Neotenic reproductives influence worker caste differentiation in the termite Reticulitermes speratus (Isoptera; Rhinotermitidae)

Division of labor among castes in social insect colonies increases ergonomic efficiency and colony-level fitness, and has played a key role in the ecological success of social insects. Knowledge of the factors that regulate castes is important for understanding adaptive social organization. Our previous study on the termite Reticulitermes speratus demonstrated that the presence of a pair of nymphoid reproductives during development affected offspring caste ratios. In the present study, we investigated further the influence of individual neotenics on offspring caste ratios. Parthenogenetically-produced offspring were reared in worker-tended experimental colonies with the addition of different forms (nymphoid or ergatoid) and numbers of neotenics, and compared the caste ratios of the offspring between the different experimental treatments. We found that all offspring in worker-only tended colonies became nymphs, while a proportion of offspring in colonies with a single neotenic (with the exception of male ergatoids) differentiated into workers. These results show offspring caste ratios are influenced by the presence of single female ergatoids, single female and male nymphoids, while they remain unaffected by the presence of male ergatoids

The Aging of Biomedical Research in the United States

In the past 30 years, the average age of biomedical researchers has steadily increased. The average age of an investigator at the National Institutes of Health (NIH) rose from 39 to 51 between 1980 and 2008. The aging of the biomedical workforce was even more apparent when looking at first-time NIH grantees. The average age of a new investigator was 42 in 2008, compared to 36 in 1980. To determine if the rising barriers at NIH for entry in biomedical research might impact innovative ideas and research, we analyzed the research and publications of Nobel Prize winners from 1980 to 2010 to assess the age at which their pioneering research occurred. We established that in the 30-year period, 96 scientists won the Nobel Prize in medicine or chemistry for work related to biomedicine, and that their groundbreaking research was conducted at an average age of 41—one year younger than the average age of a new investigator at NIH. Furthermore, 78% of the Nobel Prize winners conducted their research before the age of 51, the average age of an NIH principal investigator. This suggested that limited access to NIH might inhibit research potential and novel projects, and could impact biomedicine and the next generation scientists in the United States

Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities

With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy document collections. One such setting is the Civil Rights Litigation Clearinghouse (CRLC) (https://clearinghouse.net),which posts information about large-scale civil rights lawsuits, serving lawyers, scholars, and the general public. Today, summarization in the CRLC requires extensive training of lawyers and law students who spend hours per case understanding multiple relevant documents in order to produce high-quality summaries of key events and outcomes. Motivated by this ongoing real-world summarization effort, we introduce Multi-LexSum, a collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing. Multi-LexSum presents a challenging multi-document summarization task given the length of the source documents, often exceeding two hundred pages per case. Furthermore, Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence "extreme" summaries to multi-paragraph narrations of over five hundred words). We present extensive analysis demonstrating that despite the high-quality summaries in the training data (adhering to strict content and style guidelines), state-of-the-art summarization models perform poorly on this task. We release Multi-LexSum for further research in summarization methods as well as to facilitate development of applications to assist in the CRLC's mission at https://multilexsum.github.io.Comment: 37 pages, 2 figures, 9 table