MILHO INTEIRO E MOÍDO NA ALIMENTAÇÃO DE POEDEIRAS SEMI-PESADAS DURANTE A FASE DE CRESCIMENTO

With the objective to evaluate the effect particles size of the corn in the feed and the supplement of hails in the performance of brown laying hens during the phase of growth, were used 420 Hy Line Brow pullets created on bed during 8th and 17th weeks of age, which will receive daily food and controlled, distributed in 5 treatments in a randomized design with 6 replications. The treatments constituted of the ground corn with geometrical average diameter (AGM) of 664,64 m; ground corn with 1.255 m of AGM;  ground corn with 3.209 m of AGM; grain corn and grain corn with supplement of hails with 3.560 m of AGM. Evaluated the weight game (g) and viability (%) between 8th and 17th weeks; the weight (g) and uniformity (%) in the 17th week. Corn with different particles size can be used in the food of brow laying hens during the phase of growth. The grain corn in the feed increase the weight and the weight game with reduces the uniformity. The viability there no affect. There is no advantage in the supplement of hails when the grain corn is supplied.Com o objetivo de avaliar o efeito de rações contendo diferentes granulometrias do milho em rações e a suplementação de pedriscos no desempenho de poedeiras semi-pesadas durante a fase de crescimento, utilizou-se 420 aves Hy Line Brown criadas sobre cama durante a 8ª e 17ª semanas de idade, as quais receberão alimentação balanceada e controlada, distribuídas em 5 tratamentos em um delineamento ao acaso com 6 repetições. Os tratamentos constituíram-se de milho moído com diâmetro geométrico médio (DGM) de 664,64 m, milho moído com DGM de 1.255 m; milho moído com DGM de 3.209 m; milho inteiro e milho inteiro com suplementação de pedriscos com 3.560 m de DGM. Avaliou-se o ganho de peso (g) e a viabilidade (%) entre a 8ª e a 17ª semanas, peso vivo (g) e uniformidade (%) das aves na 17ª semana. Milhos com diferentes granulometrias podem ser utilizados na alimentação de aves semi-pesadas durante a fase de crescimento. O milho inteiro nas rações faz aumentar o peso vivo e o ganho de peso com redução da uniformidade. A viabilidade não é afetada. Não há vantagem na suplementação de pedriscos quando se fornece o milho inteiro

AutoPSI: a database for automatic structural classification of protein sequences and structures

In protein research, structural classifications of protein domains provided by databases such as SCOP play an important role. However, as such databases have to be curated and prepared carefully, they update only up to a few times per year, and in between newly entered PDB structures cannot be used in cases where a structural classification is required. The Automated Protein Structure Identification (AutoPSI) database delivers predicted SCOP classifications for several thousand yet unclassified PDB entries as well as millions of UniProt sequences in an automated fashion. In order to obtain predictions, we make use of two recently published methods, namely AutoSCOP (sequence-based) and Vorolign (structure-based) and the consensus of both. With our predictions, we bridge the gap between SCOP versions for proteins with known structures in the PDB and additionally make structure predictions for a very large number of UniProt proteins. AutoPSI is freely accessible at http://www.bio.ifi.lmu.de/AutoPSIDB

Automatic structure classification of small proteins using random forest

<p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p

Discovery of novel targets for multi-epitope vaccines: Screening of HIV-1 genomes using association rule mining

© 2009 Paul and Piontkivska; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine