Broussard Santos (tennisjury47)

001) and lower white blood cell (P less then .001) and lymphocyte (P less then .001) values, higher occurrence of apthae (P = .008), and a stronger association with autoimmune diseases/markers (P = .001) than LL-Np, thus suggesting a more pronounced autoimmune signature for LO-Np. A next-generation sequencing panel applied in a small subgroup of LO-Np and LL-Np patients identified variants related to immune dysregulations. Overall, these findings indicate that there are important differences among pAN LL-Np and LO-Np. Forms rising after 3 years of age, with low tendency to resolution, require tight monitoring and extensive immune investigations aimed to early identify underlying immunologic disease.Animal models are widely employed in basic research to test mechanistic hypotheses in a complex biological environment as well as to evaluate the therapeutic potential of candidate compounds in preclinical settings. Rodents, and in particular mice, represent the most common in vivo models for their small size, short lifespan and possibility to manipulate their genome. Over time, a typical laboratory will develop a substantial number of inbred strains and transgenic mouse lines, requiring a substantial effort, in both logistic and economic terms, to maintain an animal colony for research purposes and to safeguard the integrity of results. To meet this need, here we present TopoDB, a robust and extensible web-based platform for the rational management of laboratory animals. TopoDB allows an easy tracking of individual animals within the colony and breeding protocols as well as the convenient storage of both genetic and phenotypic data generated in the different experiments. Altogether, these features facilitate and enhance the design of in vivo research, thus reducing the number of necessary animals and the housing costs. In summary, TopoDB represents a novel valuable tool in modern biomedical research. Database URL https//github.com/UCSF-MS-DCC/TopoDB.Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by Int