Análise multivariada da qualidade do sono em algumas comunidades do Sertão do Pajeú-PE

O sono é um fator indispensavél para a Qualidade de Vida (QV), importante para a saúde corporal e a mental, além de proporcionar uma melhoria no bem-estar humano. Com isso, o objetivo deste estudo foi avaliar e modelar a Qualidade do Sono (QS) em algumas comunidades do Sertão do Pajeú/PE, aplicando o Índice de Qualidade do Sono de Pittsburgh e o perfil sociodemográfico. Foi realizado um estudo tipo corte transversal em setembro de 2015, incluindo 73 moradores. O estudo estatístico foi baseado no Modelo Logístico Multivariado (MLM), que é um caso particular dos Modelos Lineares Generalizados (MLGs). Foi realizada uma análise dos fatores que contribuíram para os componentes de transtornos do sono, destacando-se a necessidade de levantar-se e dirigir-se ao banheiro (56,3%) e o despertar noturno ou diurno (54,8%), ao menos uma vez por semana. Os resultados do MLM neste estudo revelaram que, além da variável cochilar, as variáveis “Estado Civil”, “Gênero” e “Idade” são consideradas fatores explicativos da QV, verificando-se que a área sob a curva ROC correspondente ao valor de 0,79, demostrou um aceitável poder de discriminação de acordo com a classifiação dada por hosmer e Lemeshow. Estudos que visam avaliar e modelar a QS de determinada população, em especial agricultores familiares, são importantes, pois poderão identificar fatores que influenciam direta ou indiretamente na QV

Labor, trabalho e ação: elementos pertinentes aos conceitos arendtianos em relatos autobiográficos de trabalhadores do setor de transportes

A reflexão, junto à classe trabalhadora, sobre questões relacionadas à saúde no ambiente de trabalho, visando à criticidade e às ações que resultem no enfrentamento de seus problemas é um importante instrumento de mudança da realidade. O objetivo desta pesquisa exploratória é identificar elementos pertinentes aos conceitos arendtianos (labor, trabalho e ação) presentes no discurso de trabalhadores do setor de transportes participantes de um projeto de extensão universitária. A abordagem é qualitativa e a metodologia consiste em relato autobiográfico. As histórias dos trabalhadores foram gravadas em DVD e posteriormente transcritas. Para a análise, optou-se pela definição de categorias a priori (labor, trabalho e ação), uma vez que o marco teórico era a obra de Hannah Arendt. Como resultado, foram encontrados: insegurança alimentar, doenças crônicas não transmissíveis relacionadas ao conceito de labor; riscos ergonômicos e distúrbios psíquicos relacionados ao trabalho; e participação coletiva e inclusão digital como elementos da ação. Concluiu-se que conhecer, compreender e discutir essas três categorias para o incremento da reflexão acerca da saúde do trabalhador pode ser importante, uma vez que todas elas foram expressas nos relatos de vida, mostrando sua permanência e relevância na história de todos e de cada um

Physical activity levels during COVID-19 pandemic and its associated factors in patients with Chagas disease

BackgroundA better understanding of the consequences of the Coronavirus Disease 2019 (COVID-19) pandemic on lifestyle of patients with Chagas disease (ChD) is of paramount importance to facilitate the implementation of intervention strategies tailored to this specific population.ObjectiveThe present study aimed to evaluate the level of physical activity (PA) in Chagas disease (ChD) patients during the Coronavirus Disease 2019 (COVID-19) pandemic and its main associated factors.MethodsThis is a cross-sectional study with 187 patients of both sexes, aged ≥18 years, followed in a national infectious disease center (Rio de Janeiro, Brazil). The level of PA was determined by the International Physical Activity Questionnaire short version and expressed in terms of total volume of physical activity (PA) (MET-minutes per week). Individuals were classified as physically active following the 2020 World Health Organization PA guideline. The exposure variables were age, sex, race, marital status, schooling, income per capita, number of rooms per domicile, number of residents per domicile, body mass index, clinical form of ChD, COVID-19 antibodies, comorbidities, self-reported anxiety, self-reported depression, self-reported fear, and self-reported sadness. The association between the exposure variables with total PA (as a continuous variable) was determined using univariate and multivariate linear regression models.ResultsMean age was 61.1 ± 11.6 years. Most (62%) were women and self-declared their race as mixed (50.8%). The percentage of physically active individuals according to was 52%. The variables independently associated with total PA levels were non-white race (Exp β = 1.39; 95% CI 1.02 to 1.90), dyslipidemia (Exp β = 0.73; 95% CI 0.56 to 0.95) and self-reported depression during quarantine (Exp β = 0.71; 95% CI 0.52 to 0.96).ConclusionNon-white race was positively associated with total levels of PA, while dyslipidemia, and self-reported depression during quarantine were negatively associated with total levels of PA. The identification of associated factors can facilitate the development of tailored strategies to increase PA levels ChD patients

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

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

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

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