Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method.

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual's susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals' inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 "healthy" type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of "white" ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals' inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer

Patterns of Charitable Giving in the Center on Philanthropy Panel Study

poster abstractThis study explores the charitable giving patterns of Americans by analyzing data from the 2007 Center on Philanthropy Panel Study (COPPS). The study provides nonprofit sector professionals, fundraisers, policymakers and public officials a unique perspective of families’ giving behaviors over time by estimating giving to religious and secular causes. COPPS is the Center on Philanthropy’s signature research project. Data from COPPS is collected as a module of the Panel Study of Income Dynamics, which reaches more than 8,000 households every two years. COPPS is the only study that surveys giving and volunteering by the same households over time, following families as they mature, face differing economic circumstances and encounter changes in their family size, health and other factors. It is also the only data available that asks families extensively about their wealth and philanthropy, as well as income and other relevant factors. In this study, we probe deeply into the topic of household charitable giving by analyzing the most recent data from the COPPS 2007 wave. We investigate how charitable giving differs by socio-demographic characteristics, such as age, marital status, education level, regions, income, wealth, and others

Prefrontal grey-matter changes in short-term and long-term abstinent methamphetamine abusers

Journal ArticleAuthor's explored grey-matter density in 29 methamphetamine abusers and 20 healthy comparison subjects using voxel-based morphometry. Grey-matter density changes and performances on the Wisconsin Card Sorting test (WCST) were also compared between 11 short-term (<6 months) and 18 long-term (o6 months) abstinent methamphetamine abusers. Methamphetamine abusers had lower grey-matter density in the right middle frontal cortex (corrected p<0.05) and more total errors in the WCST (p<0.01) relative to healthy comparison subjects. Grey-matter density decrease in the right middle frontal cortex correlated with total errors in the WCST in methamphetamine abusers (r=x0.45). Long-term abstinent abusers had significantly less right middle frontal grey-matter density decrease (p<0.01) and total errors in the WCST (p<0.01) than short-term abstinent abusers, but more than the healthy comparison subjects. We report that methamphetamine abusers have prefrontal grey-matter deficit, which may, in part, recover with long-term abstinence

Generalized Numerical Index and Denseness of Numerical Peak Holomorphic Functions on a Banach Space

The generalized numerical index of a Banach space is introduced, and its properties on certain Banach spaces are studied. Ed-dari's theorem on the numerical index is extended to the generalized index and polynomial numerical index of a Banach space. The denseness of numerical strong peak holomorphic functions is also studied