SUMMARY Eukaryotic cells possess an exquisitely interwoven and fine-tuned series of signal transduction mechanisms with which to sense and respond to the ubiquitous fermentable carbon source glucose. The budding yeast Saccharomyces cerevisiae has proven to be a fertile model system with which to identify glucose signaling factors, determine the relevant functional and physical interrelationships, and characterize the corresponding metabolic, transcriptomic, and proteomic readouts. The early events in glucose signaling appear to require both extracellular sensing by transmembrane proteins and intracellular sensing by G proteins. Intermediate steps involve cAMP-dependent stimulation of protein kinase A (PKA) as well as one or more redundant PKA-independent pathways. The final steps are mediated by a relatively small collection of transcriptional regulators that collaborate closely to maximize the cellular rates of energy generation and growth. Understanding the nuclear events in this process may necessitate the further elaboration of a new model for eukaryotic gene regulation, called “reverse recruitment.” An essential feature of this idea is that fine-structure mapping of nuclear architecture will be required to understand the reception of regulatory signals that emanate from the plasma membrane and cytoplasm. Completion of this task should result in a much improved understanding of eukaryotic growth, differentiation, and carcinogenesis.
Despite efforts to promote diversity in the biomedical workforce, there remains a lower rate of funding of National Institutes of Health R01 applications submitted by African-American/black (AA/B) scientists relative to white scientists. To identify underlying causes of this funding gap, we analyzed six stages of the application process from 2011 to 2015 and found that disparate outcomes arise at three of the six: decision to discuss, impact score assignment, and a previously unstudied stage, topic choice. Notably, AA/B applicants tend to propose research on topics with lower award rates. These topics include research at the community and population level, as opposed to more fundamental and mechanistic investigations; the latter tend to have higher award rates. Topic choice alone accounts for over 20% of the funding gap after controlling for multiple variables, including the applicant’s prior achievements. Our findings can be used to inform interventions designed to close the funding gap.
Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at https://icite.od.nih.gov.
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