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The SARS-CoV-2 (COVID-19) pandemic impacted the health systems between and within countries, and in the course of the pandemic sexual and reproductive health services were the most disrupted. Findings from high-income settings have reported significant changes in preterm birth prevalence during the pandemic period. To understand the possible effects of the COVID-19 pandemic on preterm birth numbers at the Brazilian national level. We compare the number of preterm deliveries during the COVID-19 pandemic period (2020 and 2021) with previous years. We conducted a population-based cross-sectional study taking the period from January 2017 to December 2021 to account. We use individual-level live births data from the Brazilian Live Birth Information System (SINASC), and we estimate the odds ratio (OR) of preterm deliveries using propensity score weighting analysis in Brazil and its regions. During the study period (from 2017 to 2021), about 2.7 million live births were recorded per year, and the missing value for gestational age at delivery was less than 1.5%. The preterm birth prevalence slightly increased during the COVID-19 pandemic compared to the pre-pandemic period (11.32% in 2021 vs 11.09% in 2019, p-value < 0.0001). After adjusting for sociodemographic variables, the OR of preterm births in Brazil has significantly increased, 4% in 2020 (OR: 1.04 [1.03-1.05] 95% CI, p-value < 0.001), and 2% in 2021(OR: 1.02 [1.01-1.03] 95% CI, p-value < 0.001), compared to 2019. At the regional level, the preterm birth pattern in the South, Southeast and Northeast regions show a similar pattern. The highest odds ratio was observed in the South region (2020 vs 2019, OR: 1.07 [1.05-1.10] 95% CI; 2021 vs 2019, OR: 1.03 [1.01-1.06] 95% CI). However, we also observed a significant reduction in the ORs of preterm births in the northern region during the COVID-19 pandemic (2020 vs 2019, OR: 0.96 [0.94-0.98] 95% CI) and (2021 vs 2019, OR: 0.97 [0.95-0.99] 95% CI). Our analysis shows that the pandemic has increased regional variation in the number of preterm births in Brazil in 2020 and 2021 compared to the pre-pandemic years., (© 2023. Springer Nature Limited.)

The objective of this work was to evaluate the persistence of faecal indicators and pathogenic organisms (Salmonella spp., Escherichia coli and viable helminth eggs) and the structure/diversity of bacterial communities in soil receiving raw sewage (RS) for an extended period of application (3 uninterrupted years). In the experimental design, three treatments were defined: (1) Control soil, characterized by the analysis of a composite sample collected in an area of similar soil, but not a recipient of RS (TSC); (2) Soil receiving conventional mineral fertilization, and furrow irrigation with supply water (TW); and (3) Fertirrigated soil with RS applied by furrows (TF). The results of persistence of pathogenic organisms and indicators in TF indicated a sanitary quality similar to the control soil (TSC), thus potentially bringing low risks of contamination with pathogens present in the soil. The presence of viable helminth eggs was not identified in any treatment studied, because of its low concentration in the raw sewage of the studied system. The TW, TF and TSC treatments had 34.8% of bacterial diversity in common. The bacterial composition of the soil showed a predominance of the Proteobacteria phylum in all treatments studied; however, TF was the one with the highest relative abundance of this phylum (44.8%)., (© 2023. Springer Nature Limited.)

The globally important carbon sink of intact, old-growth tropical humid forests is declining because of climate change, deforestation and degradation from fire and logging 1-3 . Recovering tropical secondary and degraded forests now cover about 10% of the tropical forest area 4 , but how much carbon they accumulate remains uncertain. Here we quantify the aboveground carbon (AGC) sink of recovering forests across three main continuous tropical humid regions: the Amazon, Borneo and Central Africa 5,6 . On the basis of satellite data products 4,7 , our analysis encompasses the heterogeneous spatial and temporal patterns of growth in degraded and secondary forests, influenced by key environmental and anthropogenic drivers. In the first 20 years of recovery, regrowth rates in Borneo were up to 45% and 58% higher than in Central Africa and the Amazon, respectively. This is due to variables such as temperature, water deficit and disturbance regimes. We find that regrowing degraded and secondary forests accumulated 107 Tg C year -1 (90-130 Tg C year -1 ) between 1984 and 2018, counterbalancing 26% (21-34%) of carbon emissions from humid tropical forest loss during the same period. Protecting old-growth forests is therefore a priority. Furthermore, we estimate that conserving recovering degraded and secondary forests can have a feasible future carbon sink potential of 53 Tg C year -1 (44-62 Tg C year -1 ) across the main tropical regions studied., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The blackfin tuna, Thunnus atlanticus, is a small tropical tuna exploited by recreational and commercial fisheries in various parts of its range. Information on stock structure is needed to develop management plans for this species but is currently lacking. In this work, 470 blackfin tuna from nine geographic populations were assayed at 13 homologous microsatellite markers to provide a first assessment of stock structure across the species range. The overall divergence among locality samples was very low (overall FST = 0.0004) indicating high connectivity of blackfin tuna across their range. No clear grouping of localities in differentiated units was inferred but structuring followed a weak isolation by distance pattern (r = 0.16, P = 0.032). Pairwise exact tests and spatial analysis of molecular variance suggested divergence of the sample collected offshore Baía Formosa (Brazil) possibly reflecting reproductive isolation of Brazilian populations from those in the Caribbean region and further north. Further study of the status of Brazilian populations and the transition between this region and the Caribbean is warranted. Cryptic subdivision within the Northern Hemisphere part of the range is possible and should be evaluated using increased marker density and a more comprehensive geographic coverage., (© 2022. The Author(s).)

Wildlife tourism plays a crucial role in biodiversity conservation. However, long-term sustainability is difficult to achieve. In this paper, we use property theory to produce a mathematical model that aims to better support stakeholders from the wildlife tourism industry to better guarantee a balance between sightings probability, tourists' overall experience and operators' sharing behaviour. We illustrate our model with the case study of Porto Jofre in the Pantanal wetland, Brazil. We show that while dealing with low sighting probability, tourist operators must share information about species' locations, leading to a system of open access regarding mobility and information. However, when sightings become common, sharing must be restricted to a bounded group avoiding overcrowding, a system of limited open access. Finally, when the sighting probability is high, no sharing is needed to achieve maximum overall experience. Our case study in Porto Jofre, Pantanal, Brazil, clearly shows these shifts in terms of governance strategies. We show that by looking at sighting probability it is possible to predict the best optimal social strategy that will guarantee long-term sustainability of the wildlife tourism initiatives. We also show the need for external support on adaptation in cases where current strategies do not match the predicted ones., (© 2024. The Author(s).)

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution., (© 2024. The Author(s).)

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death., (© 2024. The Author(s).)

The Zika virus (ZIKV) is a serious global public health crisis. A major control challenge is its multiple transmission modes. This paper aims to simulate the transmission patterns of ZIKV using a dynamic process-based epidemiological model written in ordinary differential equations, which incorporates the human-to-mosquito infection by bites and sewage, mosquito-to-human infection by bites, and human-to-human infection by sex. Mathematical analyses are carried out to calculate the basic reproduction number and backward bifurcation, and prove the existence and stability of the equilibria. The model is validated with infection data by applying it to the 2015-2016 ZIKV epidemic in Brazil. The results indicate that the reproduction number is estimated to be 2.13, in which the contributions by mosquito bite, sex and sewage account for 85.7%, 3.5% and 10.8%, respectively. This number and the morbidity rate are most sensitive to parameters related to mosquito ecology, rather than asymptomatic or human-to-human transmission. Multiple transmission routes and suitable temperature exacerbate ZIKV infection in Brazil, and the vast majority of human infection cases were prevented by the intervention implemented. These findings may provide new insights to improve the risk assessment of ZIKV infection., (© 2024. The Author(s).)

Wildlife roadkill is a recurring, dangerous problem that affects both humans and animals and has received increasing attention from environmentalists worldwide. Addressing this problem is difficult due to the high investments required in road infrastructure to effectively reduce wildlife vehicle collisions. Despite recent applications of machine learning techniques in low-cost and economically viable detection systems, e.g., for alerting drivers about the presence of animals and collecting statistics on endangered animal species, the success and wide adoption of these systems depend heavily on the availability of data for system training. The lack of training data negatively impacts the feature extraction of machine learning models, which is crucial for successful animal detection and classification. In this paper, we evaluate the performance of several state-of-the-art object detection models on limited data for model training. The selected models are based on the YOLO architecture, which is well-suited for and commonly used in real-time object detection. These include the YoloV4, Scaled-YoloV4, YoloV5, YoloR, YoloX, and YoloV7 models. We focus on Brazilian endangered animal species and use the BRA-Dataset for model training. We also assess the effectiveness of data augmentation and transfer learning techniques in our evaluation. The models are compared using summary metrics such as precision, recall, mAP, and FPS and are qualitatively analyzed considering classic computer vision problems. The results show that the architecture with the best results against false negatives is Scaled-YoloV4, while the best FPS detection score is the nano version of YoloV5., (© 2024. The Author(s).)

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km 2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha -1 , a mean of 174 Mg ha -1 , and a standard deviation of 102 Mg ha -1 . This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions., (© 2023. Springer Nature Limited.)

Cross-habitat spillover may be the outcome of a process of habitat loss or degradation where the receiving habitat serves as a refuge for organisms. Once surface habitats are lost or degraded, animals can find underground refuge in caves. This paper is focused on testing whether taxonomic order richness inside caves is positively affected by the loss of the native vegetation cover surrounding caves; whether degradation of native vegetation cover predicts cave community composition; and whether there is a pattern of cave community clusters delimited by similarity in the effects of habitat degradation on animal communities. We gathered a comprehensive speleological dataset consisting of occurrence data of thousands of invertebrates and vertebrates sampled in 864 iron caves in the Amazon, to test the effects of both variables measured inside caves and surrounding landscapes on spatial variation in richness and composition of animal communities. We show that caves can work as refuges for the fauna in landscapes where the native vegetation cover surrounding them was degraded, which was evidenced by landcover change increasing the richness of cave communities and clustering caves by similarity in community composition. Therefore, habitat degradation on the surface should be a key variable when characterizing cave ecosystems for conservation prioritization and offset planning. Habitat degradation causing a cross-habitat spillover effect highlights the importance of maintaining the connection between caves by the surface, especially large caves. Our study can help guide industry and stakeholders working on the complex conciliation between land use and biodiversity conservation., (© 2023. The Author(s).)

The saturated hydraulic conductivity, k sat , is a crucial variable to describe the hydromechanical behavior of soils. The value of k sat of lateritic soils that are typically found in tropical regions is highly affected by the soil's structure, void ratio, and fine particle aggregation. As a result, the determination of k sat in the field or in the laboratory is complex and involves greater variability, depending on the type of test and on the spatial location of sampling. This paper presents a study of k sat values of lateritic soils, analyzing them using Statistic, Multilayer Perceptron Artificial Neural Networks (ANN) and Decision Trees (CHAID). This study aims to support decision-making regarding the type of test and depth chosen for sampling in laterite soils and understanding the factors influencing the permeability of such soils. An extensive literature review on the k sat values of lateritic soils was performed, providing data for the establishment of a database comprise of 722 registries. According to agronomic and geotechnical soil classifications, the Brazilian lateritic soils presents a "moderate" hydraulic conductivity. A significant variation of permeability values along the depth was identified, particularly for depths between 0.1 and 0.2 m. Regarding the importance of testing variables, the ANN indicated a high dependency on the type of test. The decision tree divided field test and laboratory test automatically, inferring the relevance of the type of test to the determination of k sat ., (© 2022. The Author(s).)

Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing., (© 2022. The Author(s).)

Prevention of mother-to-child transmission programs have been one of the hallmarks of success in the fight against HIV/AIDS. In Brazil, access to antiretroviral therapy (ART) during pregnancy has increased, leading to a reduction in new infections among children. Currently, lifelong ART is available to all pregnant, however yet challenges remain in eliminating mother-to-child transmission. In this paper, we focus on the role of near-infrared (NIR) spectroscopy to analyse blood plasma samples of pregnant women with HIV infection to differentiate pregnant women without HIV infection. Seventy-seven samples (39 HIV-infected patient and 38 healthy control samples) were analysed. Multivariate classification of resultant NIR spectra facilitated diagnostic segregation of both sample categories in a fast and non-destructive fashion, generating good accuracy, sensitivity and specificity. This method is simple and low-cost, and can be easily adapted to point-of-care screening, which can be essential to monitor pregnancy risks in remote locations or in the developing world. Therefore, it opens a new perspective to investigate vertical transmission (VT). The approach described here, can be useful for the identification and exploration of VT under various pathophysiological conditions of maternal HIV. These findings demonstrate, for the first time, the potential of NIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost HIV detection., (© 2021. The Author(s).)