Strong completeness for a class of stochastic differential equations with irregular coefficients

We prove the strong completeness for a class of non-degenerate SDEs, whose coefficients are not necessarily uniformly elliptic nor locally Lipschitz continuous nor bounded. Moreover, for each $t$, the solution flow $F_t$ is weakly differentiable and for each $p>0$ there is a positive number $T(p)$ such that for all $t<T(p)$, the solution flow $F_t(\cdot)$ belongs to the Sobolev space W_{\loc}^{1,p}. The main tool for this is the approximation of the associated derivative flow equations. As an application a differential formula is also obtained

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well

Anti-thrombotic and anti-tumor effect of water extract of caulis of Sargentodoxa cuneata (Oliv) Rehd et Wils (Lardizabalaceae) in animal models

Purpose: To investigate the anti-thrombosis and anti-tumor effect of the water extract of the caulis of Sargentodoxa cuneata (Oliv.) Rehd. et Wils. (WCSW) in rat and mouse models.Methods: WCSW extract was prepared and the main constituents were determined by high pressure liquid chromatography (HPLC). The acute toxicity of the extract was determined in mice. Platelet aggregation in rat platelet-rich plasma (PRP) was examined to evaluate the effect of the extract on platelet function. Thereafter, the cytotoxic activity of WCSW on HL60, A549, S180 and H22 cells was determined by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. In vivo antitumor effect of WCSW was further evaluated on H22 cells transplanted in mice, while the expression of caspase-3, caspase-9, Bcl-2 and Bax proteins were assayed by Western blot analysis.Results: Protocatechuic acid, rhodiola glucoside and chlorogenic acid were identified as the main constituents of WCSW. Platelet aggregation was significantly inhibited by treatment with the extract at concentrations of 1, 5 and 10 mg/mL. WCSW also showed significant inhibitory effect on HL60, A549, S180 and H22 cells in vitro with half maximal inhibitory concentration (IC50 value of 321.9, 285.0, 130.3 and 76.1 μg/mL, respectively. Furthermore, WCSW exhibited obvious anti-tumor effect on H22 transplanted tumor in vivo. After treatment with WCSW, caspase-3, caspase-9 and Bax were significantly (p &lt; 0.05) up-regulated, whereas Bcl-2 was significantly (p &lt; 0.05) down-regulated in the tumor tissues.Conclusion: WCSW possesses significant antithrombosis and anti-tumor effect, and therefore, has the potentials to be developed into effective drugs for clinical treatment of cancer and thrombosis diseases.Keywords: Sargentodoxa cuneata, Anti-thrombosis, Anti-tumor, Platelet aggregation, Apoptosis, Caspase, Protocatechuic acid, Rhodiola glucoside, Chlorogenic aci

Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms

Background: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. Results: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. Conclusion: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application

Determinants and Outcomes of Internet Banking Adoption

This paper examines the drivers of adoption of Internet banking and the linkages among adoption drivers and outcomes (product acquisition, service activity, profitability, loyalty). We relate Internet banking adoption to customer demand for banking services, the availability of alternative channels, customers\u27 efficiency in service coproduction (“customer efficiency”), and local Internet banking penetration. We find that customers who have greater transaction demand and higher efficiency, and reside in areas with a greater density of online banking adopters, are faster to adopt online banking after controlling for time, regional, and individual characteristics. Consistent with prior work, we find that customers significantly increase their banking activity, acquire more products, and perform more transactions. These changes in behavior are not associated with short-run increases in customer profitability, but customers who adopt online banking have a lower propensity to leave the bank. Building on these observations we also find that the adoption drivers are linked to the postadoption changes in behavior or profitability. Customers who live in areas with a high branch density or high Internet banking penetration increase their product acquisition and transaction activity more than Internet banking adopters in other regions. Efficient customers and those with high service demand show greater postadoption profitability