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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. [ABSTRACT FROM AUTHOR]

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide. [ABSTRACT FROM AUTHOR]

Vector control is one of the principal strategies used for reducing malaria transmission. Long-lasting insecticidal bed nets (LLINs) are a key tool used to protect populations at risk of malaria, since they provide both physical and chemical barriers to prevent human-vector contact. This study aimed to assess the physical durability and insecticidal efficacy of LLINs distributed in Cruzeiro do Sul (CZS), Brazil, after 4 years of use. A total of 3000 LLINs (PermaNet 2.0) were distributed in high malaria risk areas of CZS in 2007. After 4 years of use, 27 'rectangular' LLINs and 28 'conical' LLINs were randomly selected for analysis. The evaluation of physical integrity was based on counting the number of holes and measuring their size and location on the nets. Insecticidal efficacy was evaluated by cone bioassays, and the amount of residual insecticide remaining on the surface of the LLINs was estimated using a colorimetric method. After 4 years of use, physical damage was highly prevalent on the rectangular LLINs, with a total of 473 holes detected across the 27 nets. The upper portion of the side panels sustained the greatest damage in rectangular LLINs. The overall mosquito mortality by cone bioassay was < 80% in 25/27 rectangular LLINs, with panel A (at the end of the rectangular bednet) presenting the highest mortality (54%). The overall mean insecticide concentration was 0.5 µg/sample, with the bednet roof containing the highest average concentration (0.61 µg/sample). On the conical LLINs, 547 holes were detected, with the bottom areas sustaining the greatest damage. The cone bioassay mortality was < 80% in 26/28 of the conical LLINs. The mean insecticide concentration was 0.3 µg/sample. After 4 years of use, the insecticidal efficacy of the LLINs was diminished to below acceptable thresholds. [ABSTRACT FROM AUTHOR]

Water is a scarce, strategic resource and the most important input for economic development, especially in agricultural countries such as Brazil. Cocoa production is directly related to water availability, and, as climate changes, selecting drought-tolerant genotypes is vital to keep cacao crops sustainable. Here, we evaluated cacao genotypes under irrigated and water-stressed conditions and selected drought-tolerant ones based on nutritional and physiological traits. Thirty-nine genotypes were monitored for three years for agronomic traits and higher fruit yield. After this evaluation, the 18 most promising genotypes were evaluated in a randomized block design, under a 2 (with and without irrigation) × 18 (genotypes) factorial arrangement, with three replicates and five plants per plot. We evaluated seven physiological and 11 nutritional traits, selecting genotypes based on the Genotype-by-Trait Biplot approach. Significant effects (p < 0.05) were observed for the nutritional traits N, P, Mg, S, Zn, Cu, Mn and for the physiological traits CO2 assimilation rate (A), stomatal conductance (gs), transpiration (E), intercellular and atmospheric CO2 concentrations (Ci/Ca), intrinsic water use efficiency (A/gs), instantaneous water use efficiency (A/E), and instantaneous carboxylation efficiency (A/Ci), as determined by analysis of variance. The genotype × irrigation treatment interaction was significant (p < 0.05) for the traits A, gs, and E. Genotypes CP 41, CP 43, and CCN 51 exhibited superior performance for both nutritional and physiological traits (A, gs, and E). In the irrigated environment, CP 41 showed superiority in traits such as P, A/E, A/gs, Mn, S, and Zn. Conversely, under non-irrigated conditions, CP 43 exhibited better performance in nutritional properties, specifically Mn, Mg, and Zn. Notably, in both irrigated and non-irrigated environments, CCN 51 excelled in key physiological traits, including A/Ci, A/E, and A/gs. This robust performance across diverse conditions suggests that these three genotypes possess physiological mechanisms to endure water-stressed conditions. Our research can generate valuable insights into these genotypes informing suitable choices for cocoa cultivation, especially in the context of global climate change. [ABSTRACT FROM AUTHOR]

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers. [ABSTRACT FROM AUTHOR]

The Brazilian Organization for Crohn's Disease and Colitis (GEDIIB) established a national registry of inflammatory bowel disease (IBD). The aim of the study was to identify clinical factors associated with disease severity in IBD patients in Brazil. A population-based risk model aimed at stratifying the severity of IBD based on previous hospitalization, use of biologics, and need for surgery for ulcerative colitis (UC) and Crohn's Disease (CD) and on previous complications for CD. A total of 1179 patients (34.4 ± 14.7y; females 59%) were included: 46.6% with UC, 44.2% with CD, and 0.9% with unclassified IBD (IBD-U). The time from the beginning of the symptoms to diagnosis was 3.85y. In CD, 41.2% of patients presented with ileocolic disease, 32% inflammatory behavior, and 15.5% perianal disease. In UC, 46.3% presented with extensive colitis. Regarding treatment, 68.1%, 67%, and 47.6% received biological therapy, salicylates and immunosuppressors, respectively. Severe disease was associated with the presence of extensive colitis, EIM, male, comorbidities, and familial history of colorectal cancer in patients with UC. The presence of Montreal B2 and B3 behaviors, colonic location, and EIM were associated with CD severity. In conclusion, disease severity was associated with younger age, greater disease extent, and the presence of rheumatic EIM. [ABSTRACT FROM AUTHOR]

There is limited contemporary prospective real-world evidence of patients with chronic arterial disease in Latin America. The Network to control atherothrombosis (NEAT) registry is a national prospective observational study of patients with known coronary (CAD) and/or peripheral arterial disease (PAD) in Brazil. A total of 2,005 patients were enrolled among 25 sites from September 2020 to March 2022. Patient characteristics, medications and laboratorial data were collected. Primary objective was to assess the proportion of patients who, at the initial visit, were in accordance with good medical practices (domains) for reducing cardiovascular risk in atherothrombotic disease. From the total of patients enrolled, 2 were excluded since they did not meet eligibility criteria. Among the 2,003 subjects included in the analysis, 55.6% had isolated CAD, 28.7% exclusive PAD and 15.7% had both diagnoses. Overall mean age was 66.3 (± 10.5) years and 65.7% were male patients. Regarding evidence-based therapies (EBTs), 4% were not using any antithrombotic drug and only 1.5% were using vascular dose of rivaroxaban (2.5 mg bid). Only 0.3% of the patients satisfied all the domains of secondary prevention, including prescription of EBTs and targets of body-mass index, blood pressure, LDL-cholesterol, and adherence of lifestyle recommendations. The main barrier for prescription of EBTs was medical judgement. Our findings highlight that the contemporary practice does not reflect a comprehensive approach for secondary prevention and had very low incorporation of new therapies in Brazil. Large-scale populational interventions addressing these gaps are warranted to improve the use of evidence-based therapies and reduce the burden of atherothrombotic disease. ClinicalTrials.gov NCT04677725 [ABSTRACT FROM AUTHOR]

Soil fertility is key point to pastures systems and drives the microbial communities and their functionality. Therefore, an understanding of the interaction between soil fertility and microbial communities can increase our ability to manage pasturelands and maintain their soil functioning and productivity. This study probed the influence of soil fertility on microbial communities in tropical pastures in Brazil. Soil samples, gathered from the top 20 cm of twelve distinct areas with diverse fertility levels, were analyzed via 16S rRNA sequencing. The soils were subsequently classified into two categories, namely high fertility (HF) and low fertility (LF), using the K-Means clustering. The random forest analysis revealed that high fertility (HF) soils had more bacterial diversity, predominantly Proteobacteria, Nitrospira, Chloroflexi, and Bacteroidetes, while Acidobacteria increased in low fertility (LF) soils. High fertility (HF) soils exhibited more complex network interactions and an enrichment of nitrogen-cycling bacterial groups. Additionally, functional annotation based on 16S rRNA varied between clusters. Microbial groups in HF soil demonstrated enhanced functions such as nitrate reduction, aerobic ammonia oxidation, and aromatic compound degradation. In contrast, in the LF soil, the predominant processes were ureolysis, cellulolysis, methanol oxidation, and methanotrophy. Our findings expand our knowledge about how soil fertility drives bacterial communities in pastures. [ABSTRACT FROM AUTHOR]

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health. [ABSTRACT FROM AUTHOR]

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).)