Biology subjects

Dos Santos, M. A.

Publications and source records attributed to Dos Santos, M. A..

4 recordsLinked to original sources

bioRxiv2026

Understanding malaria transmission in Angola: first detection of Plasmodium malariae in Anopheles spp. and the role of secondary vectors in the Balombo region, Benguela Province

Background: Angola is one of the most affected countries in sub-Saharan Africa and was off-track to meet the WHO Global Technical Strategy targeting of a 75% reduction in malaria incidence and mortality by 2025. Recent studies highlighted major knowledge gaps regarding malaria vectors in Angola, particularly secondary vectors. A malaria control project initiated in 2007 in Balombo (Benguela Province, central Angola) has monitored malaria dynamics for 18 years, revealing increased Plasmodium prevalence and more frequent detection of non-falciparum species, mainly Plasmodium malariae (in co-infection with P. falciparum) in villagers of the Balombo region. This study aimed to characterize Anopheles populations in this region by assessing species composition, behavioural patterns, and infection rate. And to a less extent, to compare the performance of two indoor and outdoor mosquito trapping methods. Methods: Surveys were conducted during the rainy (December 2023 and 2024) and dry seasons (July 2024) in villages around the town of Balombo. Adult mosquitoes were collected indoors and outdoors using CDC Light Traps and BG-Pro traps in randomly selected houses. Female Anopheles were morphologically and molecularly identified. Primary and secondary vectors were screened for Plasmodium spp. using qPCR-high resolution melting, and positive samples were sequenced. Results: A strong seasonal effect was observed in Anopheles populations, with higher abundance, diversity, and Plasmodium- infection rates during the rainy season. An. funestus remained the main vector, but Plasmodium spp. was also detected in five other species: An. arabiensis, An. gambiae, An. marshallii, An. rufipes, and An. maculipalpis. This is the first report of P. malariae in Anopheles mosquitoes and the first mention of the role of secondary vectors in malaria transmission in Benguela Province. Conclusion: This study provides a thorough and updated inventory of Anopheles species in the Balombo region. Molecular biology confirmed the presence of P. falciparum and P. malariae in several species of Anopheles underscoring the existence of a complex transmission system in villages of Balombo. Infected exophagic and exophilic Anopheles species, exhibiting opportunistic behaviour, likely contribute, as secondary vectors, to the increase of Plasmodiumspp. transmission in the study sites. Therefore, confirming the need to implement complementary interventions targeting outdoor malaria transmission.

microbiology↗

bioRxiv2026

Revisiting Logistic Regression for High-Dimensional Gene Expression Data

Logistic regression remains a widely used classification method due to its interpretability and computational efficiency, but its direct application to high-dimensional biomedical data is limited when the number of features greatly exceeds the number of samples. In this paper, we propose a reformulated logistic regression framework designed for feature selection and classification in complex high-dimensional settings. The method is evaluated on three biomedical datasets, including scenarios with tens of thousands of attributes and substantially fewer samples. Across these datasets, the proposed approach achieved clear separation between control and disease groups while selecting a compact set of features. Several selected features were consistent with previously reported disease-associated markers, supporting the biological plausibility of the model, while additional selected features suggest potential novel candidates for further investigation. These results indicate that the proposed framework may provide an interpretable and computationally efficient alternative for feature selection in high-dimensional computational biology applications.

bioinformatics↗

bioRxiv2025

A single low-dimensional neural component of motor unit activity explains force generation across repetitive isometric tasks

Previous studies suggest that low-dimensional control underlies motor unit activity, with low-frequency oscillations in common synaptic inputs serving as the primary determinant of muscle force production. In this study, we used principal component analysis (PCA) and factor analysis (FA) to investigate the relationship between low-dimensional motor unit components and force oscillations during repetitive isometric tasks with similar force profiles. We assessed the consistency of these components across trials in both individual (tibialis anterior; first dorsal interosseous) and synergistic muscles (vastus medialis, VM; vastus lateralis, VL). Participants performed 15 trials of a force-matching learning task. Three post-skill acquisition trials were selected for analysis to ensure high similarity in force profiles. Motor units were decomposed from high-density surface electromyograms, tracked across trials, and their smoothed discharge rates were decomposed into low-dimensional components using PCA and FA. Parallel analysis indicated that a single component could explain the smoothed discharge rates for the individual muscles and two components for VM-VL. Importantly, the first component explained most of the variance ([~]70%) in smoothed discharge rates across all muscles. The first motor unit component also showed significantly higher correlations with force oscillations than the second component and remained highly consistent across trials. These findings were further supported by a non-linear framework combining network- and information-theoretic tools, which revealed high motor unit network density in the first component of all muscles. Collectively, these results suggest that, during isometric contractions, motor unit activity is primarily controlled by a single dominant shared synaptic input that closely mirrors force oscillations.

neuroscience↗

bioRxiv2021

Milk-Way Algorithm applied in Imbalanced Dataset

We wish to evaluate the algorithm Milk-Way, using a known dataset deposited in a public repository. The new algorithm, which converges various techniques from different areas of knowledge, can classify ligands and select potential new drugs. It was used a dataset of ligands, organized by 15 Bioassays and described by different fingerprints. Full details of the dataset architecture were already published in a public repository. Through the stratified feature selection, using the Milk-Way algorithm, the True Positive and False Positive Rates reached a higher performance compared to the published paper. Using all the features available for each Bioassay, we reached the lowest metrics in all of them. We demonstrated that adding more features have not made a significant impact on the performance. In all the Bioassays, the True Positives and False Positives reached 100% and 0%, respectively, only using 50% and 75% of the features available. The Milk-Way algorithm suggests a holistic approach, which will contribute to the machine-learning area, namely to classified ligands in the virtual screening.

bioinformatics↗