: results from longitudinal analysis in the ilSIRENTE study.

PubMed

Cesari, Matteo; Onder, Graziano; Zamboni, Valentina; Manini, Todd; Shorr, Ronald I; Russo, Andrea; Bernabei, Roberto; Pahor, Marco; Landi, Francesco

2008-12-22

Physical function measures have been shown to predict negative health-related events in older persons, including mortality. These markers of functioning may interact with the self-rated health (SRH) in the prediction of events. Aim of the present study is to compare the predictive value for mortality of measures of physical function and SRH status, and test their possible interactions. Data are from 335 older persons aged >or= 80 years (mean age 85.6 years) enrolled in the "Invecchiamento e Longevità nel Sirente" (ilSIRENTE) study. The predictive values for mortality of 4-meter walk test, Short Physical Performance Battery (SPPB), hand grip strength, Activities of Daily Living (ADL) scale, Instrumental ADL (IADL) scale, and a SRH scale were compared using proportional hazard models. Kaplan-Meier survival curves for mortality and Receiver Operating Characteristic (ROC) curve analyses were also computed to estimate the predictive value of the independent variables of interest for mortality (alone and in combination). During the 24-month follow-up (mean 1.8 years), 71 (21.2%) events occurred in the study sample. All the tested variables were able to significantly predict mortality. No significant interaction was reported between physical function measures and SRH. The SPPB score was the strongest predictor of overall mortality after adjustment for potential confounders (per SD increase; HR 0.64; 95%CI 0.48-0.86). A similar predictive value was showed by the SRH (per SD increase; HR 0.76; 95%CI 0.59-0.97). The chair stand test was the SPPB subtask showing the highest prognostic value. All the tested measures are able to predict mortality with different extents, but strongest results were obtained from the SPPB and the SRH. The chair stand test may be as useful as the complete SPPB in estimating the mortality risk.

Interactions Between Energy Drink Consumption and Sleep Problems: Associations with Alcohol Use Among Young Adolescents.

PubMed

Marmorstein, Naomi R

2017-09-01

Background: Energy drink consumption and sleep problems are both associated with alcohol use among adolescents. In addition, caffeine consumption (including energy drinks) is associated with sleep problems. However, information about how these three constructs may interact is limited. The goal of this study was to examine potential interactions between energy drink consumption and sleep problems in the concurrent prediction of alcohol use among young adolescents. Coffee and soda consumption were also examined for comparison. Methods: Participants from the Camden Youth Development Study were included ( n  = 127; mean age = 13.1; 68% Hispanic, 29% African American) and questionnaire measures of frequency of caffeinated beverage consumption (energy drinks, coffee, and soda), sleep (initial insomnia, sleep disturbances, daytime fatigue, and sleep duration), and alcohol consumption were used. Regression analyses were conducted to examine interactions between caffeinated beverage consumption and sleep in the concurrent prediction of alcohol use. Results: Energy drink consumption interacted with initial insomnia and daytime fatigue to concurrently predict particularly frequent alcohol use among those with either of these sleep-related problems and energy drink consumption. The pattern of results for coffee consumption was similar for insomnia but reached only a trend level of significance. Results of analyses examining soda consumption were nonsignificant. Conclusions: Young adolescents who both consume energy drinks and experience initial insomnia and/or daytime fatigue are at particularly high risk for alcohol use. Coffee consumption appears to be associated with similar patterns. Longitudinal research is needed to explain the developmental pathways by which these associations emerge, as well as mediators and moderators of these associations.

Prediction of the Aero-Acoustic Performance of Open Rotors

NASA Technical Reports Server (NTRS)

VanZante, Dale; Envia, Edmane

2014-01-01

The rising cost of jet fuel has renewed interest in contrarotating open rotor propulsion systems. Contemporary design methods offer the potential to maintain the inherently high aerodynamic efficiency of open rotors while greatly reducing their noise output, something that was not feasible in the 1980's designs. The primary source mechanisms of open rotor noise generation are thought to be the front rotor wake and tip vortex interacting with the aft rotor. In this paper, advanced measurement techniques and high-fidelity prediction tools are used to gain insight into the relative importance of the contributions to the open rotor noise signature of the front rotor wake and rotor tip vortex. The measurements include three-dimensional particle image velocimetry of the intra-rotor flowfield and the acoustic field of a model-scale open rotor. The predictions provide the unsteady flowfield and the associated acoustic field. The results suggest that while the front rotor tip vortex can have a significant influence on the blade passing tone noise produced by the aft rotor, the front rotor wake plays the decisive role in the generation of the interaction noise produced as a result of the unsteady aerodynamic interaction of the two rotors. At operating conditions typical of takeoff and landing operations, the interaction noise level is easily on par with that generated by the individual rotors, and in some cases is even higher. This suggests that a comprehensive approach to reducing open rotor noise should include techniques for mitigating the wake of the front rotor as well as eliminating the interaction of the front rotor tip vortex with the aft rotor blade tip.

The effect of pathological narcissism on interpersonal and affective processes in social interactions.

PubMed

Wright, Aidan G C; Stepp, Stephanie D; Scott, Lori N; Hallquist, Michael N; Beeney, Joseph E; Lazarus, Sophie A; Pilkonis, Paul A

2017-10-01

Narcissism has significant interpersonal costs, yet little research has examined behavioral and affective patterns characteristic of narcissism in naturalistic settings. Here we studied the effect of narcissistic features on the dynamic processes of interpersonal behavior and affect in daily life. We used interpersonal theory to generate transactional models of social interaction (i.e., linkages among perceptions of others' behavior, affect, and one's own behavior) predicted to be characteristic of narcissism. Psychiatric outpatients (N = 102) completed clinical interviews and a 21-day ecological momentary assessment protocol using smartphones. After social interactions (N = 5,781), participants reported on perceptions of their interaction partner's behavior (scored along the dimensions of dominant-submissive and affiliative-quarrelsome), their own affect, and their own behavior. Multilevel structural equation modeling was used to examine dynamic links among behavior and affect across interactions, and the role of narcissism in moderating these links. Results showed that perceptions of others' dominance did not predict dominant behavior, but did predict quarrelsome behavior, and this link was potentiated by narcissism. Furthermore, the link between others' dominance and one's own quarrelsome behavior was mediated by negative affect. Moderated mediation was also found: Narcissism amplified the link between ratings of others' dominance and one's own quarrelsomeness and negative affect. Narcissism did not moderate the link between other dominance and own dominance, nor the link between other affiliation and own affiliation. These results suggest that narcissism is associated with specific interpersonal and affective processes, such that sensitivity to others' dominance triggers antagonistic behavior in daily life. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

PubMed Central

Thaden, Joshua T; Mogno, Ilaria; Wierzbowski, Jamey; Cottarel, Guillaume; Kasif, Simon; Collins, James J; Gardner, Timothy S

2007-01-01

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data. PMID:17214507

A Network-Based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions

PubMed Central

Oh, Min; Ahn, Jaegyoon; Yoon, Youngmi

2014-01-01

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer’s disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer’s disease. PMID:25356910

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

DOE PAGES

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

2015-06-04

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less