Equine infectious anemia : prevalence in working equids of livestock herds, in Minas Gerais, Brazil

Estimaram-se, no estado de Minas Gerais, a prevalência e a distribuição espacial da anemia infecciosa eqüina (AIE) em propriedades com eqüídeos de serviço. As amostras de sangue, de 6540 eqüídeos de 1940 rebanhos foram coletadas no período de setembro de 2003 a março de 2004, nos 853 municípios do estado. Utilizaram-se dois testes de laboratório em seqüência: ELISA, usando-se antígeno recombinante gp90, e imunodifusão em gel de ágar (IDGA). As prevalências foram de 5,3% [IC=4,3 a 6,3%] para rebanhos e de 3,1% [IC=2,2 a 3,9%] para animais. O estado de Minas Gerais foi considerado área endêmica para AIE. As mais altas prevalências para rebanhos e para animais foram encontradas na região Norte/Noroeste, seguida pela região Vale do Mucuri/Jequitinhonha. ___________________________________________________________________________________________________________ ABSTRACTThe prevalence and spatial distribution of equine infectious anemia (EIA) were estimated in livestock herds where equids were used as draft power and for transportation in the State of Minas Gerais, Brazil. Serum samples were collected from September/2003 to March/2004 in 853 municipalities of the state. The sample comprised 6,540 equids from 1,940 herds. Two laboratorial tests were performed in sequence: ELISA using a recombinant gp90 protein, following by the AGID. The prevalence in the herds was estimated in 5.3% [CI = 4.3 to 6.3%], and 3.1% [CI = 2.2 to 3.9%] of the animals tested were positive. Minas Gerais was considered an endemic region for EIA. The highest prevalence for herds and animals was found in North/Northwest region (strata) followed by Vale do Mucuri/Jequitinhonha region

Ecomorphological correlates of twenty dominant fish species of Amazonian floodplain lakes

Fishes inhabiting Amazonian floodplain lakes exhibits a great variety of body shape, which was a key advantage to colonize the several habitats that compose these areas adjacent to the large Amazon rivers. In this paper, we did an ecomorphological analysis of twenty abundant species, sampled in May and August 2011, into two floodplain lakes of the lower stretch of the Solimões River. The analysis detected differences among species, which could be probably associated with swimming ability and habitat use preferences. © 2017, Instituto Internacional de Ecologia. All rights reserved

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language's constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.Molecular Technology and Informatics for Personalised Medicine and Healt

Factor analysis as a tool to estimate association among individual proteins and other milk components with casein micelle size and cheese yield

ABSTRACT The present study attempted to identify individual milk proteins and other milk components that are associated with casein micelle size (CMS) and dry matter cheese yield (DMCY) using factor analysis. Here, we used 140 bulk tank milk samples from different farms. Milk composition was determined using a Fourier transform infrared equipament. The individual milk proteins were (αS-casein, β-casein, κ-casein, β-lactoglobulin and α-lactoalbumin) measured by their electrophoretic profile. The CMS was estimated by photon correlation spectroscopy, and the DMCY was determined using reduced laboratory-scale cheese production. Factor analysis partitioned the milk components into three groups that, taken together, explain 68.3% of the total variance. The first factor was defined as “CMS”, while the second as “DMCY” factor, based on their high loadings. The CMS was positively correlated with protein, casein, non-fat solids and αS-casein and negatively associated with κ-casein and β-lactoglubulin. DMCY was positively correlated with fat, protein, casein, total solids and negatively correlated with αs-casein. These results indicate that the variation of individual milk proteins may be an important aspect correlated to milk quality and cheese production