Levantamento e avaliacao da qualidade de sementes de algodao, Gossypium hirsutum L. distribuidas aos agricultores de alguns Estados do nordeste do Brasil.

bitstream/item/33361/1/LEVANTAMENTO-E-AVALIACAO.pd

Two new Morganella species from the Brazilian Amazon rainforest

Two new Morganella species, M. albostipitata and M. rimosa were found during studies of gasteroid fungi in the Brazilian Amazon rainforest, Adolpho Ducke Forest Reserve, Amazonas State, Brazil. The new taxa are described, and illustrated with photographs and line drawings, and taxonomical comments are made

The Illusion of Distribution-Free Small-Sample Classification in Genomics

Classification has emerged as a major area of investigation in bioinformatics owing to the desire to discriminate phenotypes, in particular, disease conditions, using high-throughput genomic data. While many classification rules have been posed, there is a paucity of error estimation rules and an even greater paucity of theory concerning error estimation accuracy. This is problematic because the worth of a classifier depends mainly on its error rate. It is common place in bio-informatics papers to have a classification rule applied to a small labeled data set and the error of the resulting classifier be estimated on the same data set, most often via cross-validation, without any assumptions being made on the underlying feature-label distribution. Concomitant with a lack of distributional assumptions is the absence of any statement regarding the accuracy of the error estimate. Without such a measure of accuracy, the most common one being the root-mean-square (RMS), the error estimate is essentially meaningless and the worth of the entire paper is questionable. The concomitance of an absence of distributional assumptions and of a measure of error estimation accuracy is assured in small-sample settings because even when distribution-free bounds exist (and that is rare), the sample sizes required under the bounds are so large as to make them useless for small samples. Thus, distributional bounds are necessary and the distributional assumptions need to be stated. Owing to the epistemological dependence of classifiers on the accuracy of their estimated errors, scientifically meaningful distribution-free classification in high-throughput, small-sample biology is an illusion

O cultivo irrigado da gravioleira (Annona muricata L.), no litoral Cearense.

O objetivo deste trabalho foi gerar informações sobre a produção da graviola sob irrigação, a partir de um ensaio conduzido durante cinco anos, com as cultivares Morada e Lisa submetidas a cinco diferentes níveis de irrigação.bitstream/CNPAT-2010/9020/1/Ct-088.pd

A systematic model of the LC-MS proteomics pipeline

MOTIVATION: Mass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view. RESULTS: In this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data

Comportamento das cultivares de algodoeiro moco Gossypium hirsutum L.r. marie galante Hutch no Estado da Paraiba em 1978/1979.

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