Multitask and transfer learning for cardiac abnormality detection in heart sounds

We present a deep learning model for the automatic detection of murmurs and other cardiac abnormalities from the analysis of digital recordings of cardiac auscultations. This approach was developed in the context of the George B. Moody PhysioNet Challenge 2022. More precisely, we consider multi-objective neural networks, with several Transformer blocks at their core, trained to perform 3 distinct tasks simultaneously: murmur detection, outcome classification and audio signal segmentation. We also perform pre-training with the 2016’s Challenge data. We entered the challenge under the team name matLisboa. Our results on the hidden test dataset were: Murmur score (weighted accuracy): 0.735 (ranked 15th). Outcomes score (cost): 12593 (ranked 16th).info:eu-repo/semantics/publishedVersio

Ganho de peso bovino em resteva de arroz irrigado na safra de 2009/2010 na Embrapa Pecuária Sul.

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Analysis of the economic viability from an integrated system of rice and beef cattle in the Pampa Biome of Rio Grande do Sul.

The environmental conditions of the Pampa Biome are favorable to the degradation of soil, especially when grain crops are introduced in this region, traditionally exploited with beef cattle systems

Raising Brangus steers on natural pasture developed after irrigated rice crop in the Pampa biome of Rio Grande do Sul state, Brazil.

The traditional systems of exploitation with beef cattle in the Pampa biome of Rio Grande do Sul presents historically low rates of productivity and profitability due, mainly, to the inadequate management of natural pastures

Microscopic mechanism for mechanical polishing of diamond (110) surfaces

Mechanically induced degradation of diamond, as occurs during polishing, is studied using total--energy pseudopotential calculations. The strong asymmetry in the rate of polishing between different directions on the diamond (110) surface is explained in terms of an atomistic mechanism for nano--groove formation. The post--polishing surface morphology and the nature of the polishing residue predicted by this mechanism are consistent with experimental evidence.Comment: 4 pages, 5 figure

What Physical Education Teachers Know About Asthma: Impact of a Training Course

info:eu-repo/semantics/publishedVersio

Instruções práticas para produção de composto orgânico em pequenas propriedades.

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The Anopheles gambiae transcriptome – a turning point for malaria control

Mosquitoes are important vectors of several pathogens and thereby contribute to the spread of diseases, with social, economic and public health impacts. Amongst the approximately 450 species of Anopheles, about 60 are recognized as vectors of human malaria, the most important parasitic disease. In Africa, Anopheles gambiae is the main malaria vector mosquito. Current malaria control strategies are largely focused on drugs and vector control measures such as insecticides and bed-nets. Improvement of current, and the development of new, mosquito-targeted malaria control methods rely on a better understanding of mosquito vector biology. An organism's transcriptome is a reflection of its physiological state and transcriptomic analyses of different conditions that are relevant to mosquito vector competence can therefore yield important information. Transcriptomic analyses have contributed significant information on processes such as blood-feeding parasite–vector interaction, insecticide resistance, and tissue- and stage-specific gene regulation, thereby facilitating the path towards the development of new malaria control methods. Here, we discuss the main applications of transcriptomic analyses in An. gambiae that have led to a better understanding of mosquito vector competence.publishersversionpublishe