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

Periconceptional use of folic acid and risk of miscarriage – Findings of Oral Cleft Prevention Program in Brazil

We report on the risk of miscarriage due to high dosage periconceptional folic acid (FA) supplementation from a double blind randomized clinical trial for prevention of orofacial clefts in Brazil. The miscarriage rate was 14.2% in the low dose FA group (0.4 mg per day) and 11.3% for the high dose (4 mg per day) group (p=0.4877); the population miscarriage rate is 14%. These results indicate that high dose FA does not increase miscarriage risk in this population and add further information to the literature on the safety of high FA supplementation for prevention of birth defect recurrence

A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

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