O manuscrito e o iconográfico em cartões-postais belicosos: da apologia cavalheiresca à contestação da Grande Guerra (1914-1918) na França

O trabalho analisa mensagens transmitidas por cartões-postais produzidos e circulados na França no contexto da Primeira Guerra Mundial (1914-1918), apresentando temática associada ao conflito. O objetivo é contrapor as mensagens iconográficas e textuais neles impressas à quelas que foram manuscritas por seus remetentes, de modo a evidenciar formas de expressão e percepções do conflito, conforme empregadas por civis e militares, em diferentes momentos de seu desenvolvimento.The paper analyzes messages conveyed by postcards produced and circulated in France during the First World War (1914-1918), with themes referring to the conflict. The intention is to compare the iconographic and textual messages printed with handwritten messages, to display forms of expression and perception of war, used by civilian and military on different occasions

Homology modeling, molecular docking, and dynamics of two alpha-methyl-D-mannoside-specific lectins from Arachis genus

The Arachis genus belongs to the Dalbergieae tribe, a group of plants that show promising potential novel lectins. Three lectins of the well-known Arachis hypogaea have already been purified, while lectins from related species are still unknown. Genomes of two closely related species, Arachis duranensis and Arachis ipaensis, were recently sequenced. Therefore, this study aimed to establish the three-dimensional structure of Arachis duranensis lectin (ADL) and Arachis ipaensis lectin (AIL) by homology modeling, test their activity against mannosides, and perform molecular dynamics (MD) simulations on these two proteins, both unligated and interacting with mannose or alpha-methyl-D-mannoside. The fold obtained for the molecular models agrees with data obtained from previous leguminous lectins, showing a conserved jelly roll motif. Docking scores indicate that these lectins have different theoretical binding energy with monosaccharides, disaccharides, and high-mannose glycans. MD simulations revealed that these proteins are alpha-methyl-D-mannoside-specific, having less stable interactions with mannose. This study thus serves as a guide for further research on lectins of the Arachis genus

QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred\u27s modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrate customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred\u27s functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred. Scientific Contribution QSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models

Update on the Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Guideline of the Brazilian Society of Cardiology-2019

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