Summary The Distance Constraint Model (DCM) is an ensemble-based biophysical model that integrates thermodynamic and mechanical viewpoints of protein structure. The DCM outputs a large number of structural characterizations that collectively allow for Quantified Stability/Flexibility Relationships (QSFR) to be identified and compared across protein families. Using five metallo-β-lactamases (MBLs) as a representative set, we demonstrate how QSFR properties are both conserved and varied across protein families. Similar to our characterizations on other protein families, the backbone flexibility of the five MBLs are overall visually conserved, yet there are interesting specific quantitative differences. For example, the plasmid-encoded NDM-1 enzyme, which leads to a fast spreading drug-resistant version of Klebsiella Pneumoniae, has several regions of significantly increased rigidity relative to the other four. In addition, the set of intramolecular couplings within NDM-1 are also atypical. While long-range couplings frequently vary significantly across protein families, NDM-1 is distinct because it has limited correlated flexibility, which is isolated within the active site S3/S4 and S11/H6 loops. These loops are flexibly correlated in the other members, suggesting it is important to function, but the others also have significant amounts of correlated flexibility throughout the rest of their structures.

This paper reports on results of idealized numerical simulations testing the influence of low-level humidity, and thus lifting condensation level (LCL), on the morphology and evolution of low-level rotation in supercell thunderstorms. Previous studies have shown that the LCL can influence outflow buoyancy, which can in turn affect generation and stretching of near-surface vertical vorticity. A less explored hypothesis is tested: that the LCL affects the relative positioning of near-surface circulation and the overlying mesocyclone, thus influencing the dynamic lifting and intensification of near-surface vertical vorticity. To test this hypothesis, a set of three base-state thermodynamic profiles with varying LCLs are implemented and compared over a variety of low-level wind profiles. The thermodynamic properties of the simulations are sensitive to variations in the LCL, with higher LCLs contributing to more negatively buoyant cold pools. These outflow characteristics allow for a more forward propagation of near-surface circulation relative to the midlevel mesocyclone. When the mid- and low-level mesocyclones become aligned with appreciable near-surface circulation, favorable dynamic updraft forcing is able to stretch and intensify this rotation. The strength of the vertical vorticity generated ultimately depends on other interrelated factors, including the amount of near-surface circulation generated within the cold pool and the buoyancy of storm outflow. However, these simulations suggest that mesocyclone alignment with near-surface circulation is modulated by the ambient LCL, and is a necessary condition for the strengthening of near-surface vertical vorticity. This alignment is also sensitive to the low-level wind profile, meaning that the LCL most favorable for the formation of intense vorticity may change based on ambient low-level shear properties.

This study investigates relationships between climate-scale patterns and seasonal tornado outbreaks across the southeastern United States. Time series of several daily climate indices—including Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Pacific–North American (PNA) pattern, east/west Pacific Oscillation (EPO/WPO), and both raw and detrended Gulf of Mexico SST anomalies (SSTA/SSTAD)—are collected in advance of Southeast severe convective days and grouped using self-organizing maps (SOMs). Spatiotemporal distributions of storm reports within nodes are compared to seasonal climatology, and the evolution of the regional environment for nodes associated with outbreaks is analyzed to provide physical justification for such associations. This study confirms findings from several tornado-related climate studies in the literature, while also identifying a number of new patterns associated with Southeast tornado outbreaks. Both the AO and NAO are relevant across all seasons, especially on lead time scales of 1–2 months, while SSTA/SSTADs are relevant on smaller time scales. The physical connection between these patterns and the regional storm environment is largely related to alterations of upper-level circulation and jet stream patterns, which in turn influence deep- and low-level shear, inland transport of moisture and instability, and other regional characteristics pertinent to tornado outbreaks. These results suggest that climate-scale variability can modulate and potentially be used to predict regional storm environments and their likelihood to produce tornado outbreaks across the Southeast.

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Prominent voices have called for a better way to measure, predict, and adjust for social factors in healthcare and population health. Local area characteristics are sometimes framed as a proxy for patient characteristics, but they are often independently associated with health outcomes. We have developed an “artificially intelligent” approach to risk adjustment for local social determinants of health (SDoH) using random forest models to understand life expectancy at the Census tract level. Our Local Social Inequity score draws on more than 150 neighborhood-level variables across 10 SDoH domains. As piloted in Ohio, the score explains 73 percent of the variation in life expectancy by Census tract, with a mean squared error of 4.47 years. Accurate multidimensional, cross-sector, small-area social risk scores could be useful in understanding the impact of healthcare innovations, payment models, and SDoH interventions in communities at higher risk for serious illnesses and diseases; identifying neighborhoods and areas at highest risk of poor outcomes for better targeting of interventions and resources; and accounting for factors outside of providers’ control for more fair and equitable performance/quality measurement and reimbursement.