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This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved to be the most accurate model in terms of determination coefficient, mean squared error, and mean absolute error. The analysis revealed that the harvested area of permanent crops is the most influential variable in predicting deforestation, followed by the area of temporary crops. Furthermore, our findings indicate a significant inverse relationship between public spending and deforestation rates. These results contribute to understanding deforestation dynamics and offer potential strategies for improving conservation efforts.

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The cancer patient is undergoing a set of procedures that affect the physical and psychological balance, which can generate stressful situations in the organism. In turn, physical activity helps to treat stress, promoting well-being and reducing anxiety. Our study aimed to verify the influence of physical activity practice on stress symptoms in patients undergoing oncological chemotherapy treatment. For this, we used Lipp's Inventory of Stress Symptoms (ISSL) and the International Physical Activity Questionnaire (IPAQ) in 56 patients with cancer. Our data show that 45.4% of the participants exhibited stress scores, of which, 21.8% were at near to exhaustion, and 23.6% at exhaustion. We observed that 30% of them are considered sufficiently active, 25% insufficiently active, 45% sedentary. No association was observed between physical activity and stress. These findings indicate that stress symptoms occur in patients undergoing chemotherapy treatment regardless of the level of physical activity.

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Poorly designed preclinical studies may compromise human health due to erroneous conclusions regarding treatment effects in addition to contributing to experimental irreproducibility and wasted resources. Randomization is one of the crucial steps to enhance scientific rigor and is a commonly recognized bias-reducing instrument that increases the reliability and reproduction of studies involving animals (even with syngeneic animals). This procedure should be considered when planning a study and reported during data publication. In this context, this work aimed to highlight the importance of adopting quality measures in preclinical trials, with an emphasis on animal randomization. The 'Mouse Randomization' app was developed to help researchers estimate an adequate sample size to obtain significant statistical power, ensuring the ethical use of animals. This app is freely available on the internet to carry out animal randomization and calculate sample sizes for in vivo experiments. We believe that this brief discussion about animal randomization could raise awareness among researchers on how to improve the quality of preclinical research, increasing reproducibility and avoiding animal misuse.

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Sambaqui (shellmound) societies are among the most intriguing archaeological phenomena in pre-colonial South America, extending from approximately 8,000 to 1,000 years before present (yr BP) across 3,000 km on the Atlantic coast. However, little is known about their connection to early Holocene hunter-gatherers, how this may have contributed to different historical pathways and the processes through which late Holocene ceramists came to rule the coast shortly before European contact. To contribute to our understanding of the population history of indigenous societies on the eastern coast of South America, we produced genome-wide data from 34 ancient individuals as early as 10,000 yr BP from four different regions in Brazil. Early Holocene hunter-gatherers were found to lack shared genetic drift among themselves and with later populations from eastern South America, suggesting that they derived from a common radiation and did not contribute substantially to later coastal groups. Our analyses show genetic heterogeneity among contemporaneous Sambaqui groups from the southeastern and southern Brazilian coast, contrary to the similarity expressed in the archaeological record. The complex history of intercultural contact between inland horticulturists and coastal populations becomes genetically evident during the final horizon of Sambaqui societies, from around 2,200 yr BP, corroborating evidence of cultural change.

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The high diversity of bats in the Neotropics is primarily associated with various ectoparasite species on their bodies. Interactions between these animals need to be comprehensively investigated at landscape scales, focusing on understanding the patterns of diversity of species. We sought to evaluate, througt bat captures and ectoparasite sampling, the determinants of the composition of ectoparasitic flies species present in bats in in the Amazon and Cerrado biomes and ecotone areas. We used a generalized dissimilarity model (GDM) to verify what factors explained the composition of ectoparasitic flies of bats using landscape metrics, geographic distance, biome, and host composition. Twenty-four bat species haboured a total of 33 species of ectoparasitic flies. Host composition was the best predictor of fly composition, followed by the environmental variables and by biome. Geographical distance presented negligible effects. Studies on large scales tend to reveal a wide diversity of ectoparasitic flies. Host composition, as the best predictor of fly composition, may be associated with interspecific characteristics among species. We recommend studies focusing on the landscape to understand better the parasitic associations of bats and their distribution across environments.

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To verify the association between the practice of physical activity and dietary patterns and psychological distress before and during the lockdown due to COVID-19, a cross-sectional study was performed with 2000 Brazilians (mean [M] = 35.78 years; standard deviation [SD] = 11.20; 59.6% women) recruited through convenience sampling via digital media. Participants completed an electronic questionnaire containing sociodemographic and clinical information, nutritional patterns, physical activity, and psychological distress. Data were analyzed using descriptive statistics and multinomial regression. Before the COVID-19 lockdown, the chance of women presenting very high stress, in relation to men, was six times higher (OR = 6.32; 95% CI 4.20-9.51), a behavior that remained similar during the lockdown (OR = 6.63; 95% CI 4.40-10.00). Before the lockdown, insufficient physical activity doubled the chance of having very high stress in relation to those who engaged in physical activities six to seven times a week (OR = 2.11; 95% CI 1.10-4.02). However, during the lockdown, this probability was higher, from twice to 10 times the chance (OR = 10.19; 95% CI 4.85-21.41). Not exercising alone (OR = 2.18; 95% CI 1.52-3.11) and a decreasing physical activity frequency (OR = 2.28; 95% CI 1.40-3.71) were also associated with very high stress during the lockdown. Additionally, the consumption of smaller amounts of food showed an inverse association with very high stress (OR = 0.28; 95% CI 0.18-0.43). The maintenance of physical activity and an adequate eating frequency are measures that should be considered to cope with higher levels of anxiety and depression.

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The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associated with generalized Gaussian distributions in the context of Rényi, Tsallis and Kaniadakis statistics. In this regard, we analytically analyze the outlier-resistance of each proposal through the so-called influence function. In this way, we formulate inverse problems by constructing objective functions linked to the maximum likelihood estimators. To demonstrate the robustness of the generalized methodologies, we consider an important geophysical inverse problem with high noisy data with spikes. The results reveal that the best data inversion performance occurs when the entropic index from each generalized statistic is associated with objective functions proportional to the inverse of the error amplitude. We argue that in such a limit the three approaches are resistant to outliers and are also equivalent, which suggests a lower computational cost for the inversion process due to the reduction of numerical simulations to be performed and the fast convergence of the optimization process.

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The estimation of physical parameters from data analyses is a crucial process for the description and modeling of many complex systems. Based on Rényi α-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). From the assumption that the residual seismic data (the difference between the modeled and observed data) obeys the Rényi α-Gaussian probability distribution, we introduce an outlier-resistant criterion to deal with erratic measures in the FWI context, in which the classical FWI based on l2-norm is a particular case. The new misfit function arises from the probabilistic maximum-likelihood method associated with the α-Gaussian distribution. The PGF technique works on the forward modeling process by dividing the computational domain into outside target area and target area, where the wave equation is solved only once on the outside target (before FWI). During the FWI processing, Green's functions related only to the target area are computed instead of the entire computational domain, saving computational efforts. We show the effectiveness of our proposed approach by considering two distinct realistic P-wave velocity models, in which the first one is inspired in the Kwanza Basin in Angola and the second in a region of great economic interest in the Brazilian pre-salt field. We call our proposal by the abbreviation α-PGF-FWI. The results reveal that the α-PGF-FWI is robust against additive Gaussian noise and non-Gaussian noise with outliers in the limit α → 2/3, being α the Rényi entropic index.

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Background: Neuroinflammation is a response that involves different cell lineages of the central nervous system, such as neurons and glial cells. Among the non-pharmacological interventions for neuroinflammation, photobiomodulation (PBM) is gaining prominence because of its beneficial effects found in experimental brain research. We systematically reviewed the effects of PBM on laboratory animal models, specially to investigate potential benefits of PBM as an efficient anti-inflammatory therapy. Methods: We conducted a systematic search on the bibliographic databases (PubMed and ScienceDirect) with the keywords based on MeSH terms: photobiomodulation, low-level laser therapy, brain, neuroinflammation, inflammation, cytokine, and microglia. Data search was limited from 2009 to June 2022. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The initial systematic search identified 140 articles. Among them, 54 articles were removed for duplication and 59 articles by screening. Therefore, 27 studies met the inclusion criteria. Results: The studies showed that PBM has anti-inflammatory properties in several conditions, such as traumatic brain injury, edema formation and hyperalgesia, ischemia, neurodegenerative conditions, aging, epilepsy, depression, and spinal cord injury. Conclusion: Taken together, these results indicate that transcranial PBM therapy is a promising strategy to treat brain pathological conditions induced by neuroinflammation.