3 resultados para ANIMAL FEED

em Repositório da Produção Científica e Intelectual da Unicamp


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Biofilm formation on reverse osmosis (RO) systems represents a drawback in the application of this technology by different industries, including oil refineries. In RO systems the feed water maybe a source of microbial contamination and thus contributes for the formation of biofilm and consequent biofouling. In this study the planktonic culturable bacterial community was characterized from a feed water of a RO system and their capacities were evaluated to form biofilm in vitro. Bacterial motility and biofilm control were also analysed using phages. As results, diverse Protobacteria, Actinobacteria and Bacteroidetes were identified. Alphaproteobacteria was the predominant group and Brevundimonas, Pseudomonas and Mycobacterium the most abundant genera. Among the 30 isolates, 11 showed at least one type of motility and 11 were classified as good biofilm formers. Additionally, the influence of non-specific bacteriophage in the bacterial biofilms formed in vitro was investigated by action of phages enzymes or phage infection. The vB_AspP-UFV1 (Podoviridae) interfered in biofilm formation of most tested bacteria and may represent a good alternative in biofilm control. These findings provide important information about the bacterial community from the feed water of a RO system that may be used for the development of strategies for biofilm prevention and control in such systems.

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Pregnant women have a 2-3 fold higher probability of developing restless legs syndrome (RLS - sleep-related movement disorders) than general population. This study aims to evaluate the behavior and locomotion of rats during pregnancy in order to verify if part of these animals exhibit some RLS-like features. We used 14 female 80-day-old Wistar rats that weighed between 200 and 250 g. The rats were distributed into control (CTRL) and pregnant (PN) groups. After a baseline evaluation of their behavior and locomotor activity in an open-field environment, the PN group was inducted into pregnancy, and their behavior and locomotor activity were evaluated on days 3, 10 and 19 of pregnancy and in the post-lactation period in parallel with the CTRL group. The serum iron and transferrin levels in the CTRL and PN groups were analyzed in blood collected after euthanasia by decapitation. There were no significant differences in the total ambulation, grooming events, fecal boli or urine pools between the CTRL and PN groups. However, the PN group exhibited fewer rearing events, increased grooming time and reduced immobilization time than the CTRL group (ANOVA, p<0.05). These results suggest that pregnant rats show behavioral and locomotor alterations similar to those observed in animal models of RLS, demonstrating to be a possible animal model of this sleep disorder.

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Animal welfare has been an important research topic in animal production mainly in its ways of assessment. Vocalization is found to be an interesting tool for evaluating welfare as it provides data in a non-invasive way as well as it allows easy automation of process. The present research had as objective the implementation of an algorithm based on artificial neural network that had the potential of identifying vocalization related to welfare pattern indicatives. The research was done in two parts, the first was the development of the algorithm, and the second its validation with data from the field. Previous records allowed the development of the algorithm from behaviors observed in sows housed in farrowing cages. Matlab® software was used for implementing the network. It was selected a retropropagation gradient algorithm for training the network with the following stop criteria: maximum of 5,000 interactions or error quadratic addition smaller than 0.1. Validation was done with sows and piglets housed in commercial farm. Among the usual behaviors the ones that deserved enhancement were: the feed dispute at farrowing and the eventual risk of involuntary aggression between the piglets or between those and the sow. The algorithm was able to identify through the noise intensity the inherent risk situation of piglets welfare reduction.