Pollen analysis of honey samples produced in the counties of Santa Helena and Terra Roxa, western Region of Paraná, Southern Brazil

A melissopalinologia foi utilizada para determinar a origem botânica do mel, importante para sua rastreabilidade. Quarenta amostras de mel dos municípios de Santa Helena (20) e Terra Roxa (20), região oeste do Paraná, foram analisadas de acordo com os tipos de pólen e comparações com os catálogos de pólen e literatura especializada. Em relação à qualidade analítica, foram identificados 300 grãos de pólen por amostra. Nas amostras de Santa Helena, foram encontrados 71 tipos de pólen de 24 famílias, classificados como mel monofloral de Hovenia dulcis (8) e o restante como multifloral (12). Nas amostras da Terra Roxa, 64 tipos de pólen pertencentes a 29 famílias foram classificadas como Glycine max L. Merrill (7), Mimosa scabrella Benth (2),Mimosa caesalpiniifolia Benth (1), Mimosa verrucosa Benth (1), Mikania sp. (1) e Senecio sp. (1) e como multifloral (7). Os dois locais têm um alto índice de similaridade, no entanto, a predominância de alguns tipos de pólen indica a especificidade botânica da localidade. Para Santa Helena, maior significância foi observada para o pólen de H. dulcis , Eucalyptus sp. , Parapiptadenia rigida , e leucena ; em Terra Roxa, os tipos de pólen G. max , M. scabrella e Eucalyptussp. teve mais incidentes. Apesar do índice de similaridade, os indicadores mostram diferenças entre as áreas produtoras. As amostras de mel de Santa Helena apresentaram maior diversidade de pólen em relação às amostras de Terra Roxa, refletindo a cobertura vegetativa predominante de matas ciliares e culturas agrícolas, respectivamente, em cada município

Space-temporal dynamics and factors associated with newborn mortality / Dinâmica espaço-temporal e fatores associados à mortalidade neonatal

Objective: to analyze the spatial and temporal distribution of neonatal mortality and associated factors in Piauí from 2007 to 2017. Method: the Joinpoint method, Bayesian statistics and the Scan technique were used. The multivariate analysis of the indicators was performed using the Ordinary Least Squares Estimation model, considering p<0.05. Results: neonatal mortality decreased linearly and significantly over the period studied. The highest Bayesian rates ranged from 16.34 to 18.38 deaths per 1,000 live births, especially in Southeast Piauí. There was a negative association between neonatal mortality and the variables: Illiteracy rate (β = -0.60; p= 0.027), Family Health Strategy Coverage (β = -2.80; p= 0.023) and Human Development Index Municipal (β = -0.60; p= 0.003). Conclusion: neonatal mortality continues to decrease and its distribution in the territory proved to be irregular. Socioeconomic and health indicators influence neonatal mortality in Piauí.Objetivo: analizar la distribución espacial y temporal de la mortalidad neonatal y factores asociados en Piauí de 2007 a 2017. Método: se utilizó el método Joinpoint, la estadística bayesiana y la técnica Scan. El análisis multivariado de los indicadores se realizó mediante el modelo de Estimación por Mínimos Cuadrados Ordinarios, considerando p<0,05. Resultados: la mortalidad neonatal disminuyó lineal y significativamente durante el período estudiado. Las tasas bayesianas más altas oscilaron entre 16,34 y 18,38 muertes por 1.000 nacidos vivos, especialmente en el Sudeste de Piauí. Hubo asociación negativa entre la mortalidad neonatal y las variables: Tasa de Analfabetismo (β = -0,60; p= 0,027), Cobertura de la Estrategia de Salud de la Familia (β = -2,80; p= 0,023) e Índice de Desarrollo Humano Municipal (β = -0,60; p= 0,003). Conclusión: la mortalidad neonatal continúa en descenso y su distribución en el territorio resultó ser irregular. Indicadores socioeconómicos y de salud influyen en la mortalidad neonatal en Piauí.Objetivo: analisar a distribuição espacial e temporal da mortalidade neonatal e fatores associados no Piauí de 2007 a 2017. Método: foi utilizado o método Joinpoint, estatística bayesiana e a técnica de varredura Scan. A análise multivariada dos indicadores foi realizada através do modelo Ordinary Least Squares Estimation, considerando-se p<0,05. Resultados: a mortalidade neonatal reduziu de forma linear e significativa ao longo do período estudado. As maiores taxas bayesianas variaram de 16,34 a 18,38 óbitos por 1.000 nascidos vivos, especialmente no Sudeste piauiense. Houve associação negativa entre a mortalidade neonatal e as variáveis: Taxa de analfabetismo (β = -0,60; p= 0,027), Cobertura da Estratégia Saúde da Família (β = -2,80; p= 0,023) e Índice de Desenvolvimento Humano Municipal (β = -0,60; p= 0,003). Conclusão: a mortalidade neonatal segue decrescente e sua distribuição no território mostrou-se irregular. Indicadores socioeconômicos e de saúde influenciam a mortalidade neonatal no Piauí.

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

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

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

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

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 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set 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 southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard 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 data sets of Neotropical Series that 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 data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data

International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN