Universidade Federal de Mato Grosso

About: Universidade Federal de Mato Grosso is a education organization based out in Cuiabá, Brazil. It is known for research contribution in the topics: Population & Species richness. The organization has 7748 authors who have published 10181 publications receiving 89994 citations.

Litterfall production and fluvial export in headwater catchments of the southern Amazon

Abstract: Resolving the carbon (C) balance in the Amazonian forest depends on an improved quantification of production and losses of particulate C from forested landscapes via stream export. The main goal of this work was to quantify litterfall, the lateral movement of litter, and the export of coarse organic particulate matter (>2 mm) in four small watersheds (1-2 ha) under native forest in southern Amazonia near Juruena, Mato Grosso, Brazil (10 ◦ 25 � S, 58 ◦ 46 � W). Mean litterfall production was 11.8 Mg ha −1 y −1 (5.7 Mg C ha −1 y −1 ). Litterfall showed strong seasonality, with the highest deposition in the driest months of the year. About two times more C per month was deposited on the forest floor during the 6-mo dry season (0.65 Mg C ha −1 mo −1 ) compared with the rainy season (0.3 Mg C ha −1 mo −1 ). The measured C concentration of the litterfall samples was significantly greater in the dry season than in the rainy season (49% vs. 46%). The lateral movement of litter increased from the plateau (upper landscape position) towards the riparian zone. However, the trend in C concentration of laterally transported litter samples was the opposite, being highest on the plateau (44%) and lowest in the riparian zone (42%). Stream-water exports of particulate C were positively correlated with streamflow, increasing in the rainiest months. The export of particulate C in streamflow was found to be very small (less than 1%) in relation to the amount of litterfall produced.

Realce Tardio miocárdico por Ressonância Magnética Cardíaca pode identificar risco para Taquicardia Ventricular na Cardiopatia Chagásica Crônica

Abstract: FUNDAMENTO: Testes invasivos e nao invasivos tem sido usados para identificar risco para Taquicardia Ventricular (TV) em pacientes com Cardiopatia Chagasica Cronica (CCC). Ressonância Magnetica Cardiaca (RMC) pela tecnica do Realce Tardio (RT) pode ser util para selecionar pacientes com disfuncao ventricular global ou segmentar, com alto grau de fibrose e maior risco para TV clinica. OBJETIVO: Melhorar a identificacao de elementos preditivos de TV em pacientes com CCC. METODO: Quarenta e um pacientes com CCC foram pesquisados, sendo 30 (72%) do sexo masculino, com media de idade de 55,1 ± 11,9 anos. Vinte e seis pacientes apresentavam historico de TV (grupo TV), e 15 nao apresentavam TV (grupo NTV). Todos os pacientes incluidos tinham RT e disfuncao segmentar ventricular. Volume, porcentagem de comprometimento da espessura da parede ventricular em cada segmento, e distribuicao de RT foi determinado em cada caso. RESULTADOS: Nao houve diferenca estatistica em termos de volume de RT entre os dois grupos: grupo TV = 30,0 ± 16,2%; grupo NTV = 21,7 ± 15,7%; p = 0,118. A probabilidade de TV foi maior se duas ou mais areas contiguas de fibrose transmural estivessem presentes, sendo um fator preditor de TV clinica (RR 4,1; p = 0,04). A concordância entre os observadores foi de 100% nesse criterio (p < 0,001). CONCLUSAO: A identificacao de dois ou mais segmentos de RT transmural por RMC esta associado com a ocorrencia de TV clinica em pacientes com CCC. Portanto, a RMC melhora a estratificacao de risco na populacao estudada. (Arq Bras Cardiol. 2012; [online].ahead print, PP.0-0)

Benchmarking Antioxidant-Related Properties for Gallic Acid through the Use of DFT, MP2, CCSD, and CCSD(T) Approaches

Abstract: We present a benchmark investigation on the O–H bond dissociation enthalpies (BDEs) and ionization potential (IP) for gallic acid (GA), a widely known polyphenolic antioxidant. These properties wer...

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Abstract: Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia, Berlinia, and Anthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies.