Abstract The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients’ care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ± 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care. COVID19 COVID 19 COVID-1 urgent Patients globe studies ML. . (ML) Seventytwo Seventy two RTPCR RT PCR andor or ELISA set ROCAUC ROC AUC 080 0 80 0.8 013 13 0.13 activity ferritin iron monocytes COVID1 1 COVID- (ML 08 8 0. 01 0.1

Abstract Transposable elements are repetitive and mobile DNA segments that can be found in virtually all organisms investigated to date. Their complex structure and variable nature are particularly challenging from the genomic annotation point of view. Many softwares have been developed to automate and facilitate TEs annotation at the genomic level, but they are highly heterogeneous regarding documentation, usability and methods. In this review, we revisited the existing software for TE genomic annotation, concentrating on the most often used ones, the methodologies they apply, and usability. Building on the state of the art of TE annotation software we propose best practices and highlight the strengths and weaknesses from the available solutions. date view level documentation methods review ones apply solutions

Abstract Amazonian biodiversity is increasingly threatened due to the weakening of policies for combating deforestation, especially in Brazil. Loss of animal and plant species, many not yet known to science, is just one among many negative consequences of Amazon deforestation. Deforestation affects indigenous communities, riverside as well as urban populations, and even planetary health. Amazonia has a prominent role in regulating the Earth’s climate, with forest loss contributing to rising regional and global temperatures and intensification of extreme weather events. These climatic conditions are important drivers of emerging infectious diseases, and activities associated with deforestation contribute to the spread of disease vectors. This review presents the main impacts of Amazon deforestation on infectious-disease dynamics and public health from a One Health perspective. Because Brazil holds the largest area of Amazon rainforest, emphasis is given to the Brazilian scenario. Finally, potential solutions to mitigate deforestation and emerging infectious diseases are presented from the perspectives of researchers in different fields.