CARACTERIZAÇÃO TOPOGRÁFICA DO HABITAT DO Cardisoma guanhumi LATREILLE, 1828 (DECAPODA, GECARCINIDAE) NA APA COSTA DOS CORAIS (PERNAMBUCO E ALAGOAS, BRASIL)

In the period from July to September 2012, a survey was made of the number of burrows of blue land crab (Cardisoma guanhumi), both in twelve profiles distributed in areas of mangrove, positioned perpendicularly to the wetlands of the Una's River (Pernambuco) and Camaragibe's River (Alagoas), aiming to acquire information on the distribution of holes in different types of topographies. The depth of burrows and the salinity and temperature of the water at the bottom of the burrows was registered. In Pernambuco and Alagoas, the profiles covered steep areas (with a gradient of 4.5°) and shallow areas (with a gradient of 0.02°). The maximum distance between existing burrows in the profiles, ranged from 58m and 359.5m, compared to a benchmark level (RN). The burrows depths ranged from 0.45m to 1.90m, with an depth average of 0.99m (DP = 0,29). The maximum depth of the burrows can be justified by the fact that there is less availability of water in northeastern soils.It is recommended, studied the mangroves, the establishment of an exclusion zone to preserve the habitat of Cardisoma guanhumi, a distance of at least 200m above the high tide mark.Keywords: blue land crab; mangrove; burrows.No período de julho a setembro de 2012, foi realizado um levantamento do número de tocas de Cardisoma guanhumi, em doze perfis localizados perpendicularmente no manguezal do rio Una (Pernambuco) e do rio Camaragibe (Alagoas), objetivando adquirir informações sobre a distribuição das tocas, em diferentes tipos de topografias. Foram registradas a profundidade das tocas, temperatura e salinidade da água na parte inferior das tocas. Em Pernambuco e Alagoas, os perfis abrangeram áreas íngremes (com declive de 4,5°) a suaves (declive de 0,02°). A distância máxima das tocas existentes nos perfis variou entre 58m e 359,5m, em relação a um referencial de nível (RN). As profundidades das tocas variaram de 0,45m a 1,90m, com média de 0,99m (DP = 0,29). A profundidade máxima, das tocas pode ser justificado pelo fato de haver menor disponibilidade de água em solos nordestinos. É recomendável, nos manguezais estudados, a implantação de uma área de exclusão para se preservar o habitat do Cardisoma guanhumi, numa distância de pelo menos 200m, acima da marca de preamar.Palavras-chave: guaiamum, manguezal, tocas

Mycobacterium leprae Recombinant Antigen Induces High Expression of Multifunction T Lymphocytes and Is Promising as a Specific Vaccine for Leprosy

Leprosy is a chronic disease caused by M. leprae infection that can cause severe neurological complications and physical disabilities. A leprosy-specific vaccine would be an important component within control programs but is still lacking. Given that multifunctional CD4 T cells [i.e., those capable of simultaneously secreting combinations of interferon (IFN)-γ, interleukin (IL)-2, and tumor necrosis factor (TNF)] have now been implicated in the protective response to several infections, we tested the hypothesis if a recombinant M. leprae antigen-specific multifunctional T cells differed between leprosy patients and their healthy contacts. We used whole blood assays and peripheral blood mononuclear cells to characterize the antigen-specific T cell responses of 39 paucibacillary (PB) and 17 multibacillary (MB) leprosy patients and 31 healthy household contacts (HHC). Cells were incubated with either crude mycobacterial extracts (M. leprae cell sonicate–MLCS) and purified protein derivative (PPD) or recombinant ML2028 protein, the homolog of M. tuberculosis Ag85B. Multiplex assay revealed antigen-specific production of IFN-γ and IL-2 from cells of HHC and PB, confirming a Th1 bias within these individuals. Multiparameter flow cytometry then revealed that the population of multifunctional ML2028-specific T cells observed in HHC was larger than that observed in PB patients. Taken together, our data suggest that these multifunctional antigen-specific T cells provide a more effective response against M. leprae infection that prevents the development of leprosy. These data further our understanding of M. leprae infection/leprosy and are instructive for vaccine development

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

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

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