Federal University of Mato Grosso do Sul

About: Federal University of Mato Grosso do Sul is a education organization based out in Campo Grande, Brazil. It is known for research contribution in the topics: Population & Species richness. The organization has 6903 authors who have published 9030 publications receiving 78977 citations. The organization is also known as: UFMS & Federal University of Mato Grosso do Sul.

Dengue epidemic in the State of Rio Grande do Norte, Brazil, in 1997.

Abstract: During 1997 a large dengue epidemic occurred in Rio Grande do Norte, a State in north-east Brazil. The co-circulation of dengue virus type 1 and dengue virus type 2 was demonstrated by virus isolation in Aedes albopictus clone C6/36 cell-line and by reverse transcriptase-polymerase chain reaction (RT-PCR). IgM capture enzyme-linked immunosorbent assay confirmed 52.3% of the 8105 studied cases and dengue antigen was demonstrated by immunohistochemical reaction on hepatocytes from 2 out of 5 fatal cases studied. Individual risk factors for development of dengue haemorrhagic fever/dengue shock syndrome, such as hypertension, diabetes mellitus and bronchial asthma, are discussed.

Desempenho animal e características de pastos de capim-piatã submetidos a diferentes intensidades de pastejo

Abstract: The objective of this work was to evaluate the nutritive value, sward structure, and animal performance of steers on piata palisade grass pastures (Urochloa brizantha, cultivar Piata), managed at 15, 30, and 45 cm sward heights, under continuous stocking. A randomized block design, with two replicates and three sward heights, was used. Sward height was measured twice a week, and the stocking rate was adjusted. Monthly, the pastures were sampled and the animals were weighted. There was no difference in the nutritive value of the pastures managed with different heights. The average daily gain of 650 g per steer was similar among the different sward heights. The stocking rate was lower for the pasture managed at 45 cm (2.4 AU ha-1), intermediary for the one with 30 cm (3.1 AU ha-1), and higher for the one managed at 15 cm (3.8 AU ha-1), resulting in greater gain per area for the pastures managed at a height of 15 cm (1,050 kg ha-1) and 30 cm (910 kg ha-1) than for the one managed at 45 cm (635 kg ha-1). Piata palisade grass shows great flexibility under continuous stocking and can be managed between 15 and 45 cm sward height.

Effects of body size on estimation of mammalian area requirements

Abstract: Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals 1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

Variação temporal e espacial na composição de guildas alimentares da ictiofauna em lagoas marginais do Rio Cuiabá, Pantanal Norte

Abstract: O presente trabalho teve como objetivo avaliar a variacao espacial e temporal das guildas alimentares das comunidades de peixes em 10 lagoas marginais do Rio Cuiaba no Pantanal. As lagoas foram amostradas em tres periodos do ciclo hidrologico (inicio e final da seca e inicio da enchente de 2005). As guildas alimentares foram determinadas atraves da analise do conteudo estomacal das especies mais abundantes da comunidade. Foram analisadas 37 especies pertencentes a oito guildas alimentares (insetivora, herbivora, onivora, zooplanctivora, planctivora, detritivora, bentivora e iliofaga), as quais variaram espacial e temporalmente. Observamos uma mudanca na composicao das guildas troficas entre os periodos e locais analisados, porem o numero de guildas nao variou espaco-temporalmente. Nossos resultados sugerem que as mudancas espaciais e temporais na composicao das guildas podem estar relacionadas com diversos fatores, como as alteracoes na composicao da comunidade onde novas especies podem compor as diferentes guildas; a exploracao de recursos mais abundantes em determinadas epocas do ano favorecendo a presenca de algumas guildas somente em alguns periodos.

Cross-domain aspect extraction for sentiment analysis: A transductive learning approach

Abstract: Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each feature of the product or service is considered ABSA usually explores supervised inductive learning algorithms, which requires intense human effort for the labeling process In this paper, we investigate Cross-Domain Transfer Learning approaches, in which aspects already labeled in some domains can be used to support the aspect extraction of another domain where there are no labeled aspects Existing cross-domain transfer learning approaches learn classifiers from labeled aspects in the source domain and then apply these classifiers in the target domain, ie, two separate stages that may cause inconsistency due to different feature spaces To overcome this drawback, we present an innovative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation through Heterogeneous Networks) First, we propose a heterogeneous network-based representation that combines different features (labeled aspects, unlabeled aspects, and linguistic features) from source and target domain as nodes in a single network Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used as a bridge for this propagation Our algorithm is based on a transductive learning process, where we explore both labeled and unlabeled aspects during the label propagation Experimental results show that the CD-ALPHN outperforms the state-of-the-art methods in scenarios where there is a high-level of inconsistency between the source and target domains — the most common scenario in real-world applications