Interferência de plantas daninhas na cultura do milho: I - efeito do número de repetições sobre a precisão dos resultados obtidos

Foi conduzido um experimento com o objetivo de determinar o mínimo de repetições para que níveis preestabelecidos de precisão sejam alcançados sobre a matointerferência na produtividade da cultura do milho mantida na presença ou na ausência das plantas daninhas até 0, 10, 20, 30, 40, 50, 60 e 120 dias após a emergência. O experimento foi conduzido no período de dezembro de 1997 a abril de 1998, correspondendo à safra de verão, na Fazenda Experimental Marcelo Mesquita Serva, Universidade de Marília - UNIMAR, município de Marília, Estado de São Paulo - Brasil. A precisão experimental avaliada pela diferença mínima significativa (dms) mostrou-se continuamente crescente com a adição de novas repetições, observando-se, contudo, reduções menores acima de seis repetições. Os valores médios do quadrado médio do resíduo (QMR) e do coeficiente de variação (CV) e o F dos tratamentos não foram alterados pelo aumento do número de repetições.

Produção animal e o meio ambiente: uma comparação entre potencial de emissão de metano dos dejetos e a quantidade de alimento produzido

O objetivo do trabalho foi efetuar a comparação das principais espécies de interesse agropecuário, em relação à eficiência de conversão das dietas em produtos de origem animal (carne ou ovo), produção de resíduo e potencial de emissão de metano, a partir da fermentação dos resíduos. Para isso, foram selecionadas cinco espécies de animais durante a fase de produção: 1) suínos, do nascimento ao abate (peso vivo (PV) final de 90 kg) ; 2) bovinos, do desmame ao abate (PV: 520 kg); 3) caprinos, do desmame ao abate (PV: 30 kg); 4) aves, durante toda a fase de postura (14,7 kg de ovos); 5) frango de corte, do nascimento ao abate (PV: 3,1 kg). Para a estimativa dos parâmetros propostos, foram medidos os dados do desempenho e efetuou-se a biodigestão anaeróbia dos dejetos produzidos pelos animais. de maneira geral, os dejetos dos ruminantes apresentaram altas concentrações de fibra e baixos potenciais de produção de biogás; no entanto, o menor desempenho destes animais na conversão do alimento em produto e a maior produção de dejetos fizeram com que eles apresentassem maiores produções de metano por kg de alimento produzido.

Caracterização dos dejetos de novilhos superprecoces: reciclagem energética e de nutrientes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Biodigestão anaeróbia e características de cama de frangos obtida sob diferentes quantidades iniciais de casca de arroz e três tipos de cobertura de galpões em Dourados - MS

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Acid and fermented silage characterization and determination of apparent digestibility coefficient of crude protein for pacu Piaractus mesopotamicus

Three types of raw materials including commercial waste from saltwater (SW), freshwater fish (FW) and tilapia fillet residue (FR) were used to produce fish silage by either acid digestion (2% formic acid and 2% sulfuric acid) or anaerobic fermentation (5% of Lactobacillus plantarum and 15% sugar cane molasses). Six test diets were used in digestibility trials prepared with 70% reference diet and 30% of each experimental silage. These diets were fed to juvenile pacu Piaractus mesopotamicus (146 g average weight) in triplicate. Fish were kept in 500-L tanks and feces collected by manual extrusion. It was observed for both processes that SW waste always had the highest moisture content and lowest fat and ash. Highest crude protein levels were found in silages from commercial fish waste (SW and FW) made from whole fish unfit for human consumption. However, apparent digestibility coefficients did not vary among diets (P > 0.05). Although values did not differ statistically, fermented silage consistently displayed higher digestibility coefficients compared to acid silage. The silages exhibited relatively high protein digestibility (72.5-80.0%), thus suggesting the feasibility of using fish industry by-products in aquaculture feeds.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Synthesis of a heparan sulfate mimetic disaccharide with a conformationally locked residue from a common intermediate

A simple mimetic of a heparan sulfate disaccharide sequence that binds to the growth factors FGF-1 and FGF-2 was synthesized by coupling a 2-azido-2-deoxy-D-glucosyl trichloroacetimidate donor with a 1,6-anhydro-2-azido-2-deoxy--D-glucose acceptor. Both the donor and acceptor were obtained from a common intermediate readily obtained from D-glucal. Molecular docking calculations showed that the predicted locations of the disaccharide sulfo groups in the binding site of FGF-1 and FGF-2 are similar to the positions observed for co-crystallized heparin-derived oligosaccharides obtained from published crystal structures.