Navegando por Data de Publicação, começando com "2026-06-02"

Filtrar resultados por ano ou mês

Agora exibindo 1 - 3 de 3

Genomic organization and evolutionary dynamics of repetitive sequences in Cathartidae (Aves, Cathartiformes)
(Universidade Federal de São Carlos, 2026-06-02) Oliveira, Iago Santos de; Cioffi, Marcelo de Bello; lattes.cnpq.br/0242034365085727; https://orcid.org/0000-0003-4340-1464; https://lattes.cnpq.br/6001034472222760; https://orcid.org/0009-0004-1295-7667
In this study, we examined how repetitive DNAs influence chromosomal diversification and genome architecture in New World vultures (Cathartidae), a lineage distinguished by exceptional karyotypic stability (2n = 80). We examined five species and three distinct populations of Coragyps atratus using a combination of low-coverage genome sequencing, satellite DNA and repetitive DNA profiling, and in situ and in silico mapping. We found significant diversity in repeat composition between species and populations despite consistent chromosome structure, primarily due to lineage-specific amplification of transposable elements and satellite DNAs. The repeatome was primarily composed of retroelements, particularly LINEs and LTRs, whereas satellitomes exhibited both conserved and rapidly evolving families. Some satellite DNA groups exhibited population-specific divergence, indicating rapid turnover, whereas others were common across all species. The predominant occurrence of satellite DNAs, evidenced by in situ and in silico mapping, in centromeric and pericentromeric regions suggests their structural role in chromosome organization. However, changes in repeat distribution and abundance occurred without notable chromosomal rearrangements, indicating a disjunction between karyotypic evolution and repeat dynamics. Our findings emphasize the necessity of integrating genomic and cytogenetic methodologies to understand genome evolution, illustrating that repetitive DNA serves as a principal catalyst for genomic diversity in birds and demonstrating that substantial genomic innovation can transpire within conserved chromosomal structures
Estimativa da rigidez axial e capacidade de carga de pilares de madeira parcialmente reforçados por materiais compósitos laminados
(Universidade Federal de São Carlos, 2026-06-02) Jardim, Pedro Ignácio Lima Gadêlha; Christoforo, André Luis; https://lattes.cnpq.br/7623383075429186; https://lattes.cnpq.br/6028687809417062; https://orcid.org/0000-0002-4448-6554; Silva, Leandro José da; Bertolini, Marília da Silva; Pedroti, Leonardo Gonçalves; Chahud, Eduardo; https://lattes.cnpq.br/3410027147747579; https://lattes.cnpq.br/0280120880255759; https://lattes.cnpq.br/8770106216994640; https://lattes.cnpq.br/7061747933713446
Wood is a material widely used in construction since antiquity, commonly found in historical buildings around the planet. As wood is a biodegradable material, historical buildings that use it as a structural typology, without the knowledge of current techniques to ensure the durability of this element, require greater care regarding their protection. Several studies have been observed addressing techniques for strengthening and recovering timber structures, with the use of fiber-reinforced polymers (FRP) being a solution of growing interest in the scientific community. A gap was observed in published studies regarding the application of localized FRP reinforcement in timber columns with longitudinal openings. Thus, obtaining an equation to determine the ultimate load and axial stiffness is necessary to make the strengthening method viable. The present study proposed an equation capable of describing the ultimate load of timber columns with longitudinal openings and partial FRP reinforcement, considering multiplying factors related to instability and the confinement effect generated by the reinforcement. Furthermore, a factor for adjusting the axial stiffness was also proposed. For this, a broad parametric study was carried out through finite element method simulations, comprising 1392 models. The following parameters were considered: wood species, column diameter and length, opening proportion, fiber type, number of layers, and FRP spacing. For the model prediction, a 'white-box' evolutionary algorithm was adopted: symbolic regression. This solution was chosen due to its high capacity for data evaluation and physical interpretation, allowing for the topological dynamism of the generated model. The results of the parametric study revealed that the effectiveness of the reinforcement is not universal, being strictly dictated by the dominant collapse mode. In slender members, the application of low FRP rates optimized the structural response by generating local confinement and mitigating early transverse deformations. In contrast, in robust columns, excessive reinforcement demonstrated marginal efficiency due to premature failures induced by stress concentrations at the wood-composite interface. The predictive models generated by symbolic regression show significant statistical robustness (R2 = 0.92 for load capacity) and surpass pre-existing empirical models in the literature by introducing a slightly conservative variance, ensuring that eventual deviations act in favor of structural safety.
Classificação em três classes (normal, bacteriana e viral) em radiografias de tórax usando MobileNetv2 e Grad-Cam
(Universidade Federal de São Carlos, 2026-06-02) Almeida, Miguel Felipe de; França, Celso Aparecido de; https://lattes.cnpq.br/4547836128892982
Pneumonia is a respiratory disease of significant clinical and epidemiological relevance, whose initial investigation often involves the analysis of chest radiographs. However, the interpretation of these images can be challenging due to subtle radiographic patterns, overlapping anatomical structures, variations in image quality, and similarities among different pulmonary manifestations. In this context, deep learning techniques have been widely investigated as support tools for medical image analysis. This work aimed to develop and evaluate an automatic system for classifying chest X-rays into three classes: Normal, Bacterial Pneumonia, and Viral Pneumonia, using transfer learning with MobileNetV2, a lightweight convolutional neural network architecture, and visual analysis of predictions through Grad-CAM, an interpretability technique based on activation maps. For this purpose, a public chest X-ray dataset was used and reorganized into three categories based on the original file structure and filename patterns. The images were divided into training, validation, and test sets, resized to the MobileNetV2 input format, and submitted to a preprocessing workflow compatible with the adopted architecture. The training process was conducted in two stages: initially with the convolutional base frozen and, subsequently, with partial fine-tuning of the final layers of the network. The model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and a complementary analysis with a calibrated decision threshold. On the test set, the MobileNetV2 model achieved an accuracy of 80.13%, with the best performance observed for the Bacterial Pneumonia class and greater difficulty in distinguishing between the Normal and Viral Pneumonia classes. The complementary analysis with the calibrated threshold increased the accuracy to 80.45%, representing a slight improvement over the baseline model. In addition, the Grad-CAM maps made it possible to observe the image regions that most influenced the network decisions, contributing to a qualitative analysis of the predictions. The results indicate that the proposed approach is technically feasible as a support method for multiclass chest X-ray classification, combining satisfactory performance, low computational cost, and visual interpretability. However, the model should not be interpreted as an autonomous diagnostic tool, since it presents limitations related to the dataset, the absence of external clinical validation, and the inherent difficulty of separating visually similar radiographic patterns.