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Common Data Models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in healthcare. CDMs harmonize data from disparate sources and eases collaborations across institutions which lead to generation of larger standardized data repositories across different entities. This Scoping Review (Sc-R) on methods used in the development of CDMs for healthcare aims to obtain a broad overview of approaches that are used in developing CDMs, i.e., Common Data Elements (CDEs) or Common Data Sets (CDS) for different disease domains on an international level. To get an overview of the state-of-the-art literature databases, namely PubMed, Web of Science, Science Direct, and Scopus are searched for five-year publications, starting from 2017, with associated keywords. The included articles will be evaluated methodically and a list of different types of methods will be created. The methods will then be categorized into groups.

Early results from an investigation into the usefulness of botanical literature to provide historical information on the distributions of plants. Based upon the case of Chenopodium vulvaria, a small weed of waste places.

A common, automated, pre-publication registration model for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank) [pdf, 650 KB] Use the reference from http://dx.doi.org/10.6084/m9.figshare.784947 *only*

Interoperability model between PLAZI and the CDM Platform.

International audience; Overcoming the problem of resistance to antiretroviral drugs (ARVs) in HIV-infected patients is a major issue in AIDS research today. Advances in genome sequencing have facilitated the identification of a growing number of individual genotypes. Hence, it is now possible to understand HIV drug resistance at the molecular level by considering the three-dimensional (3D) structural interactions between ARVs and the mutated viral proteins of patients. Therefore, identification of the critical interactions lost further to one or several HIV mutations, and consequently the modifications of other molecular factors, could be indicators to propose appropriate ARVs escaping the resistance. This paper introduces the HIV-PDI (Protein-Drug Interactions) resource designed to be a decision making tool to propose alternative ARVs against a particular mutated viral protein, and thus to provide a personalized antiretroviral treatment. The HIV-PDI was conceived to serve as an integrated resource for studying HIV drug resistance at the structural level of the protein-drug interaction, with a special emphasis on the active site of the HIV drug target. As a first step, we focus on the well documented protease and related drugs. The HIV-PDI includes clinical information on patients, resistance to given ARVs treatments, HIV proteins structures and mutations, HIV protein/ARV drugs and their 3D interactions. The HIV-PDI may be queried using multiple combinations of fields including protein, drug and treatment conditions and coupled to visualization/analysis tools of 3D Protein-Drug interactions. The HIV-PDI resource can be used in order to help understand the appearance of resistance and to promote further novel drug and treatment developments based on analyses of 3D pattern of protein-drug interactions. A web-based version of HIV-PDI is available at http://hiv-pdi.loria.fr.

International audience; The HIV-PDI resource was designed and implemented to address the problems of drug resistance with a central focus on the 3D structure of the target-drug interaction. Clinical and biological data, structural and physico-chemical information and 3D interaction data concerning the targets (HIV protease) and the drugs (ARVs) were meticulously integrated and combined with tools dedicated to study HIV mutations and their consequences on the efficacy of drugs. Here, the capabilities of the HIV-PDI resource are demonstrated for several different scenarios ranging from retrieving information associated with patients to analyzing structural data relating cognate proteins and ligands. HIV-PDI allows such diverse data to be correlated, especially data linking antiretroviral drug (ARV) resistance to a given treatment with changes in three-dimensional interactions between a drug molecule and the mutated protease. Our work is based on the assumption that ARV resistance results from a loss of affinity between the mutated HIV protease and a drug molecule due to subtle changes in the nature of the protein-ligand interaction. Therefore, a set of patients whose resistance to first line treatment was corrected by a second line treatment was selected from the HIV-PDI database for detailed study, and several queries regarding these patients are processed via its graphical user interface. Considering the protease mutations found in the selected set of patients, our retrospective analysis was able to establish in most cases that the first line treatment was not suitable, and it predicted a second line treatment which agreed perfectly with the clincian's prescription. The present study demonstrates the capabilities of HIV-PDI. We anticipate that this decision support tool will help clinicians and researchers find suitable HIV treatments for individual patients. The HIVPDI database is thereby useful as a system of data collection allowing interpretation on the basis of all available information, thus helping in possible decision-makings.