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Background: While many investigators have studied symptomatic prostatitis, little research has been done with regard to asymptomatic (NIH-IV) prostatitis. Purpose: To describe the prevalence of and risk factors for NIH-IV prostatitis among a large male population. Methods: The study population was comprised of 1,868 men at the second phase recruitment of a population-based cohort in China. Asymptomatic and symptomatic men were defined by the National Institutes of Health Chronic Prostatitis (CP) Symptom Index. Meanwhile, EPS specimens and their leukocyte count were collected. Lifestyle and demographic characteristics were obtained through a questionnaire. Results: Prevalence of NIH-IV prostatitis was 21.1% among 1,868 asymptomatic men aged 19–78 years and increased with age. After adjusteing for potential confounding variables (age, smoking habits, alcohol drinking habits, education, physical activity, hypertension, dyslipidemia, obesity and diabetes), age remained a significant factor for NIH-IV prostatitis (OR = 1.35; 95% CI = 1.06–1.71; P = 0.01) and the risk of NIH-IV prostatitis was significantly higher in smokers≧15 pack/years than non-smokers (OR = 1.33; 95% CI = 1.01–1.75; P = 0.03). In addition, compared with non-drinkers, the OR of NIH-IV prostatitis in drinkers ≧1 drinks/week was 1.35 (95% CI = 1.03, 1.77, p = 0.02) after adjusting for the other variables above. In addition, having less than a college education may be a risk factor for NIH-IV prostatitis, although a statistically significant difference did not exist in our data (OR = 1.22; 95% CI = 0.97–1.52; P = 0.08). Conclusions: Our findings suggest that NIH-IV prostatitis is prevalent in China. Age, smoking, drinking and lower education levels were associated with an increased risk of NIH-IV prostatitis. The prevalence of NIH-IV prostatitis should be taken into account when estimating the total prevalence of CP in future studies. [ABSTRACT FROM AUTHOR]

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research. [ABSTRACT FROM AUTHOR]