NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

Genome variability and population analysis in Fusarium moniliforme var. subglutinans

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN006476 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Diversidade genética de Begomovirus que infectam plantas invasoras na região nordeste Genetic diversity of Begomovirus infecting weeds in northeastern Brazil

Os Begomovirus fazem parte de uma família numerosa de fitovírus denominada Geminiviridae. Eles infectam ampla gama de hospedeiras, incluindo muitas espécies cultivadas, como tomate (Lycopersicon esculentum), feijão (Phaseolus vulgaris), pimentão (Capsicum annuum), caupi (Vigna unguiculata), mandioca (Manihot esculenta) etc., além de plantas invasoras de várias espécies. Em alguns casos, plantas invasoras podem funcionar como reservatórios desses vírus para plantas cultivadas, mediante transmissão pelo inseto-vetor. No presente trabalho, plantas invasoras com sintomas de mosaico amarelo, deformação do limbo foliar e redução do crescimento foram avaliadas no tocante à presença de Begomovirus mediante a técnica de PCR, empregando-se oligonucleotídeos universais para detecção desses vírus. Foram avaliadas 11 amostras, correspondendo a 10 espécies, coletadas em municípios dos Estados de Alagoas, Pernambuco e Bahia. Algumas, como Herissantia crispa, Waltheria indica e Triumfetta semitriloba, são relatadas pela primeira vez como espécies hospedeiras de Begomovirus. Para estimar a variabilidade genética dos Begomovirus detectados, o produto de amplificação dos diversos isolados foi clivado com as enzimas de restrição EcoRI, HinfI e TaqI. Confirmando resultados obtidos para plantas cultivadas por outros grupos de pesquisa, foram observados padrões distintos de clivagem para os isolados estudados, evidenciando a grande variabilidade genética desses vírus.<br>Genus Begomovirus belong to the family Geminiviridae. Begomovirus is associated with a wide range of hosts, including many cultivated species such as tomato (Lycopersicon esculentum), dry beans (Phaseolus vulgaris), pepper (Capsicum annuum), cowpea (Vigna unguiculata), cassava (Manihot esculenta), etc., besides many weed species. It has been demonstrated that in some cases weeds act as virus reservoirs for cultivated plants. In the present work, weed samples presenting yellow mosaic, foliar malformation and size reduction were tested by PCR for infection by Begomovirus, using specific degenerate oligonucleotides. Eleven samples corresponding to 10 plant species were collected in the countryside towns in the states of Alagoas, Pernambuco and Bahia. Some plant species such as Herissantia crispa, Waltheria indica and Triumfetta semitriloba are reported for the first time as hosts for Begomovirus. To estimate the genetic diversity of the detected Begomovirus, the amplified products of several isolates were cleaved with each three restriction enzymes, EcoRI, HinfI, and TaqI. Different patterns were observed for the studied isolates, pointing out to a great genetic diversity for these viruses

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