Interactions of fully nonlinear solitary wave with a freely floating vertical cylinder

Fully nonlinear numerical interaction of a transient wave with a three dimensional structure has been analysed based on a higher-order boundary element method (BEM). The BEM mesh on the free surface is generated through a combination of the structured and unstructured meshes. Through some auxiliary functions, the mutual dependence of fluid/structure motions is decoupled, which allows the body acceleration to be obtained without the knowledge of the pressure distribution. The solitary wave is used as the case study for the transient wave. It is obtained by the third order theory and the fully nonlinear theory. The accuracy of the present numerical model is verified through the steady propagation of a solitary wave and comparison with the published results for solitary wave interaction with a vertical wall. Simulations are then made to study solitary wave interaction with a truncated cylinder. Numerical results are provided for motions, forces and run-ups on the cylinder and comparison between results for the fixed cylinder and the freely floating cylinder is also made

Global entrainment of transcriptional systems to periodic inputs

This paper addresses the problem of giving conditions for transcriptional systems to be globally entrained to external periodic inputs. By using contraction theory, a powerful tool from dynamical systems theory, it is shown that certain systems driven by external periodic signals have the property that all solutions converge to a fixed limit cycle. General results are proved, and the properties are verified in the specific case of some models of transcriptional systems. The basic mathematical results needed from contraction theory are proved in the paper, making it self-contained

European Sea Bass (Dicentrarchus labrax) immune status and disease resistance are impaired by arginine dietary supplementation

Infectious diseases and fish feeds management are probably the major expenses in the aquaculture business. Hence, it is a priority to define sustainable strategies which simultaneously avoid therapeutic procedures and reinforce fish immunity. Currently, one preferred approach is the use of immunostimulants which can be supplemented to the fish diets. Arginine is a versatile amino acid with important mechanisms closely related to the immune response. Aiming at finding out how arginine affects the innate immune status or improve disease resistance of European seabass (Dicentrarchus labrax) against vibriosis, fish were fed two arginine-supplemented diets (1% and 2% arginine supplementation). A third diet meeting arginine requirement level for seabass served as control diet. Following 15 or 29 days of feeding, fish were sampled for blood, spleen and gut to assess cell-mediated immune parameters and immune-related gene expression. At the same time, fish from each dietary group were challenged against Vibrio anguillarum and survival was monitored. Cell-mediated immune parameters such as the extracellular superoxide and nitric oxide decreased in fish fed arginine-supplemented diets. Interleukins and immune-cell marker transcripts were down-regulated by the highest supplementation level. Disease resistance data were in accordance with a generally depressed immune status, with increased susceptibility to vibriosis in fish fed arginine supplemented diets. Altogether, these results suggest a general inhibitory effect of arginine on the immune defences and disease resistance of European seabass. Still, further research will certainly clarify arginine immunomodulation pathways thereby allowing the validation of its potential as a prophylactic strategy.European Union's Seventh Framework Programme AQUAEXCEL (Aquaculture Infrastructures for Excellence in European Fish Research) [262336]; AQUAIMPROV [NORTE-07-0124-FEDER-000038]; North Portugal Regional Operational Programme (ON. 2 - O Novo Norte) , under the National Strategic Reference Framework, through the European Regional Development Fund; North Portugal Regional Operational Programme (ON. 2 - O Novo Norte), under the National Strategic Reference Framework through the COMPETE - Operational Competitiveness Programme; Fundacao para a Ciencia e Tecnologia; Fundacao para a Ciencia e Tecnologia [SFRH/BD/89457/2012, SFRH/BPD/77210/2011]; Generalitat Valenciana through the project REVIDPAQUA [ISIC/2012/003]; [PEst-C/MAR/LA0015/2013]; [UID/Multi/04423/2013]info:eu-repo/semantics/publishedVersio

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

Geochemistry of soil gas in the seismic fault zone produced by the Wenchuan Ms 8.0 earthquake, southwestern China

The spatio-temporal variations of soil gas in the seismic fault zone produced by the 12 May 2008 Wenchuan Ms 8.0 earthquake were investigated based on the field measurements of soil gas concentrations after the main shock. Concentrations of He, H2, CO2, CH4, O2, N2, Rn, and Hg in soil gas were measured in the field at eight short profiles across the seismic rupture zone in June and December 2008 and July 2009. Soil-gas concentrations of more than 800 sampling sites were obtained. The data showed that the magnitudes of the He and H2 anomalies of three surveys declined significantly with decreasing strength of the aftershocks with time. The maximum concentrations of He and H2 (40 and 279.4 ppm, respectively) were found in three replicates at the south part of the rupture zone close to the epicenter. The spatio-temporal variations of CO2, Rn, and Hg concentrations differed obviously between the north and south parts of the fault zone. The maximum He and H2 concentrations in Jun 2008 occurred near the parts of the rupture zone where vertical displacements were larger. The anomalies of He, H2, CO2, Rn, and Hg concentrations could be related to the variation in the regional stress field and the aftershock activity

Predicting protein-protein interface residues using local surface structural similarity

<p>Abstract</p> <p>Background</p> <p>Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce <it>PrISE</it>, a family of local structural similarity-based computational methods for predicting protein-protein interface residues.</p> <p>Results</p> <p>We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The <it>PrISE </it>family of interface prediction methods uses a representation of structural elements that captures the atomic composition and accessible surface area of the residues that make up each structural element. Each of the members of the <it>PrISE </it>methods identifies for each structural element in the query protein, a collection of <it>similar </it>structural elements in its repository of structural elements and weights them according to their similarity with the structural element of the query protein. <it>PrISE_L</it>relies on the similarity between structural elements (i.e. local structural similarity). <it>PrISE_G</it>relies on the similarity between protein surfaces (i.e. general structural similarity). <it>PrISE_C</it>, combines local structural similarity and general structural similarity to predict interface residues. These predictors label the central residue of a structural element in a query protein as an interface residue if a weighted majority of the structural elements that are similar to it are interface residues, and as a non-interface residue otherwise. The results of our experiments using three representative benchmark datasets show that the <it>PrISE_C</it>outperforms <it>PrISE_L</it>and <it>PrISE_G</it>; and that <it>PrISE_C</it>is highly competitive with state-of-the-art structure-based methods for predicting protein-protein interface residues. Our comparison of <it>PrISE_C</it>with <it>PredUs</it>, a recently developed method for predicting interface residues of a query protein based on the known interface residues of its (global) structural homologs, shows that performance superior or comparable to that of <it>PredUs </it>can be obtained using only local surface structural similarity. <it>PrISE_C</it>is available as a Web server at <url>http://prise.cs.iastate.edu/</url></p> <p>Conclusions</p> <p>Local surface structural similarity based methods offer a simple, efficient, and effective approach to predict protein-protein interface residues.</p

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

HER2 Oncogenic Function Escapes EGFR Tyrosine Kinase Inhibitors via Activation of Alternative HER Receptors in Breast Cancer Cells

BACKGROUND: The response rate to EGFR tyrosine kinase inhibitors (TKIs) may be poor and unpredictable in cancer patients with EGFR expression itself being an inadequate response indicator. There is limited understanding of the mechanisms underlying this resistance. Furthermore, although TKIs suppress the growth of HER2-overexpressing breast tumor cells, they do not fully inhibit HER2 oncogenic function at physiological doses. METHODOLOGY AND PRINCIPAL FINDINGS: Here we have provided a molecular mechanism of how HER2 oncogenic function escapes TKIs' inhibition via alternative HER receptor activation as a result of autocrine ligand release. Using both Förster Resonance Energy Transfer (FRET) which monitors in situ HER receptor phosphorylation as well as classical biochemical analysis, we have shown that the specific tyrosine kinase inhibitors (TKIs) of EGFR, AG1478 and Iressa (Gefitinib) decreased EGFR and HER3 phosphorylation through the inhibition of EGFR/HER3 dimerization. Consequent to this, we demonstrate that cleavage of HER4 and dimerization of HER4/HER2 occur together with reactivation of HER3 via HER2/HER3, leading to persistent HER2 phosphorylation in the now resistant, surviving cells. These drug treatment-induced processes were found to be mediated by the release of ligands including heregulin and betacellulin that activate HER3 and HER4 via HER2. Whereas an anti-betacellulin antibody in combination with Iressa increased the anti-proliferative effect in resistant cells, ligands such as heregulin and betacellulin rendered sensitive SKBR3 cells resistant to Iressa. CONCLUSIONS AND SIGNIFICANCE: These results demonstrate the role of drug-induced autocrine events leading to the activation of alternative HER receptors in maintaining HER2 phosphorylation and in mediating resistance to EGFR tyrosine kinase inhibitors (TKIs) in breast cancer cells, and hence specify treatment opportunities to overcome resistance in patients