The number of applicants vastly outnumbers the available academic faculty positions. What makes a successful academic job market candidate is the subject of much current discussion. Yet, so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable. It depends on the number of publications, the impact factor (IF) of the journals in which those papers are published, and the number of papers that receive more citations than average for the journal in which they were published. However, both the scientist’s gender and the rank of their university are also of importance, suggesting that non-publication features play a statistically significant role in the academic hiring process. Our model (www.pipredictor.com) allows anyone to calculate their likelihood of becoming a PI.
Cardiovascular disease is the foremost cause of mortality in the world. Among predisposing factors, obesity and metabolic syndrome are growing in prevalence. The pathogenesis of these disorders and of their complications is closely related to insulin resistance, promoting hyperglycemia and dyslipidemia through the loss of insulin action in the liver, skeletal muscle, and adipose tissue. The occurrence of insulin resistance in the myocardium is considered a sign of cardiovascular vulnerability (1,2), reflecting the inability of the heart to switch from the use of fatty acids to that of glucose, which is an oxygen-sparing (i.e., more efficient) substrate. The recent state of knowledge supports the role of oxidative stress in translating these metabolic abnormalities into organ dysfunction. The oxidant pathway is stimulated by insulin resistance and metabolic syndrome–related features, and DNA oxidative damage in peripheral blood cells has been reported in association with these conditions and with heart disease of various origins
In 1961, Mary Lyon first put forth the hypothesis that one X chromosome is inactivated in each cell of the female mammal. As we enter the new millennium and complete 40 years of study, the field of X-inactivation is rich with ideas and many contrasting viewpoints. This review will focus on the random form of X-inactivation and present the latest views on its mechanism. Much attention has been focused on the genetic parsing of X-chromosome counting, choice, silencing and maintenance. It is now known that counting is functionally distinct from choice and that initiation and establishment of silencing are distinct from maintenance. Since Xist’s seminal discovery 10 years ago, significant progress has been made towards understanding its function. Required only for initiation and establishment, Xist must act within a narrow developmental window, but its precise mode of action remains elusive. The ongoing search for Xist RNA-binding factors and effector proteins for silencing has led to members of the macroH2A family of histone variants. Finally, the recent discovery of Tsix implicates regulation of Xist expression by an antisense mechanism. Required for choice but not counting, Tsix blocks Xist RNA accumulation and hence blocks initiation of silencing on the future active X.
Differences in cancer incidences between men and women are often explained by either differences in environmental exposures or by influences of sex hormones. However, there are few studies on intrinsic gender differences in susceptibility to chemical carcinogens. We have analyzed the National Toxicology Program (NTP) database for sex differences in rat responses to chemical carcinogens. We found that the odds that male rat bioassays were assigned a higher level of evidence than female rat bioassays was 1.69 (p
