Inundation dynamics in seasonally dry floodplain forests in southeastern Brazil

Floodplains are one of the most threatened ecosystems. Even though the vegetation composition in floodplain forests is expected to reflect the variation in groundwater levels and flood duration and frequency, there is little field data on the inundation dynamics (e.g., the variability in flood duration and flood frequency), especially for the understudied seasonally dry tropics. This limits our understanding of these ecosystems and the mechanisms that cause the flooding. We, therefore, investigated six floodplain forests in the state of Minas Gerais in Brazil for 1.5 years (two wet seasons): Capivari, Jacaré, and Aiuruoca in the Rio Grande basin, and Jequitaí, Verde Grande, and Carinhanha in the São Francisco basin. These locations span a range of climates (humid subtropical to seasonal tropical) and biomes (Atlantic forest to Caatinga). At each location, we continuously measured water levels in five geomorphologically distinct eco‐units: marginal levee, lower terrace, higher terrace, lower plain, and higher plain, providing a unique hydrological dataset for these understudied regions. The levees and terraces were flooded for longer periods than the plains. Inundation of the terraces lasted around 40 days per year. The levees in the Rio Grande basin were flooded for shorter durations. In the São Francisco basin, the flooding of the levees lasted longer and the water level regime of the levees was more similar to that of the terraces. In the Rio Grande basin, flooding was most likely caused by rising groundwater levels (i.e., “flow pulse”) and flood pulses that caused overbank flooding. In the São Francisco basin, inundation was most likely caused by overbank flooding (i.e., “flood pulse”). These findings highlight the large variation in inundation dynamics across floodplain forests and are relevant to predict the impacts of changes in the flood regime due to climate change and other anthropogenic changes on floodplain forest functioning

Sensitive and specific serodiagnosis of Leishmania infantum infection in dogs by using peptides selected from hypothetical proteins identified by an immunoproteomic approach

In Brazil, the percentage of infected dogs living in areas where canine visceral leishmaniasis (CVL) is endemic ranges from 10 to 62%; however, the prevalence of infection in dogs is probably higher than figures reported from serological studies. In addition, problems with the occurrence of false-positive or false-negative results in the serodiagnosis of CVL have been reported. The present work analyzed the potential of synthetic peptides mapped from hypothetical proteins for improvement of the serodiagnosis of Leishmania infantum infection in dogs. From 26 identified leishmanial proteins, eight were selected, considering that no homologies between these proteins and others from trypanosomatide sequence databases were encountered. The sequences of these proteins were mapped to identify linear B-cell epitopes, and 17 peptides were synthesized and tested in enzyme-linked immunosorbent assays (ELISAs) for the serodiagnosis of L. infantum infection in dogs. Of these, three exhibited sensitivity and specificity values higher than 75% and 90%, respectively, to differentiate L. infantum-infected animals from Trypanosoma cruziinfected animals and healthy animals. Soluble Leishmania antigen (SLA) showed poor sensitivity (4%) and specificity (36%) to differentiate L. infantum-infected dogs from healthy and T. cruzi-infected dogs. Lastly, the three selected peptides were combined in different mixtures and higher sensitivity and specificity values were obtained, even when sera from T. cruzi-infected dogs were used. The study’s findings suggest that these three peptides can constitute a potential tool for more sensitive and specific serodiagnosis of L. infantum infection in dogsThis work was supported by grants from the Pró-Reitoria de Pesquisa from UFMG (Edital 07/2012), Instituto Nacional de Ciência e Tecnologia em Nano-biofarmacêutica (INCT-NANOBIOFAR, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (CBB-APQ-02364-08, CBB-APQ-00356-10, CBB-APQ-00496-11, and CBB-APQ-00819-12), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (APQ-472090/2011-9), and the Instituto Nacional de Ciência e Tecnologia em Vacinas (INCT-V). E.A.F.C. and A.P.F. are CNPq grant recipients. M.A.C.-F. is a FAPEMIG/CAPES grant recipient. This study was supported in Spain, in part, by grants from the Ministerio de Ciencia e Innovación (FIS/PI1100095)

Margarita de Sossa, Sixteenth-Century Puebla de los Ángeles, New Spain (Mexico)

Margarita de Sossa’s freedom journey was defiant and entrepreneurial. In her early twenties, still enslaved in Portugal, she took possession of her body; after refusing to endure her owner’s sexual demands, he sold her, and she was transported to Mexico. There, she purchased her freedom with money earned as a healer and then conducted an enviable business as an innkeeper. Sossa’s biography provides striking insights into how she conceptualized freedom in terms that included – but was not limited to – legal manumission. Her transatlantic biography offers a rare insight into the life of a free black woman (and former slave) in late sixteenth-century Puebla, who sought to establish various degrees of freedom for herself. Whether she was refusing to acquiesce to an abusive owner, embracing entrepreneurship, marrying, purchasing her own slave property, or later using the courts to petition for divorce. Sossa continued to advocate on her own behalf. Her biography shows that obtaining legal manumission was not always equivalent to independence and autonomy, particularly if married to an abusive husband, or if financial successes inspired the envy of neighbors

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

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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

STOP-Bang and NoSAS questionnaires as a screening tool for OSA: which one is the best choice?

INTRODUCTION: Currently there has been significant growth in the number of patients with suspected obstructive sleep apnea (OSA) referred to sleep clinics. In this sense, screening and stratification methods of the severity of this pathology have become increasingly relevant. OBJECTIVE: To evaluate the performance of the NoSAS and STOP-Bang scores in the screening of OSA in a sleep clinic. METHODS: Prospectively, for 12 months, all patients referred by primary care physicians to our sleep unit for clinical evaluation and who underwent in-lab polysomnography (PSG), also completed the NoSAS score (Neck circumference, Obesity, Snoring, Age, Sex) and STOP-Bang (Snoring, Tiredness, Observed apnea, Pressure (high blood), BMI, Age, Neck circumference, Gender). A ROC (receiver operating characteristic) analysis was used to find the scores that simultaneously maximize sensitivity and specificity for each diagnosis. RESULTS: Of the 294 individuals included, 84% had OSA, of which 28.8% were mild, 34.8% moderate, and 36.4% were severe. USING THE NOSAS SCORE FOR PREDICTING OSA, MODERATE TO SEVERE OSA, AND SEVERE OSA, THE ROC AREA WAS: 0.770 (95% CI: 0.703- 0.837), p<0.001, sensitivity of 57.5%, and specificity of 83.0% for a score of 12; 0.746 (95% CI: 0.691-0.802), p<0.001, sensitivity of 68.2% and specificity of 75.4% for a score of 13; 0.686 (95% CI: 0.622-0.749), p<0.001, sensitivity of 71.1% and specificity of 58.3% for a score of 13, respectively. USING THE STOP-BANG SCORE FOR PREDICTING OSA, MODERATE TO SEVERE OSA, AND SEVERE OSA, THE ROC AREA WAS: 0.862 (95% CI: 0.808-0.916), p<0.001, sensitivity of 68.4% and specificity of 85.1% for a score of 5; 0.813 (95% CI: 0.756-0.861), p<0.001, sensitivity of 77.3% and specificity of 66.1% for a score of 5; 0.787 (95% CI: 0.732-0.841), p<0.001, sensitivity of 70.0% and specificity of 79.9% for a score of 6, respectively. CONCLUSIONS: The ROC area was consistently high for both scores confirming the diagnostic ability of the NoSAS and STOP-Bang questionnaires for all OSA severities. Thus, our results suggest that these questionnaires may be a powerful tool for the screening and stratification of patients in the diagnosis of OSA. Overall, the diagnostic ability of the STOP-Bang was higher than the NoSAS.INTRODUÇÃO: Na atualidade tem se verificado um crescimento significativo no número de doentes com suspeita de apneia obstrutiva do sono (AOS) referenciados para consulta do sono. Nesse sentido, instrumentos de rastreio e estratificação da gravidade dessa patologia têm se tornado cada vez mais relevantes. OBJETIVO: Avaliar e comparar o desempenho da escala NoSAS e Stop-Bang para o rastreio de AOS. MÉTODOS: Estudo prospectivo durante 12 meses. Avaliados todos os doentes encaminhados aos cuidados de saúde primários do centro de medicina do sono que completaram o questionário NoSAS (Neck circumference, Obesity, Snoring, Age, Sex), Stop-Bang (Snoring, Tiredness, Observed apnea, Pressure [high blood], BMI, Age, Neck circumference, Gender) e foram submetidos a polissonografia. Utilizou-se uma análise ROC (receiver operating characteristic) para encontrar as pontuações que maximizam simultaneamente a sensibilidade e especificidade para cada diagnóstico. RESULTADOS: Incluídos 294 indivíduos, 84% apresentavam AOS, sendo que em 28,8% a OAS era ligeira, 34,8% moderada e 36,4% grave. USANDO A ESCALA NOSAS PARA PREVISÃO DE AOS, AOS MODERADA A GRAVE E AOS GRAVE, A ÁREA ROC FOI: 0,770 (IC95%: 0,703-0,837), p<0,001, sensibilidade de 57,5% e especificidade de 83,0% para a pontuação 12); 0,746 (IC95%: 0,691- 0,802), p<0,001, sensibilidade de 68,2% e especificidade de 75,4% para a pontuação 13); 0,686 (IC95%: 0,622-0,749), p<0,001, sensibilidade de 71,1% e especificidade de 58,3% para a pontuação 13), respectivamente. USANDO A ESCALA STOP-BANG PARA A PREVISÃO DE AOS, AOS MODERADA A GRAVE E AOS GRAVE, A ÁREA ROC FOI: 0,862 (IC95%: 0,808-0,916), p<0,001, sensibilidade de 68,4% e especificidade de 85,1% para pontuação 5); 0,813 (IC95%: 0,756-0,861), p<0,001, sensibilidade de 77,3% e especificidade de 66,1% para a pontuação 5); 0,787 (IC95%: 0,732-0,841), p<0,001, sensibilidade de 70,0% e especificidade de 79,9% para a pontuação 6), respectivamente. CONCLUSÕES: A área ROC foi consistentemente alta para as duas escalas, confirmando a capacidade diagnóstica dos questionários NoSAS e Stop-Bang para todos os graus de gravidade de AOS. Assim, os nossos resultados sugerem que esses questionários podem ser um importante instrumento para rastreio e estratificação de doentes no diagnóstico de AOS. Globalmente, a capacidade de diagnóstico do Stop-Bang foi superior à do NoSAS