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Lista Nacional de Espécies Ameaçadas de Extinção: atualizações para os anfíbios brasileiros

The fungal kingdom is too large to be discovered exclusively by classical genetics. The access to omics data opens a new opportunity to study the diversity within the fungal kingdom and how adaptation to new environments shapes fungal metabolism. Genomes are the foundation of modern science but their quality is crucial when analysing omics data. In this study, we demonstrate how one gold-standard genome can improve functional prediction across closely related species to be able to identify key enzymes, reactions and pathways with the focus on primary carbon metabolism.Based on this approach we identified alternative genes encoding various steps of the different sugar catabolic pathways, and as such provided leads for functional studies into this topic. We also revealed significant diversity with respect to genome content, although this did not always correlate to the ability of the species to use the corresponding sugar as a carbon source. Key words: Aspergillus, Genomic diversity, Gold standard genome, Sugar catabolism

Background.Psychiatric nosology lacks objective biological foundation, as well as typical biomarkers for diagnoses, which raises questions about its validity. The problem is particularly evident concerning Attention Deficit/Hyperactivity Disorder (ADHD). The objective of this study is to estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically valid for ADHD diagnosis using a multivariate analysis for small samples from a large dataset concerning neurophysiological, behavioral, and psychological variables.Methods:Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10-13 years, were examined using the Attentional Network Test (ANT) with records of event-related potentials (ERPs). From 815 variables, a reduced number of latent variables (LVs) were extracted with a clustering method, for further reclassification of subjects using the k-means method. This approach allowed multivariate analysis to be applied to a significantly larger number of variables than the number of cases (E. Wigneau et al., 2003, 2015)Results:From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central channels, only seven subjects were miss-reclassified by the LVs. An estimated specificity of 75.00% and sensitivity of 89.47% for DSM were found in the reclassification. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”.Discussion:Results showed that CLV is a useful method for diagnostic classification using a large dataset of small samples, suggesting the biological validity of DSM for ADHD diagnosis, in accordance to alterations in fronto-striatal networks previously related to ADHD.