Reticuloendotheliosis virus (REV) is a retroviral pathogen capable of infecting several avian hosts and is associated with immunosuppression, anemia, proventriculitis, neoplasia, and runting–stunting syndrome. Its genome contains the three major genes, gag, pol, and env, and two flanking long terminal repeat (LTR) regions. Complete genome sequences of REV are limited in terms of geographical origin. The aim of this study was to characterize the complete genome of REV detected in Brazilian chickens with multiple viral coinfections and analyze the polymorphisms in the deduced amino acids sequences corresponding to its encoded proteins. We tested the presence and completeness of REV as well as other viral pathogens in samples from Brazilian poultry farms by qPCR. The complete genomes of two REV strains were sequenced by overlapping fragments through the dideoxy method. Phylogenetic analysis, pairwise identity matrix, polymorphism identification and protein modeling were performed along the entire genome. We detected REV in 65% (26/40) of the tested samples. Concomitant viral infections were detected in 82.5% (33/40) of the samples and in 90% (9/10) of the farms. Multiple infections included up to seven viruses. Phylogenetic analysis classified both Brazilian strains into REV subtype 3, and the pairwise comparison indicated that strains from the USA and fowlpox virus (FWPV)-related strains were the most identical. The subdomain p18 in gag, the reverse transcriptase/ribonuclease H in pol, and the surface (SU) in the env protein were the most polymorphic in genomic comparisons. The relevant motifs for each protein were highly conserved, with fewer polymorphisms in the fusion peptide, immunosuppression domain, and disulfide bonds on the surface (SU) and transmembrane (TM) of env. This is the first study to include complete genomes of REV in Brazil and South America detected in farms with multiple viral coinfections. Our findings suggest an involvement of REV as an immunosuppressor and active agent in the emergence and progression of multiple infectious diseases. We also found a possible etiological relationship between Brazilian strains and the USA and FWPV recombinant strains. This information highlights the need for epidemiological vigilance regarding REV in association with another pathogens.

Brazil is the sixth largest emitter of GHG, with land use change and agriculture being the main source of these emissions. The recent expansion of agriculture in Brazil has been occurring mainly in the MATOPIBA region, a territorial division inserted on Cerrado. Clustering methods are useful for driving hyper-local GHG emission reduction strategies, but they have yet to be applied at the municipal level, nor from the emissions of various economic sectors. In order to contribute to the identification of the most critical areas in relation to GHG emissions for MATOPIBA, this study proposes an approach of municipal clustering according to the percentage contribution of Agriculture, Land Use Change, Energy and Waste sectors in total emissions. The clustering was performed with the k-means algorithm, using the elbow method and the silhouette score to define the number of clusters. In addition, statistical and geostatistical analyses were conducted to assess the consistency and spatial autocorrelation of the groups formed. The approach was able to generate six clusters with distinct characteristics, showing the heterogeneous profile of GHG emissions from MATOPIBA. At the same time, the clustering of similar municipalities can help in making decisions about the best pro-environmental measures to reduce/remove GHG to contain global warming.

Dance has the potential to improve people's quality of life, as well as assist to decrease depression and anxiety. However, the lack of technologies capable of exploring alternative senses of hearing limits music and dance's beneficial effects on listeners. In order to find a model capable of being implemented in accessible devices, this work evaluated the use of a model based on neural networks to estimate the forro music bar length. Model variations were trained for seven datasets composed of mixes of music samples without noise, with real noise and with white noise. For each dataset, the best variation was selected and these were evaluated for the same real noise samples. The model variations that were presented to samples with real noise in the training estimated the bar duration with an average percentage error of less than 7% in the test step, being significantly better the model trained only with real sample. The evaluated model was able to estimate the length of the forro music bar length, even in real scenarios, as long as it was presented in this scenario during training. Increased database diversity and the use of data augmentation techniques can lead to improvements in the generalizability of the model. The simplicity of the evaluated model and its ability to learn when properly trained, indicate its potential to be used, in real time, on a mobile device to pass the rhythm of forro music to deaf and hard of hearing (D/HH) people.