In vitro and in vivo studies of the trypanocidal properties of WRR-483 against Trypanosoma cruzi.

BackgroundCruzain, the major cysteine protease of Trypanosoma cruzi, is an essential enzyme for the parasite life cycle and has been validated as a viable target to treat Chagas' disease. As a proof-of-concept, K11777, a potent inhibitor of cruzain, was found to effectively eliminate T. cruzi infection and is currently a clinical candidate for treatment of Chagas' disease.Methodology/principal findingsWRR-483, an analog of K11777, was synthesized and evaluated as an inhibitor of cruzain and against T. cruzi proliferation in cell culture. This compound demonstrates good potency against cruzain with sensitivity to pH conditions and high efficacy in the cell culture assay. Furthermore, WRR-483 also eradicates parasite infection in a mouse model of acute Chagas' disease. To determine the atomic-level details of the inhibitor interacting with cruzain, a 1.5 A crystal structure of the protease in complex with WRR-483 was solved. The structure illustrates that WRR-483 binds covalently to the active site cysteine of the protease in a similar manner as other vinyl sulfone-based inhibitors. Details of the critical interactions within the specificity binding pocket are also reported.ConclusionsWe demonstrate that WRR-483 is an effective cysteine protease inhibitor with trypanocidal activity in cell culture and animal model with comparable efficacy to K11777. Crystallographic evidence confirms that the mode of action is by targeting the active site of cruzain. Taken together, these results suggest that WRR-483 has potential to be developed as a treatment for Chagas' disease

Intelligent matching for public internet web services ? towards semi-automatic internet services mashup

In this paper, we propose an Internet public Web service matching approach that paves the way for(semi-)automatic service mashup. We will first provide the overview of the solution, which requires a detailed review of two fundamental models ? schema/graph matching and semantic space. Based on the conceptual model and the literature study, the complete service matching approach is then provided with four essential steps ? semantic space, parameter tree, similarity measures, and WSDL operation matching. The system demonstration that proves the concept proposed in this approach is finally presented. The solution has the potential to facilitate the Internet services mashup

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches

Amorphous metallic alloys: a new advance in thin-film diffusion barriers for copper metallization

Copper, which has a lower electrical resistivity and a higher resistance to electromigration than aluminum, is currently being evaluated for ULSI applications as a replacement for aluminum. Drawbacks to the use of copper include its strong tendency to oxidation, a high mobility in metals and semiconductors, and a high reactivity with silicon at temperatures as low as 200°C. To overcome these problems, very effective diffusion barriers need to be developed. These barriers should have a low diffusivity for copper, a high thermal stability, and should lack a driving force for chemical reactions with Cu, silicon or silicides. Unlike aluminum, copper does not form stable intermetallic compounds with the transition metals of the V and Cr groups, and the mutual solid solubilities of these metals with Cu are low, so that these metals would seem th be a logical choice for barrier applications. It has long been known, however, that these arguments are misleading[1]. Previous studies have indeed shown Cu diffuses through grain boundaries and defects in a tantalum layer and inth silicon at a relatively low temperature (450°C) causing a failure of devices[2,3]. The effectiveness of non-reactive and insoluble tantalum barriers can be improved by adding impurities like oxygen or nitrogen th stuff grain boundaries of the material in order th suppress fast grain boundary diffusion[4]. It is difficult, however to reproducibly improve the effectiveness of barriers by adjusting the level of impurities. Since amorphous alloys lack grain boundaries that can act as fast diffusion paths, they should offer an improved alternative for effective barriers [5-71. In this paper we report on the properties and diffusion barrier performance of amorphous tantalum and tungsten silicides and tantalum-silicon-nitrogen ternary alloys [3,81 for Cu metallizations

Response time for cloud computing providers

Cloud services are becoming popular in terms of distributed technology because they allow cloud users to rent well-specified resources of computing, network, and storage infrastructure. Users pay for their use of services without needing to spend massive amounts for integration, maintenance, or management of the IT infrastructure. This creates the need for a reliable measurement methodology of the scalability for this type of new paradigm of services. In this paper, we develop performance metrics to measure and compare the scalability of the resources of virtualization on the cloud data centres. First, we discuss the need for a reliable method to compare the performance of cloud services among a number of various services being offered. Second, we develop a different type of metrics and propose a suitable methodology to measure the scalability using these types of metrics. We focus on the visualization resources such as CPU, storage disk, and network infrastructure. Finally, we compare well-known cloud providers using the proposed approach and conclude the recommendations. This type of research will help cloud consumers, before signing any official contract to use the desired services, to ascertain the ability and capacity of the cloud providers to deliver a particular service

Assembly and architecture of the EBV B cell entry triggering complex.

Epstein-Barr Virus (EBV) is an enveloped double-stranded DNA virus of the gammaherpesvirinae sub-family that predominantly infects humans through epithelial cells and B cells. Three EBV glycoproteins, gH, gL and gp42, form a complex that targets EBV infection of B cells. Human leukocyte antigen (HLA) class II molecules expressed on B cells serve as the receptor for gp42, triggering membrane fusion and virus entry. The mechanistic role of gHgL in herpesvirus entry has been largely unresolved, but it is thought to regulate the activation of the virally-encoded gB protein, which acts as the primary fusogen. Here we study the assembly and function of the reconstituted B cell entry complex comprised of gHgL, gp42 and HLA class II. The structure from negative-stain electron microscopy provides a detailed snapshot of an intermediate state in EBV entry and highlights the potential for the triggering complex to bring the two membrane bilayers into proximity. Furthermore, gHgL interacts with a previously identified, functionally important hydrophobic pocket on gp42, defining the overall architecture of the complex and playing a critical role in membrane fusion activation. We propose a macroscopic model of the initiating events in EBV B cell fusion centered on the formation of the triggering complex in the context of both viral and host membranes. This model suggests how the triggering complex may bridge the two membrane bilayers, orienting critical regions of the N- and C- terminal ends of gHgL to promote the activation of gB and efficient membrane fusion

Using Case Description Information to Reduce Sensitivity to Bias for the Attributable Fraction Among the Exposed

The attributable fraction among the exposed (\textbf{AF}$_e$), also known as the attributable risk or excess fraction among the exposed, is the proportion of disease cases among the exposed that could be avoided by eliminating the exposure. Understanding the \textbf{AF}$_e$ for different exposures helps guide public health interventions. The conventional approach to inference for the \textbf{AF}$_e$ assumes no unmeasured confounding and could be sensitive to hidden bias from unobserved covariates. In this paper, we propose a new approach to reduce sensitivity to hidden bias for conducting statistical inference on the \textbf{AF}$_e$ by leveraging case description information. Case description information is information that describes the case, e.g., the subtype of cancer. The exposure may have more of an effect on some types of cases than other types. We explore how leveraging case description information can reduce sensitivity to bias from unmeasured confounding through an asymptotic tool, design sensitivity, and simulation studies. We allow for the possibility that leveraging case definition information may introduce additional selection bias through an additional sensitivity parameter. The proposed methodology is illustrated by re-examining alcohol consumption and the risk of postmenopausal invasive breast cancer using case description information on the subtype of cancer (hormone-sensitive or insensitive) using data from the Women's Health Initiative (WHI) Observational Study (OS).Comment: 30 pages, 8 tables, 1 figur