Thales Francisco Mota Carvalho

PurposeWe aim to provide a robust dataset for training automated systems to detect tuberculosis bacilli using Ziehl–Neelsen stained slides. By making this dataset available, a critical gap in the availability of public datasets that can be used for developing and testing artificial intelligence techniques for tuberculosis diagnosis is addressed. Our rationale is grounded in the urgent need for diagnostic tools that can enhance tuberculosis diagnosis quickly and efficiently, especially in resource-limited settings.ApproachThe Ziehl–Neelsen method was used to prepare 362 slides, which were manually read. According to the World Health Organization’s guidelines for performing bacilloscopy for tuberculosis diagnosis, experts annotated each slide to diagnose it as negative or positive. In addition, selected images underwent a detailed annotation process aimed at pinpointing the location of each bacillus and cluster within each image.ResultsThe database consists of three directories. The first contains all the images, separated by slide, and indicates whether it is negative or the number of crosses if positive, for each slide. The second directory contains the 502 images selected for training automated systems, with each bacillus’s position annotated and the Python code used. All the image fragments (positive and negative patches) used in the models’ training, validation, and testing stages are available in the third directory.ConclusionsThe development of this annotated image database represents a significant advancement in tuberculosis diagnosis. By providing a high-quality and accessible resource to the scientific community, we enhance existing diagnostic tools and facilitate the development of automated technologies.

Este estudo investiga as condições térmicas no ambiente interno de um gabinete na Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), comparando-as com o ambiente externo monitorado pela estação meteorológica do INMET em Janaúba, durante os meses de abril e maio de 2024. A análise aborda as variações de temperatura ao longo de quatro períodos diários (madrugada, manhã, tarde e noite) e destaca a estabilidade térmica e as temperaturas elevadas dentro do edifício da UFVJM. Os resultados indicam que, apesar do isolamento térmico das construções, o ambiente interno sofre de retenção de calor, resultando em temperaturas consistentemente altas, mesmo durante a noite. Com base nestes achados, o estudo conclui que há uma necessidade de um projeto de climatização adequado para os espaços internos da UFVJM.

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.

O objetivo deste trabalho é salientar o histórico da participação feminina na área de Tecnologia da Informação (TI) e destacar o cenário feminino atual dos cursos superiores de TI no Brasil. Para isso, realizou-se uma pesquisa bibliográfica e uma análise dos microdados referentes ao Censo da Educação Superior do período de 2014 a 2019, disponibilizados pelo Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP). Os resultados indicam a baixa representatividade feminina nos Cursos Superiores de TI no Brasil, durante o período estudado.