Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.

This study explored the effects of different human milk oligosaccharides (HMOs), solely and in combination, on gut microbiota composition and metabolic activity (organic acid production), using anaerobic in vitro batch culture fermenters. The aim was to compare prebiotic effects of HMOs (2’FL, 3’FL, 3’SL, 6’SL, LNT, LNnT, and 1:1 ratio mixes of 2’FL/3’SL and 3’SL/LNT) in faecal samples from irritable bowel syndrome (IBS) donors and healthy controls, and to determine the best-performing HMO in IBS. Fluorescent in situ hybridisation coupled with flow cytometry was utilised to study microbiota changes in major colonic genera, and organic acid production was assessed by gas chromatography. IBS donors had different starting microbial profiles compared to healthy controls and lower levels of organic acids. In response to HMOs, there were alterations in both the control and IBS faecal microbiomes. In IBS donor fermenters, Bifidobacterium, Faecalibacterium, total bacterial numbers, and organic acid production significantly increased post-HMO intervention. When comparing the effect of HMO interventions on the microbiota and organic acid production, a mix of 3’SL/LNT HMOs may be the most promising intervention for IBS patients.

Experimental methods are currently being extensively used to elicit subjective values for commodities and projects. Three methodological problems are not systematically addressed in this emerging literature. The first is the potential for laboratory responses to be censored by field opportunities, so that lab responses can be confounded by uncontrolled knowledge of the field; the second is the potential for subjective perceptions about field opportunities, and hence valuation responses, to be affected by the institution used to elicit values; and the third is the potential for some elicitation institutions to influence subjective perceptions of characteristics of the commodity or project being valued, and hence change the very commodity being valued. All three problems result in potential loss of control over the value elicitation process. For example, we show that censoring affects conclusions drawn in a major study of beef packaging valuation. We derive implications for experimental designs that minimize the potential effect of these methodological problems.

Converting knowledge from basic research into innovations that improve clinical care requires a specialized workforce that converts a laboratory invention into a product that can be developed and tested for clinical use. As the mandate to demonstrate more real-world impact from the national investment in research continues to grow, the demand for staff that specialize in product development and clinical trials continues to outpace supply. In this study, two academic medical institutions in the greater Houston–Galveston region termed this population the “bridge and clinical research professional” (B + CRP) workforce and assessed its turnover before and after the onset of the COVID-19 pandemic . Both institutions realized growth (1.2 vs 2.3-fold increase) in B + CRP-specific jobs from 2017 to 2022. Turnover increased 1.5–2-fold after the onset of the pandemic but unlike turnover in the larger clinical and translational research academic workforce, the instability did not resolve by 2022. These results are a baseline measurement of the instability of our regional B + CRP workforce and have informed the development of a regional alliance of universities, academic medical centers, and economic development organizations in the greater Houston–Galveston region to increase this highly specialized and skilled candidate pool.

The Minnesota Longitudinal Study of Risk and Adaptation (MLSRA) is a landmark prospective, longitudinal study of human development focused on a sample of mothers experiencing poverty and their firstborn children. Although the MLSRA pioneered a number of important topics in the area of social and emotional development, it began with the more specific goal of examining the antecedents of child maltreatment. From that foundation and for more than 40 years, the study has produced a significant body of research on the origins, sequelae, and measurement of childhood abuse and neglect. The principal objectives of this report are to document the early history of the MLSRA and its contributions to the study of child maltreatment and to review and summarize results from the recently updated childhood abuse and neglect coding of the cohort, with particular emphasis on findings related to adult adjustment. While doing so, we highlight key themes and contributions from Dr Dante Cicchetti’s body of research and developmental psychopathology perspective to the MLSRA, a project launched during his tenure as a graduate student at the University of Minnesota.