Gesneriaceae da Serra do Relógio, Descoberto, Estado de Minas Gerais, Brasil

The Serra do Relógio is located in the municipality of Descoberto, Zona da Mata in the State of Minas Gerais. There is two important conservation units in this region, Reserva Biológica da Represa do Grama and Reserva Particular do Patrimônio Natural Alto da Boa Vista. Both Conservation Units together cover a total area of ~403 ha of vegetation, mainly semi-deciduous seasonal forest. The Gesneriaceae is represented in the area by eight species, distributed in the genus Besleria (2 spp.), Nematanthus (3 spp.), Paliavana (1 spp.), Sinningia (1 spp.) and Vanhouttea (1spp.). We present descriptions, identification key, comments on taxonomy, distribution and ecology as well as photos of all recorded species.A Serra do Relógio está localizada no município de Descoberto, Zona da Mata no Estado de Minas Gerais. Há duas importantes Unidades de Conservação, a Reserva Biológica da Represa do Grama (RBRG) e a Reserva Particular do Patrimônio Natural Alto da Boa Vista (RPPNABV). Juntas abrangem uma área total de 402,6 hectares, sendo composta, predominantemente pela Floresta Estacional Semidecidual baixo-montana. Gesneriaceae está representada na área por oito espécies, distribuídas nos gêneros Besleria (2 spp.), Nematanthus (3 spp.), Paliavana (1 spp.), Sinningia (1 spp.) e Vanhouttea (1 spp.). Neste trabalho são apresentadas descrições, chave de identificação, comentários sobre taxonomia, distribuição e ecologia assim como fotos para todas as espécies

EpIG‐DB: A database of vascular epiphyte assemblages in the Neotropics

Vascular epiphytes are a diverse and conspicuous component of biodiversity in tropical and subtropical forests. Yet, the patterns and drivers of epiphyte assemblages are poorly studied in comparison with soil‐rooted plants. Current knowledge about diversity patterns of epiphytes mainly stems from local studies or floristic inventories, but this information has not yet been integrated to allow a better understanding of large‐scale distribution patterns. EpIG‐DB, the first database on epiphyte assemblages at the continental scale, resulted from an exhaustive compilation of published and unpublished inventory data from the Neotropics. The current version of EpIG‐DB consists of 463,196 individual epiphytes from 3,005 species, which were collected from a total of 18,148 relevés (host trees and ‘understory’ plots). EpIG‐DB reports the occurrence of ‘true’ epiphytes, hemiepiphytes and nomadic vines, including information on their cover, abundance, frequency and biomass. Most records (97%) correspond to sampled host trees, 76% of them aggregated in forest plots. The data is stored in a TURBOVEG database using the most up‐to‐date checklist of vascular epiphytes. A total of 18 additional fields were created for the standardization of associated data commonly used in epiphyte ecology (e.g. by considering different sampling methods). EpIG‐DB currently covers six major biomes across the whole latitudinal range of epiphytes in the Neotropics but welcomes data globally. This novel database provides, for the first time, unique biodiversity data on epiphytes for the Neotropics and unified guidelines for future collection of epiphyte data. EpIG‐DB will allow exploration of new ways to study the community ecology and biogeography of vascular epiphytes

Diarréia em bezerros da raça Nelore criados extensivamente: estudo clínico e etiológico Diarrhea in Nelore calves: Clinical and etiologic study

A diarréia é considerada uma das principais causas de morbidade e mortalidade de bezerros neonatos. Foram colhidas 100 amostras fecais diarréicas e 30 amostras não diarréicas (grupo controle), de bezerros Nelore com até nove semanas de idade com o objetivo de detectar os enteropatógenos Salmonella spp., Escherichia coli, rotavírus, coronavírus, Cryptosporidium spp. e ovos de helmintos. Enteropatógenos foram detectados em 79,0% das amostras diarréicas e em 70,0% das amostras não-diarréicas. No grupo de bezerros com diarréia, E. coli (69,0%) foi o agente mais freqüentemente isolado, seguido de Cryptosporidium spp. (30,0%), coronavírus (16,0%) e rotavírus (11,0%). No grupo controle, E. coli, Cryptosporidium spp. e coronavírus foram detectados, respectivamente, em 66,7%, 10,0% e 3,3% das amostras. Salmonella spp. e ovos de estrongilídeos não foram encontrados nos dois grupos avaliados. A fímbria K99 foi identificada exclusivamente nas linhagens de E. coli isoladas de bezerros com diarréia (5,8%). Entre os antimicrobianos avaliados "in vitro" a enrofloxacina, a norfloxacina e a gentamicina foram os mais efetivos. O peso dos bezerros aos 210 dias de idade não apresentou diferença significativa entre os animais com e sem diarréia.<br>Diarrhea is considered as one of the main causes of morbidity and mortality in neonates calves. Fecal samples from 100 diarrheic and 30 non-diarrheic (control group) Nelore calves less than 9 weeks old were collected for Salmonella spp., Escherichia coli, rotavirus, coronavirus, Cryptosporidium spp., and for helminth eggs investigation. Enteropathogens were detected in 79.0% diarrheic samples and 70.0% non-diarrheic samples. Among diarrheic calves, Escherichia coli (69.0%) was the most common agent found, following by Cryptosporidium spp. (30.0%), coronavirus (16.0%), and rotavirus (11.0%). In the control group, E. coli, Cryptosporidium spp. and coronavirus were detected in 66.7%, 10.0% and 3.3% of the samples, respectively. Salmonella spp. and strongylids were not found in any of the calves from either group. The K99 fimbrial only was detected in E. coli strains from diarrheic calves (5.8%). Enrofloxacin, norfloxacin, and gentamicin were the most effective among the antimicrobials tested. The weight of 210-day-old calves did not show statistic differences between diarrheic and non-diarrheic calves

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024