Wind farm bat fatalities in southern Brazil: temporal patterns and influence of environmental factors

Energy demand created by the present model of economic growth has transformed the natural land scape. Changes in megadiverse environments should be accompanied by studies that describe and predict the effects of these changes on ecosystems, underpinning the avoidance or at least the re duction of impacts and species conservation. Wind farm impacts on bats are scarcely known in Brazil. To fulfill this gap on spatiotemporal patterns in bat fatalities in a wind complex in southern Brazil were analysed. Monthly surveys were done around 129 wind towers in search for bat car casses between 2014 and 2018. The number of specimens found per species was analysed in annual sets and also seasonally to understand the influence of land use in the spatial pattern of bat fatalit ies. The activity of aerial insectivore bats was monitored using ultrasound detectors and modelled using Generalized Linear Models (GLM), using meteorological variables as predictors. As a result of 48 months of surveys, 266 carcasses of six insectivorous bat species were recorded. The highest number of fatalities belonged to Tadarida brasiliensis. Fatalities occurred exclusively between Oc tober and May (Austral Spring to Austral Autumn), mainly in towers near the closest urban centre. Most fatalities occurred in the first (69%) and fourth (17%) years of operation; fatalities were pos itively related to wind speed. Eighty-three percent of the bat activity occurred between 15 ◦C and 23 ◦C. To minimize fatalities of synanthropic bat species such as T. brasiliensis, we suggest that wind complexes should be located at least 4 km distant from the urban centres, where those species roost. Moreover, between December and March, when most species from subtropical and temper ate South America reproduce, wind towers located closer to known roosts should shut down on warmer nights, when bats are more active

O diagnóstico das faringoamigdalites: revisão sistemática

Introduction: Pharyngitis is a medical condition characterized by inflammation of the palatine tonsils. Typically caused by viral or bacterial infections, this condition affects individuals of all ages and can present a variety of uncomfortable symptoms. Methodology: To conduct a systematic review on the diagnosis of pharyngitis, the adopted methodology began with the definition of search filters. The established timeframe spanned from 2013 to 2023, aiming to obtain a contemporary analysis of the available literature on the subject. The search yielded initially identified 20 articles. Result: When streptococcal pharyngitis is identified early, and prompt treatment with penicillin is initiated, bacterial replication is suppressed, reducing the exaggerated immune response that may lead to rheumatic fever. Penicillin is particularly effective against Group A Streptococcus, the bacterium responsible for the majority of streptococcal pharyngitis cases. Conclusion: In conclusion, pharyngitis, though common and often considered a benign condition, underscores the crucial importance of early diagnosis to prevent serious complications. The swiftness in identifying the cause, whether of viral or bacterial origin, guides the appropriate therapeutic approach, directly influencing the effectiveness of treatment.Introdução: A faringoamigdalite é uma condição médica caracterizada pela inflamação das tonsilas palatinas. Geralmente causada por infecções virais ou bacterianas, essa condição afeta pessoas de todas as idades e pode apresentar uma variedade de sintomas desconfortáveis. Metodologia: Para realizar uma revisão sistemática sobre o diagnóstico das faringoamigdalites, a metodologia adotada começou com a definição dos filtros de busca. O período estabelecido compreendeu os anos de 2013 a 2023, visando obter uma análise contemporânea da literatura disponível sobre o tema. A pesquisa resultou em 20 artigos inicialmente identificados. Resultado: Quando a faringoamigdalite estreptocócica é identificada precocemente, e o tratamento com penicilina é iniciado prontamente, a replicação bacteriana é suprimida, reduzindo a resposta imunológica exagerada que pode levar à febre reumática. A penicilina é particularmente eficaz contra o Streptococcus do grupo A, a bactéria responsável pela maioria dos casos de faringoamigdalite estreptocócica. Conclusão: Em conclusão, a faringoamigdalite, embora comum e muitas vezes considerada uma condição benigna, destaca a importância crucial do diagnóstico precoce para evitar complicações graves. A rapidez na identificação da causa, seja ela de origem viral ou bacteriana, orienta a abordagem terapêutica apropriada, influenciando diretamente a eficácia do tratamento

Contexto brasileiro da Doença de Chagas: Perspectivas atuais sobre epidemiologia, vetores e diagnóstico

Este estudo discorre sobre os dados epidemiológicos mais recentes da doença de Chagas no Brasil, conforme apresentados no Boletim Epidemiológico do Ministério da Saúde de abril de 2022. Os resultados revelam uma ampla gama de informações, desde a prevalência da doença em diferentes regiões do país até os índices de vulnerabilidade. Notavelmente, há variações significativas nos indicadores entre estados e macrorregiões de saúde. Além disso, os dados destacam desafios no controle da doença, como a necessidade de intensificar as ações preventivas e de vigilância em áreas mais vulneráveis. Compreender esses padrões epidemiológicos é crucial para orientar políticas públicas direcionadas e estratégias de intervenção, visando reduzir a transmissão da doença e mitigar seus impactos na saúde pública. Essa análise aprofundada dos dados epidemiológicos fornece uma base sólida para o desenvolvimento de medidas eficazes de prevenção, diagnóstico e tratamento da doença de Chagas, contribuindo assim para o avanço no controle dessa enfermidade no Brasil

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost