Peroneal tendonitis

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of peroneal tendonitis

Symmetrized mean-field description of magnetic instabilities in k-(BEDT-TTF)_2Cu[N(CN)]_2 Y salts

We present a novel and convenient mean-field method, and apply it to study the metallic/antiferromagnetic interface of k-(BEDT-TTF)_2Cu[N(CN)]_2 Y organic superconductors (BEDT_TTF is bis-ethylen-dithio-tetrathiafulvalene, Y=Cl, Br). The method, which fully exploits the crystal symmetry, allows one to obtain the mean-field solution of the 2D Hubbard model for very large lattices, up to 6x10^5 sites, yielding a reliable description of the phase boundary in a wide region of the parameter space. The metal/antiferromagnet transtion appears to be second order, except for a narrow region of the parameter space, where the transition is very sharp and possibly first order. The cohexistence of metallic and antiferromagnetic properties is only observed for the transient state in the case of smooth second order transitions. The relevance of the present resaults to the complex experimental behavior of centrosymmetric k-phase BEDT-TTF salts is discussed.Comment: 9 pages in PS format, 7 figures (included in PS), 1 tabl

A Proposed Cross Platform Privacy and Security Framework for Supply Chain Information Sharing

Information sharing has become eminent to supply chain management, as it allows supply chain partners to collaborate more closely. However, currently supply chain partners are often on disjoint information platforms, which prevent them from effectively sharing critical supply chain information. One of the main barriers of information sharing is revealing confidential information to unintended parties and thus the disclosure of privacy. Therefore the information sharing needs and characteristics of a supply chain has been analyzed and subsequently a cross platform privacy and security framework to allow safe information sharing has been proposed

Extending Asia Pacific bioinformatics into new realms in the "-omics" era

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation dating back to 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 7-11, 2009 at Biopolis, Singapore. Besides bringing together scientists from the field of bioinformatics in this region, InCoB has actively engaged clinicians and researchers from the area of systems biology, to facilitate greater synergy between these two groups. InCoB2009 followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India), Hong Kong and Taipei (Taiwan), with InCoB2010 scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010. The Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and symposia on Clinical Bioinformatics (CBAS), the Singapore Symposium on Computational Biology (SYMBIO) and training tutorials were scheduled prior to the scientific meeting, and provided ample opportunity for in-depth learning and special interest meetings for educators, clinicians and students. We provide a brief overview of the peer-reviewed bioinformatics manuscripts accepted for publication in this supplement, grouped into thematic areas. In order to facilitate scientific reproducibility and accountability, we have, for the first time, introduced minimum information criteria for our pubilcations, including compliance to a Minimum Information about a Bioinformatics Investigation (MIABi). As the regional research expertise in bioinformatics matures, we have delineated a minimum set of bioinformatics skills required for addressing the computational challenges of the "-omics" era

The oligopeptide ABC transporter OppA4 negatively regulates the virulence factor OspC production of the Lyme disease pathogen

Borrelia burgdorferi sensu lato, the agent of Lyme disease, exists in nature through a complex enzootic life cycle that involves both ticks and mammals. The B. burgdorferi genome encodes five Oligopeptide ABC transporters (Opp) that are predicted to be involve in transport of various nutrients. Previously, it was reported that OppA5 is important for the optimal production of OspC, a major virulence factor of B. burgdorferi. In this study, possible role of another Oligopeptide ABC transporter, OppA4 in ospC expression was investigated by construction of an oppA4 deletion mutant and the complemented strain. Inactivation of oppA4 resulted an increased production of OspC, suggesting that OppA4 has a negative impact on ospC expression. Expression of ospC is controlled by Rrp2-RpoN-RpoS, the central pathway essential for mammal infection. We showed that increased ospC expression in the oppA4 mutant was due to an increased rpoS expression. We then further investigated how OppA4 negatively regulates this pathway. Two regulators, BosR and BadR, are known to positively and negatively, respectively, regulate the Rrp2-RpoN-RpoS pathway. We found that deletion of oppA4 resulted in an increased level of BosR. Previous reports showed that bosR is mainly regulated at the post-transcriptional level by other factors. However, OppA4 appears to negatively regulate bosR expression at the transcriptional level. The finding of OppA4 involved in regulation of the Rrp2-RpoN-RpoS pathway further reinforces the importance of nutritional virulence to the enzootic cycle of B. burgdorferi

Neural Network iLQR: A New Reinforcement Learning Architecture

As a notable machine learning paradigm, the research efforts in the context of reinforcement learning have certainly progressed leaps and bounds. When compared with reinforcement learning methods with the given system model, the methodology of the reinforcement learning architecture based on the unknown model generally exhibits significantly broader universality and applicability. In this work, a new reinforcement learning architecture is developed and presented without the requirement of any prior knowledge of the system model, which is termed as an approach of a "neural network iterative linear quadratic regulator (NNiLQR)". Depending solely on measurement data, this method yields a completely new non-parametric routine for the establishment of the optimal policy (without the necessity of system modeling) through iterative refinements of the neural network system. Rather importantly, this approach significantly outperforms the classical iterative linear quadratic regulator (iLQR) method in terms of the given objective function because of the innovative utilization of further exploration in the methodology. As clearly indicated from the results attained in two illustrative examples, these significant merits of the NNiLQR method are demonstrated rather evidently.Comment: 13 pages, 9 figure