Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too

Co- and multimorbidity patterns in primary care based on episodes of care: results from the German CONTENT project

Contains fulltext : 69171.pdf (publisher's version ) (Open Access)BACKGROUND: Due to technological progress and improvements in medical care and health policy the average age of patients in primary care is continuously growing. In equal measure, an increasing proportion of mostly elderly primary care patients presents with multiple coexisting medical conditions. To properly assess the current situation of co- and multimorbidity, valid scientific data based on an appropriate data structure are indispensable. CONTENT (CONTinuous morbidity registration Epidemiologic NeTwork) is an ambitious project in Germany to establish a system for adequate record keeping and analysis in primary care based on episodes of care. An episode is defined as health problem from its first presentation by a patient to a doctor until the completion of the last encounter for it. The study aims to describe co- and multimorbidity as well as health care utilization based on episodes of care for the study population of the first participating general practices. METHODS: The analyses were based on a total of 39,699 patients in a yearly contact group (YCG) out of 17 general practices in Germany for which data entry based on episodes of care using the International Classification of Primary Care (ICPC) was performed between 1.1.2006 and 31.12.2006. In order to model the relationship between the explanatory variables (age, gender, number of chronic conditions) and the response variables of interest (number of different prescriptions, number of referrals, number of encounters) that were applied to measure health care utilization, we used multiple linear regression. RESULTS: In comparison to gender, patients' age had a manifestly stronger impact on the number of different prescriptions, the number of referrals and number of encounters. In comparison to age (beta = 0.043, p < 0.0001), multimorbidity measured by the number of patients' chronic conditions (beta = 0.51, p < 0.0001) had a manifestly stronger impact the number of encounters for the observation period. Moreover, we could observe that the number of patients' chronic conditions had a significant impact on the number of different prescriptions (beta = 0.226, p < 0.0001) as well as on the number of referrals (beta = 0.3, p < 0.0001). CONCLUSION: Documentation in primary care on the basis of episodes of care facilitates an insight to concurrently existing health problems and related medical procedures. Therefore, the resulting data provide a basis to obtain co- and multimorbidity patterns and corresponding health care utilization issues in order to understand the particular complex needs caused by multimorbidity

Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p

Properties and identification of antibiotic drug targets

<p>Abstract</p> <p>Background</p> <p>We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from <it>E. coli </it>only and 4243 non-drug targets from <it>E. coli </it>to identify differences in their properties and to predict new potential drug targets.</p> <p>Results</p> <p>When compared to non-targets, bacterial antibiotic targets tend to be long, have high β-sheet and low α-helix contents, are polar, are found in the cytoplasm rather than in membranes, and are usually enzymes, with ligases particularly favoured. Sequence features were used to build a support vector machine model for <it>E. coli </it>proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. We identified 319 proteins (7%) in the non-target set that have target-like properties, many of which have unknown function. 63 of these proteins have significant and undesirable similarity to a human protein, leaving 256 target like proteins that are not present in humans.</p> <p>Conclusions</p> <p>We suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. In particular, 64 are essential genes where the cell is not able to recover from a random insertion disruption.</p

Common characteristics of open source software development and applicability for drug discovery: a systematic review

<p>Abstract</p> <p>Background</p> <p>Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery.</p> <p>Methods</p> <p>A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project.</p> <p>Results</p> <p>Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined.</p> <p>Conclusions</p> <p>We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.</p

Identification of Sare0718 As an Alanine-Activating Adenylation Domain in Marine Actinomycete Salinispora arenicola CNS-205

BACKGROUND: Amino acid adenylation domains (A domains) are critical enzymes that dictate the identity of the amino acid building blocks to be incorporated during nonribosomal peptide (NRP) biosynthesis. NRPs represent a large group of valuable natural products that are widely applied in medicine, agriculture, and biochemical research. Salinispora arenicola CNS-205 is a representative strain of the first discovered obligate marine actinomycete genus, whose genome harbors a large number of cryptic secondary metabolite gene clusters. METHODOLOGY/PRINCIPAL FINDINGS: In order to investigate cryptic NRP-related metabolites in S. arenicola CNS-205, we cloned and identified the putative gene sare0718 annotated "amino acid adenylation domain". Firstly, the general features and possible functions of sare0718 were predicted by bioinformatics analysis, which suggested that Sare0718 is a soluble protein with an AMP-binding domain contained in the sequence and its cognate substrate is L-Val. Then, a GST-tagged fusion protein was expressed and purified to further explore the exact adenylation activity of Sare0718 in vitro. By a newly mentioned nonradioactive malachite green colorimetric assay, we found that L-Ala but not L-Val is the actual activated amino acid substrate and the basic kinetic parameters of Sare0718 for it are K(m) = 0.1164±0.0159 (mM), V(max) = 3.1484±0.1278 (µM/min), k(cat) = 12.5936±0.5112 (min(-1)). CONCLUSIONS/SIGNIFICANCE: By revealing the biochemical role of sare0718 gene, we identified an alanine-activating adenylation domain in marine actinomycete Salinispora arenicola CNS-205, which would provide useful information for next isolation and function elucidation of the whole cryptic nonribosomal peptide synthetase (NRPS)-related gene cluster covering Sare0718. And meanwhile, this work also enriched the biochemical data of A domain substrate specificity in newly discovered marine actinomycete NRPS system, which bioinformatics prediction will largely depend on

Genomic organization and evolution of the Atlantic salmon hemoglobin repertoire

<p>Abstract</p> <p>Background</p> <p>The genomes of salmonids are considered pseudo-tetraploid undergoing reversion to a stable diploid state. Given the genome duplication and extensive biological data available for salmonids, they are excellent model organisms for studying comparative genomics, evolutionary processes, fates of duplicated genes and the genetic and physiological processes associated with complex behavioral phenotypes. The evolution of the tetrapod hemoglobin genes is well studied; however, little is known about the genomic organization and evolution of teleost hemoglobin genes, particularly those of salmonids. The Atlantic salmon serves as a representative salmonid species for genomics studies. Given the well documented role of hemoglobin in adaptation to varied environmental conditions as well as its use as a model protein for evolutionary analyses, an understanding of the genomic structure and organization of the Atlantic salmon α and β hemoglobin genes is of great interest.</p> <p>Results</p> <p>We identified four bacterial artificial chromosomes (BACs) comprising two hemoglobin gene clusters spanning the entire α and β hemoglobin gene repertoire of the Atlantic salmon genome. Their chromosomal locations were established using fluorescence <it>in situ </it>hybridization (FISH) analysis and linkage mapping, demonstrating that the two clusters are located on separate chromosomes. The BACs were sequenced and assembled into scaffolds, which were annotated for putatively functional and pseudogenized hemoglobin-like genes. This revealed that the tail-to-tail organization and alternating pattern of the α and β hemoglobin genes are well conserved in both clusters, as well as that the Atlantic salmon genome houses substantially more hemoglobin genes, including non-Bohr β globin genes, than the genomes of other teleosts that have been sequenced.</p> <p>Conclusions</p> <p>We suggest that the most parsimonious evolutionary path leading to the present organization of the Atlantic salmon hemoglobin genes involves the loss of a single hemoglobin gene cluster after the whole genome duplication (WGD) at the base of the teleost radiation but prior to the salmonid-specific WGD, which then produced the duplicated copies seen today. We also propose that the relatively high number of hemoglobin genes as well as the presence of non-Bohr β hemoglobin genes may be due to the dynamic life history of salmon and the diverse environmental conditions that the species encounters.</p> <p>Data deposition: BACs S0155C07 and S0079J05 (fps135): GenBank <ext-link ext-link-id="GQ898924" ext-link-type="gen">GQ898924</ext-link>; BACs S0055H05 and S0014B03 (fps1046): GenBank <ext-link ext-link-id="GQ898925" ext-link-type="gen">GQ898925</ext-link></p