Unsupervised feature selection for large data sets

The last decade saw a considerable increase in the availability of data. Unfortunately, this increase was overshadowed by various technical difficulties that arise when analysing large data sets. These include long processing times, large requirements for data storage, and other technical issues related to the analysis of high-dimensional data sets. By consequence, reducing the cardinality of data sets (with minimum information loss) has become of interest to virtually any data scientist. Many feature selection algorithms have been introduced in the literature, however, there are two main issues with these. First, the vast majority of such algorithms require labelled samples to learn from. One should note it is often too expensive to label a meaningful amount of data, particularly when dealing with large data sets. Second, these algorithms were not designed to deal with the volume of data we have nowadays. This paper introduces a novel unsupervised feature selection algorithm designed specifically to deal with large data sets. Our experiments demonstrate the superiority of our method

BRS Tropical: cultivar de arroz de ampla adaptação para as várzeas tropicais.

A BRS Tropical é uma cultivar que combina características de arquitetura moderna de planta, resistência ao acamamento, alta capacidade produtiva, grãos de classe longo-fino e de excelentes qualidades industrial e culinária. Indicada para cultivo no sistema de irrigação por inundação nas várzeas tropicais.bitstream/CNPAF-2009-09/27964/1/comt_163.pd

BRS Tropical : cultivar de arroz de ampla adaptação para as várzeas tropicais.

A BRS Tropical é uma cultivar que combina características de arquitetura moderna de planta, resistência ao acamamento, alta capacidade produtiva, grãos de classe longo-fino e de excelentes qualidades industrial e culinária. Indicada para cultivo no sistema de irrigação por inundação nas várzeas tropicais.bitstream/CPAF-RR-2009-09/10885/1/comt_163.pd

BRS Tropical: cultivar de arroz de ampla adaptação para as várzeas tropicais.

O objetivo do presente trabalho é apresentar a BRS Tropical, cultivar que combina características de arquitetura moderna de planta, resistência ao acamamento, alta capacidade produtiva, grãos de classe longo-fino e de excelentes qualidades industrial e culinária

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio

GalNAc-T15 in gastric adenocarcinoma: Characterization according to tissue architecture and cellular location

Gastric cancer (GC) is the second most common cause of cancer-related deaths in the world. This study aims to investigate the differential tissue expression of ppGalNAc-T15 and to evaluate its possible association with clinical-pathological parameters and outcome of gastric adenocarcinoma patients. For these 70 patients were evaluated the expression by immunohistochemistry to ppGalNAc-T15. Our results showed that 33 (47.1%) patients were ppGalNAC-T15+ positive and 37 (52.9%) negative. Positive staining for ppGalNAc-T15 was significantly present in patients older than 60 years (P=0.0306) and submitted to total gastrectomy (P=0.0087). Also, some results remained at the limit of significance as surgical standing (P=0.0562) and histological grade (P=0.0549). Therefore, the ppGalNAc-T15 immunoreactivity can be useful to understand the prognosis of patients with gastric cancer

Sistema de produção: cultivo da bananeira BRS PLATINA.

A Embrapa Mandioca e Fruticultura, situada em Cruz das Almas, Bahia, desde a década de 1970, vem executando pesquisas voltadas para o desenvolvimento e avaliação de novas variedades de bananeira, procurando reunir produtividade, qualidade dos frutos e tolerância ou resistência a pragas e doenças, visando assim aumentar a garantia de sucesso do empreendimento agrícola, sem esquecer a preservação ambiental.bitstream/item/71874/1/Sistema-de-cultivo-da-bananeira-platina.pd

Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

Abstract Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings