Заболевания печени

by Xueni Pan, Marco Gillies, Chris Barker, David M. Clark, Mel Slater

Background

Male volunteers entered an immersive virtual reality that depicted a party, where they were approached by a lone virtual woman who initiated a conversation. The goal was to study how socially anxious and socially confident men would react to this event. Interest focused on whether the socially anxious participants would exhibit sustained anxiety during the conversation or whether this would diminish over time, and differ from the responses of the more socially confident men.

Methodology

The scenario was a party with five virtual characters, four sitting at a distance from the participant and talking amongst themselves and one lone woman standing closer. The woman approached the participant, introduced herself and initiated a conversation that was first about mundane matters and then became more personal and intimate. Participants were men who were either relatively socially confident (18) or socially anxious in their relationships with women (18). A second experimental factor was whether or not the other four characters occasionally looked towards the participant. There was a post-trial questionnaire about social anxiety in relation to the experience, and skin conductance and ECG physiological measures were recorded. Our expectation was that the socially anxious participants would show greater anxiety throughout.

Conclusions

Compared to baseline readings both socially confident and socially anxious groups on average showed signs of significantly increased stress at the initial approach of the virtual woman. The stress then diminished once the conversation entered into the mundane phase and then did not significantly change. Comparing pre- and post-questionnaire anxiety scores there was no change for the more confident participants but a significant decrease in average score amongst the anxious group. The methodology of placing socially anxious participants in a virtual reality where they can gain experience of how to act in a stressful situation promises a novel way forward for treating social anxiety.

by Rong-Ping Li, Guang-Sheng Zhou

Plant phenology models, especially leafing models, play critical roles in evaluating the impact of climate change on the primary production of temperate plants. Existing models based on temperature alone could not accurately simulate plant leafing in arid and semi-arid regions. The objective of the present study was to test the suitability of the existing temperature-based leafing models in arid and semi-arid regions, and to develop a temperature-precipitation based leafing model (TP), based on the long-term (i.e., 12–27 years) ground leafing observation data and meteorological data in Northeast China. The better simulation of leafing for all the plant species in Northeast China was given by TP with the fixed starting date (TPn) than with the parameterized starting date (TPm), which gave the smallest average root mean square error (RMSE) of 4.21 days. Tree leafing models were validated with independent data, and the coefficient of determination (R²) was greater than 0.60 in 75% of the estimates by TP and the spring warming model (SW) with the fixed starting date. The average RMSE of herb leafing simulated by TPn was 5.03 days, much lower than other models (>9.51 days), while the average R² of TPn and TPm were 0.68 and 0.57, respectively, much higher than the other models (<0.22). It indicates that TPn is a universal model and more suitable for simulating leafing of trees and herbs than the prior models. Furthermore, water is an important factor determining herb leafing in arid and semi-arid temperate regions.

by Robert C. Hilborn, Benjamin Brookshire, Jenna Mattingly, Anusha Purushotham, Anuraag Sharma

We explore the connection between a stochastic simulation model and an ordinary differential equations (ODEs) model of the dynamics of an excitable gene circuit that exhibits noise-induced oscillations. Near a bifurcation point in the ODE model, the stochastic simulation model yields behavior dramatically different from that predicted by the ODE model. We analyze how that behavior depends on the gene copy number and find very slow convergence to the large number limit near the bifurcation point. The implications for understanding the dynamics of gene circuits and other birth-death dynamical systems with small numbers of constituents are discussed.

by Abhijit Aithal, Arun Sharma, Shilpy Joshi, Gajendra P. S. Raghava, Grish C. Varshney

Vaccines based on microbial cell surface polysaccharides have long been considered as attractive means to control infectious diseases. To realize this goal, detailed systematic information about the antigenic polysaccharide is necessary. However, only a few databases that provide limited knowledge in this area are available. This paper describes PolysacDB, a manually curated database of antigenic polysaccharides. We collected and compiled comprehensive information from literature and web resources about antigenic polysaccharides of microbial origin. The current version of the database has 1,554 entries of 149 different antigenic polysaccharides from 347 different microbes. Each entry provides comprehensive information about an antigenic polysaccharide, i.e., its origin, function, protocols for its conjugation to carriers, antibodies produced, details of assay systems, specificities of antibodies, proposed epitopes involved and antibody utilities. For convenience to the user, we have integrated web interface for searching, advanced searching and browsing data in database. This database will be useful for researchers working on polysaccharide-based vaccines. It is freely available from the URL: http://crdd.osdd.net/raghava/polysacdb/.

by Bogdan Barz, Brigita Urbanc

Amyloid -protein (A) is central to the pathology of Alzheimer's disease. A 5% difference in the primary structure of the two predominant alloforms, A and A, results in distinct assembly pathways and toxicity properties. Discrete molecular dynamics (DMD) studies of A and A assembly resulted in alloform-specific oligomer size distributions consistent with experimental findings. Here, a large ensemble of DMD–derived A and A monomers and dimers was subjected to fully atomistic molecular dynamics (MD) simulations using the OPLS-AA force field combined with two water models, SPCE and TIP3P. The resulting all-atom conformations were slightly larger, less compact, had similar turn and lower -strand propensities than those predicted by DMD. Fully atomistic A and A monomers populated qualitatively similar free energy landscapes. In contrast, the free energy landscape of A dimers indicated a larger conformational variability in comparison to that of A dimers. A dimers were characterized by an increased flexibility in the N-terminal region D1-R5 and a larger solvent exposure of charged amino acids relative to A dimers. Of the three positively charged amino acids, R5 was the most and K16 the least involved in salt bridge formation. This result was independent of the water model, alloform, and assembly state. Overall, salt bridge propensities increased upon dimer formation. An exception was the salt bridge propensity of K28, which decreased upon formation of A dimers and was significantly lower than in A dimers. The potential relevance of the three positively charged amino acids in mediating the A oligomer toxicity is discussed in the light of available experimental data.

by Pinyi Lu, Raquel Hontecillas, William T. Horne, Adria Carbo, Monica Viladomiu, Mireia Pedragosa, David R. Bevan, Stephanie N. Lewis, Josep Bassaganya-Riera

Background

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects.

Methodology/Principal Findings

The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses.

Conclusions/Significance

LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

by Renae K. Hovey, Kimberly P. Van Niel, Lynda M. Bellchambers, Matthew B. Pember

Background

The western rock lobster, Panulirus cygnus, is endemic to Western Australia and supports substantial commercial and recreational fisheries. Due to and its wide distribution and the commercial and recreational importance of the species a key component of managing western rock lobster is understanding the ecological processes and interactions that may influence lobster abundance and distribution. Using terrain analyses and distribution models of substrate and benthic biota, we assess the physical drivers that influence the distribution of lobsters at a key fishery site.

Methods and Findings

Using data collected from hydroacoustic and towed video surveys, 20 variables (including geophysical, substrate and biota variables) were developed to predict the distributions of substrate type (three classes of reef, rhodoliths and sand) and dominant biota (kelp, sessile invertebrates and macroalgae) within a 40 km² area about 30 km off the west Australian coast. Lobster presence/absence data were collected within this area using georeferenced pots. These datasets were used to develop a classification tree model for predicting the distribution of the western rock lobster. Interestingly, kelp and reef were not selected as predictors. Instead, the model selected geophysical and geomorphic scalar variables, which emphasise a mix of terrain within limited distances. The model of lobster presence had an adjusted D² of 64 and an 80% correct classification.

Conclusions

Species distribution models indicate that juxtaposition in fine scale terrain is most important to the western rock lobster. While key features like kelp and reef may be important to lobster distribution at a broad scale, it is the fine scale features in terrain that are likely to define its ecological niche. Determining the most appropriate landscape configuration and scale will be essential to refining niche habitats and will aid in selecting appropriate sites for protecting critical lobster habitats.

by Konstantin Klemm, Anita Mehta, Peter F. Stadler

Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space. Here we show how redundant non-invertible encodings enhance optimization by enriching the density of low-energy states. In addition, smooth landscapes may be established on encoded state spaces to guide local search dynamics towards the ground state.

by Stephany N. Duda, Bryan E. Shepherd, Cynthia S. Gadd, Daniel R. Masys, Catherine C. McGowan

Observational studies of health conditions and outcomes often combine clinical care data from many sites without explicitly assessing the accuracy and completeness of these data. In order to improve the quality of data in an international multi-site observational cohort of HIV-infected patients, the authors conducted on-site, Good Clinical Practice-based audits of the clinical care datasets submitted by participating HIV clinics. Discrepancies between data submitted for research and data in the clinical records were categorized using the audit codes published by the European Organization for the Research and Treatment of Cancer. Five of seven sites had error rates >10% in key study variables, notably laboratory data, weight measurements, and antiretroviral medications. All sites had significant discrepancies in medication start and stop dates. Clinical care data, particularly antiretroviral regimens and associated dates, are prone to substantial error. Verifying data against source documents through audits will improve the quality of databases and research and can be a technique for retraining staff responsible for clinical data collection. The authors recommend that all participants in observational cohorts use data audits to assess and improve the quality of data and to guide future data collection and abstraction efforts at the point of care.

by Emöke-Ágnes Horvát, Michael Hanselmann, Fred A. Hamprecht, Katharina A. Zweig

Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve () of at least for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.

by Sundarapandian Thangapandian, Shalini John, Mahreen Arooj, Keun Woo Lee

Human leukotriene A4 hydrolase (hLTA4H) is a bi-functional enzyme catalyzes the hydrolase and aminopeptidase functions upon the fatty acid and peptide substrates, respectively, utilizing the same but overlapping binding site. Particularly the hydrolase function of this enzyme catalyzes the rate-limiting step of the leukotriene (LT) cascade that converts the LTA4 to LTB4. This product is a potent pro-inflammatory activator of inflammatory responses and thus blocking this conversion provides a valuable means to design anti-inflammatory agents. Four structurally very similar chemical compounds with highly different inhibitory profile towards the hydrolase function of hLTA4H were selected from the literature. Molecular dynamics (MD) simulations of the complexes of hLTA4H with these inhibitors were performed and the results have provided valuable information explaining the reasons for the differences in their biological activities. Binding mode analysis revealed that the additional thiophene moiety of most active inhibitor helps the pyrrolidine moiety to interact the most important R563 and K565 residues. The hLTA4H complexes with the most active compound and substrate were utilized in the development of hybrid pharmacophore models. These developed pharmacophore models were used in screening chemical databases in order to identify lead candidates to design potent hLTA4H inhibitors. Final evaluation based on molecular docking and electronic parameters has identified three compounds of diverse chemical scaffolds as potential leads to be used in novel and potent hLTA4H inhibitor design.

by Ash Mohammad Abbas

In this paper, we describe some bounds and inequalities relating -index, -index, -index, and generalized impact factor. We derive the bounds and inequalities relating these indexing parameters from their basic definitions and without assuming any continuous model to be followed by any of them. We verify the theorems using citation data for five Price Medalists. We observe that the lower bound for -index given by Theorem 2, , comes out to be more accurate as compared to Schubert-Glanzel relation for a proportionality constant of , where is the number of citations and is the number of papers referenced. Also, the values of -index obtained using Theorem 2 outperform those obtained using Egghe-Liang-Rousseau power law model for the given citation data of Price Medalists. Further, we computed the values of upper bound on -index given by Theorem 3, , where denotes the value of -index. We observe that the upper bound on -index given by Theorem 3 is reasonably tight for the given citation record of Price Medalists.

by Pieter-Jan Kindermans, David Verstraeten, Benjamin Schrauwen

This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datasets for validation of our method and show that our unsupervised classifier performs competitively with supervised state-of-the-art spellers. Finally, we demonstrate the added value of our method in different experimental settings which reflect realistic usage situations of increasing difficulty and which would be difficult or impossible to tackle with existing supervised or adaptive methods.

by Seokshin Son, Ah Reum Kang, Hyun-chul Kim, Taekyoung Kwon, Juyong Park, Huy Kang Kim

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

by Tengiz Mdzinarishvili, Simon Sherman

Background

The Age–Period–Cohort (APC) analysis is aimed at estimating the following effects on disease incidence: (i) the age of the subject at the time of disease diagnosis; (ii) the time period, when the disease occurred; and (iii) the date of birth of the subject. These effects can help in evaluating the biological events leading to the disease, in estimating the influence of distinct risk factors on disease occurrence, and in the development of new strategies for disease prevention and treatment.

Methodology/Principal Findings

We developed a novel approach for estimating the APC effects on disease incidence rates in the frame of the Log-Linear Age-Period-Cohort (LLAPC) model. Since the APC effects are linearly interdependent and cannot be uniquely estimated, solving this identifiability problem requires setting four redundant parameters within a set of unknown parameters. By setting three parameters (one of the time-period and the birth-cohort effects and the corresponding age effect) to zero, we reduced this problem to the problem of determining one redundant parameter and, used as such, the effect of the time-period adjacent to the anchored time period. By varying this identification parameter, a family of estimates of the APC effects can be obtained. Using a heuristic assumption that the differences between the adjacent birth-cohort effects are small, we developed a numerical method for determining the optimal value of the identification parameter, by which a unique set of all APC effects is determined and the identifiability problem is solved.

Conclusions/Significance

We tested this approach while estimating the APC effects on lung cancer occurrence in white men and women using the SEER data, collected during 1975–2004. We showed that the LLAPC models with the corresponding unique sets of the APC effects estimated by the proposed approach fit very well with the observational data.