Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.
We investigated the acid-base condition of arterial and mixed venous blood during cardiopulmonary resuscitation in 16 critically ill patients who had arterial and pulmonary arterial catheters in place at the time of cardiac arrest. During cardiopulmonary resuscitation, the arterial blood pH averaged 7.41, whereas the average mixed venous blood pH was 7.15 (P less than 0.001). The mean arterial partial pressure of carbon dioxide (PCO2) was 32 mm Hg, whereas the mixed venous PCO2 was 74 mm Hg (P less than 0.001). In a subgroup of 13 patients in whom blood gases were measured before, as well as during, cardiac arrest, arterial pH, PCO2, and bicarbonate were not significantly changed during arrest. However, mixed venous blood demonstrated striking decreases in pH (P less than 0.001) and increases in PCO2 (P less than 0.004). We conclude that mixed venous blood most accurately reflects the acid-base state during cardiopulmonary resuscitation, especially the rapid increase in PCO2. Arterial blood does not reflect the marked reduction in mixed venous (and therefore tissue) pH, and thus arterial blood gases may fail as appropriate guides for acid-base management in this emergency.
The beneficial effects of moderate-intensity exercise on cardiorespiratory fitness and body composition are well documented, with the greatest health benefits reported in sedentary individuals who engage in moderate levels of exercise. The published literature contains no quantification of the threshold of lower limits of beneficial exercise or estimates of benefits derived from lower exercise levels. The specific aim of this study was to compare the effects of two walking frequencies, holding intensity and duration constant, on blood lipids, body composition, and exercise maintenance regimens of Mexican American women. A quasi-experimental design, with two treatment groups and one comparison group, was used to explore the dose-response effects of low-intensity exercise on cardiovascular outcomes. Significant interactions were found between walking and total serum cholesterol and skin-fold sums. This study demonstrated the clinical efficacy of a low-intensity exercise regimen on cardiovascular risk factors and exercise adherence.
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