Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Baseline Characteristics of the 2015-2019 First Year Student Cohorts of the NIH Building Infrastructure Leading to Diversity (BUILD) Program.

ObjectiveThe biomedical/behavioral sciences lag in the recruitment and advancement of students from historically underrepresented backgrounds. In 2014 the NIH created the Diversity Program Consortium (DPC), a prospective, multi-site study comprising 10 Building Infrastructure Leading to Diversity (BUILD) institutional grantees, the National Research Mentoring Network (NRMN) and a Coordination and Evaluation Center (CEC). This article describes baseline characteristics of four incoming, first-year student cohorts at the primary BUILD institutions who completed the Higher Education Research Institute, The Freshmen Survey between 2015-2019. These freshmen are the primary student cohorts for longitudinal analyses comparing outcomes of BUILD program participants and non-participants.DesignBaseline description of first-year students entering college at BUILD institutions during 2015-2019.SettingTen colleges/universities that each received <$7.5mil/yr in NIH Research Project Grants and have high proportions of low-income students.ParticipantsFirst-year undergraduate students who participated in BUILD-sponsored activities and a sample of non-BUILD students at the same BUILD institutions. A total of 32,963 first-year students were enrolled in the project; 64$30,000/yr and 25% were their family's first generation in college.Planned outcomesPrimary student outcomes to be evaluated over time include undergraduate biomedical degree completion, entry into/completion of a graduate biomedical degree program, and evidence of excelling in biomedical research and scholarship.ConclusionsThe DPC national evaluation has identified a large, longitudinal cohort of students with many from groups historically underrepresented in the biomedical sciences that will inform institutional/national policy level initiatives to help diversify the biomedical workforce

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting