Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

Comparative analysis of thermal adaptations of extremophilic prolyl oligopeptidases.

Prolyl oligopeptidases from psychrophilic, mesophilic, and thermophilic organisms found in a range of natural environments are studied using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis to determine how the S9 protease family adapts to extreme thermal conditions. We compare our results with hypotheses from the literature regarding structural adaptations that allow proteins to maintain structure and function at extreme temperatures, and we find that, in the case of prolyl oligopeptidases, only a subset of proposed adaptations are employed for maintaining stability. The catalytic and propeller domains are highly structured, limiting the range of mutations that can be made to enhance hydrophobicity or form disulfide bonds without disrupting the formation of necessary secondary structure. Rather, we observe a pattern in which overall prevalence of bound interactions (salt bridges and hydrogen bonds) is conserved by using increasing numbers of increasingly short-lived interactions as temperature increases. This suggests a role for an entropic rather than energetic strategy for thermal adaptation in this protein family.

Continuous Time Graph Processes with Known ERGM Equilibria: Contextual Review, Extensions, and Synthesis.

Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that do so. These include subfamilies of the well-known stochastic actor oriented models, as well as continuum extensions of temporal and separable temporal exponential family random graph models. We also comment on some related threads in the broader work on network dynamics, which provide additional context for the continuous time case. Graph processes that unfold in continuous time are natural models for social network dynamics: able to directly represent changes in structure as they unfold (rather than, e.g. as snapshots at discrete intervals), such models not only offer the promise of capturing dynamics at high temporal resolution, but are also easily mapped to empirical data without the need to preselect a level of granularity with respect to which the dynamics are defined. Although relatively few general frameworks of this type have been extensively studied, at least one (the stochastic actor-oriented models, or SAOMs) is arguably among the most successful and widely used families of models in the social sciences (see, e.g., Snijders (2001); Steglich et al. (2010); Burk et al. (2007); Sijtsema et al. (2010); de la Haye et al. (2011); Weerman (2011); Schaefer and Kreager (2020) among many others). Work using other continuous time graph processes has also found applications both within (Koskinen and Snijders, 2007; Koskinen et al., 2015; Stadtfeld et al., 2017; Hoffman et al., 2020) and beyond (Grazioli et al., 2019; Yu et al., 2020) the social sciences, suggesting the potential for further advances.

California Exodus? A Network Model of Population Redistribution in the United States.

Motivated by debates about California's net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow networks in the United States. We introduce a protocol that visualizes the complex effects of potential underlying mechanisms, and perform in silico knockout experiments to quantify their contribution to the California Exodus. We find that racial dynamics contribute to the California Exodus, urbanization ameliorates it, and political climate and housing costs have little impact. Moreover, the severity of the California Exodus depends on how one measures it, and California is not the state with the most substantial population loss. The paper demonstrates how generative statistical models can provide mechanistic insights beyond simple hypothesis-testing.

Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease.

The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.

Gene expression reveals immune response strategies of naïve Hawaiian honeycreepers experimentally infected with introduced avian malaria.

The unprecedented rise in the number of new and emerging infectious diseases in the last quarter century poses direct threats to human and wildlife health. The introduction to the Hawaiian archipelago of Plasmodium relictum and the mosquito vector that transmits the parasite has led to dramatic losses in endemic Hawaiian forest bird species. Understanding how mechanisms of disease immunity to avian malaria may evolve is critical as climate change facilitates increased disease transmission to high elevation habitats where malaria transmission has historically been low and the majority of the remaining extant Hawaiian forest bird species now reside. Here, we compare the transcriptomic profiles of highly susceptible Hawai'i 'amakihi (Chlorodrepanis virens) experimentally infected with P. relictum to those of uninfected control birds from a naïve high elevation population. We examined changes in gene expression profiles at different stages of infection to provide an in-depth characterization of the molecular pathways contributing to survival or mortality in these birds. We show that the timing and magnitude of the innate and adaptive immune response differed substantially between individuals that survived and those that succumbed to infection, and likely contributed to the observed variation in survival. These results lay the foundation for developing gene-based conservation strategies for Hawaiian honeycreepers by identifying candidate genes and cellular pathways involved in the pathogen response that correlate with a bird's ability to recover from malaria infection.

Oligomeric complexes formed by Redß single strand annealing protein in its different DNA bound states.

Redß is a single strand annealing protein from bacteriophage λ that binds loosely to ssDNA, not at all to pre-formed dsDNA, but tightly to a duplex intermediate of annealing. As viewed by electron microscopy, Redß forms oligomeric rings on ssDNA substrate, and helical filaments on the annealed duplex intermediate. However, it is not clear if these are the functional forms of the protein in vivo. We have used size-exclusion chromatography coupled with multi-angle light scattering, analytical ultracentrifugation and native mass spectrometry (nMS) to characterize the size of the oligomers formed by Redß in its different DNA-bound states. The nMS data, which resolve species with the highest resolution, reveal that Redß forms an oligomer of 12 subunits in the absence of DNA, complexes ranging from 4 to 14 subunits on 38-mer ssDNA, and a much more distinct and stable complex of 11 subunits on 38-mer annealed duplex. We also measure the concentration of Redß in cells active for recombination and find it to range from 7 to 27 µM. Collectively, these data provide new insights into the dynamic nature of the complex on ssDNA, and the more stable and defined complex on annealed duplex.

Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

The virtual knee clinic - A tool to streamline new outpatient referrals.

INTRODUCTION: Traditionally it has been the case for orthopaedic consultants to review GP referrals for the orthopaedic outpatient clinic where possible in amongst other clinical commitments. This could sometimes lead to unsuitable patients being reviewed and both patients and clinicians becoming frustrated. Building on the virtual fracture clinic, a new screening tool was implemented to streamline new referrals. The aim of this study is to investigate the change in patients given outpatient appointments following the introduction of a new streamlining protocol. METHODS: Referrals had to meet the criteria of BMI under 40 or evidence of weight loss effort, recent radiographs and appropriate clinical details in keeping with Getting It Right First Time (GIRFT). Consultant were given dedicated clinical time to review and either triage the patient to the most appropriate clinic type, or return the referral with advice to the GP. 10 months of data was collected prior to the protocol and 10 months after implementation. RESULTS: 1781 patients were referred pre-protocol with an average of 14.2% of these being returned. Post protocol there were 2110 patients referred with 31.2% returned. There was an increase in 195% of referrals returned to the GP (p < 0.0001). The highest proportion of these was for mild to moderate osteoarthritis on the radiograph which has been proven to be unsuitable for intervention. At 12 month analysis there was no significant increase in patients re-referred to the service (p = 0.53) DISCUSSION: The new screening tool allows more appropriate referrals to be seen in clinic allowing less frustration to clinicians and patients by reducing therapeutic inertia. Furthermore it allows new referrals to be seen by the most appropriate sub-specialist. It allows advice to be given to GPs on further management for the patient. 619 appointments were saved. At a cost of £120 per appointment, this leads to a real terms cost saving of £74,280, with further savings in time and travel.

An Evaluation of Tertiary Drug Resources' Consistency Regarding Drug-Drug Interactions Between Tricyclic Antidepressants and Herbal Supplements.

Background: Tertiary drug information resources are utilized frequently by health care providers. While pharmacists are uniquely trained and prepared to interpret the information available on these resources, including the results of drug-drug interaction evaluations, discrepancies between such resources pose a major concern for clinicians with regard to patient safety and medication regimen efficacy. It was postulated that drug-drug interaction evaluations between prescription medications and over-the-counter herbal supplements would be particularly problematic. Objective: The objective of this project was to distinguish the discrepancies between tertiary drug information resources in the setting of drug-drug interactions between tricyclic antidepressants (TCAs) and herbal supplements. Methods: The following medications and herbal supplements were evaluated on Lexicomp, Micromedex, and Medscape: amitriptyline, nortriptyline, doxepin, imipramine, desipramine, amoxapine, St. John's Wort, valerian root, ginkgo biloba, and ginseng. Results: While all of the tertiary drug information resources identified a significant reaction between each TCA and St. John's Wort due to the risk of serotonin syndrome, several other discrepancies were noted, with regard to both the severity of the interaction indicated and whether or not an interaction was identified. Conclusion: It is imperative that clinicians be aware of potential discrepancies between tertiary drug information resources, including the potential for variation in both the clinical interpretation of its severity and the recognition of an interaction.

Insight into Selecting Adolescents for Drinking Intervention Programs: a Simulation Based on Stochastic Actor-Oriented Models.

Adolescent drinking remains a prominent public health and socioeconomic issue in the USA with costly consequences. While numerous drinking intervention programs have been developed, there is little guidance whether certain strategies of participant recruitment are more effective than others. The current study aims at addressing this gap in the literature using a computer simulation approach, a more cost-effective method than employing actual interventions. We first estimate stochastic actor-oriented models for two schools from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We then employ different strategies for selecting adolescents for the intervention (either based on their drinking levels or their positions in the school network) and simulate the estimated model forward in time to assess the aggregated level of drinking in the school at a later time point. The results suggest that selecting moderate or heavy drinkers for the intervention produces better results compared to selecting casual or light drinkers. The intervention results are improved further if network position information is taken into account, as selecting drinking adolescents with higher in-degree or higher eigenvector centrality values for intervention yields the best results. Results from this study help elucidate participant selection criteria and targeted network intervention strategies for drinking intervention programs in the USA.

Stabilization of Alfvén Eigenmodes in DIII-D via Controlled Energetic Ion Density Ramp and Validation of Theory and Simulations.

Fast-ion driven Alfvén waves with frequency close to the ion cyclotron frequency (f=0.58f_{ci}) excited by energetic ions from a neutral beam are stabilized via a controlled energetic ion density ramp for the first time in a fusion research plasma. The scaling of wave amplitude with injection rate is consistent with theory for single mode collisional saturation near marginal stability. The wave is identified as a shear-polarized global Alfvén eigenmode excited by Doppler-shifted cyclotron resonance with fast ions with sub-Alfvénic energetic ions, a first in fusion research plasmas.

Bayesian estimation of the hydroxyl radical diffusion coefficient at low temperature and high pressure from atomistic molecular dynamics.

The hydroxyl radical is the primary reactive oxygen species produced by the radiolysis of water and is a significant source of radiation damage to living organisms. Mobility of the hydroxyl radical at low temperatures and/or high pressures is hence a potentially important factor in determining the challenges facing psychrophilic and/or barophilic organisms in high-radiation environments (e.g., ice-interface or undersea environments in which radiative heating is a potential heat and energy source). Here, we estimate the diffusion coefficient for the hydroxyl radical in aqueous solution using a hierarchical Bayesian model based on atomistic molecular dynamics trajectories in TIP4P/2005 water over a range of temperatures and pressures.

Forgotten fatalities: British military, mining and maritime accidents since 1900.

BACKGROUND: Comparative long-term trends in fatal accident rates in the UK's most hazardous occupations have not been reported. AIMS: To compare trends in fatal accident rates in six of the most hazardous occupations (the three armed forces, merchant shipping, sea fishing and coal mining) and the general British workforce during peacetime years since 1900. METHODS: Examinations of annual mortality reports, returns, inquiry files and statistics. The main outcome measure was the fatal accident rate per 100 000 population employed. RESULTS: These six occupations accounted for ~40% of all fatal accidents in the British workforce. Fatal accident rates were highest in merchant shipping to 1914 (400-600 per 100 000) and in the Royal Air Force and sea fishing by the early 1920s (around 300 per 100 000). Since the 1950s sea fishing has remained the most hazardous occupation (50-200). Widespread reductions in fatal accident rates for each occupation have been greatest in recent years in the three armed forces and merchant shipping. Compared with the general workforce, relative risks of fatalities have increased in recent decades in all these occupations except shipping. CONCLUSIONS: All six occupations still have high fatal accident rates. The greatly increased fatalities in sea fishing generally and in the Royal Air Force during its early years reflect, for different reasons, cultures of extreme risk-taking in these two sectors. Reductions in fatality rates in the armed forces over the last 20 years are due largely to decreases in land transport accidents.

Mutational Analysis of Redß Single Strand Annealing Protein: Roles of the 14 Lysine Residues in DNA Binding and Recombination In Vivo.

Redß is a 261 amino acid protein from bacteriophage λ that promotes a single-strand annealing (SSA) reaction for repair of double-stranded DNA (dsDNA) breaks. While there is currently no high-resolution structure available for Redß, models of its DNA binding domain (residues 1-188) have been proposed based on homology with human Rad52, and a crystal structure of its C-terminal domain (CTD, residues 193-261), which binds to λ exonuclease and E. coli single-stranded DNA binding protein (SSB), has been determined. To evaluate these models, the 14 lysine residues of Redß were mutated to alanine, and the variants tested for recombination in vivo and DNA binding and annealing in vitro. Most of the lysines within the DNA binding domain, including K36, K61, K111, K132, K148, K154, and K172, were found to be critical for DNA binding in vitro and recombination in vivo. By contrast, none of the lysines within the CTD, including K214, K245, K251, K253, and K258 were required for DNA binding in vitro, but two, K214 and K253, were critical for recombination in vivo, likely due to their involvement in binding to SSB. K61 was identified as a residue that is critical for DNA annealing, but not for initial ssDNA binding, suggesting a role in binding to the second strand of DNA incorporated into the complex. The K148A variant, which has previously been shown to be defective in oligomer formation, had the lowest affinity for ssDNA, and was the only variant that was completely non-cooperative, suggesting that ssDNA binding is coupled to oligomerization.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

Molecular Mechanism of Aggregation of the Cataract-Related γD-Crystallin W42R Variant from Multiscale Atomistic Simulations.

The mechanisms leading to aggregation of the crystallin proteins of the eye lens remain largely unknown. We use atomistic multiscale molecular simulations to model the solution-state conformational dynamics of γD-crystallin and its cataract-related W42R variant at both infinite dilution and physiologically relevant concentrations. We find that the W42R variant assumes a distinct conformation in solution that leaves the Greek key domains of the native fold largely unaltered but lacks the hydrophobic interdomain interface that is key to the stability of wild-type γD-crystallin. At physiologically relevant concentrations, exposed hydrophobic regions in this alternative conformation become primary sites for enhanced interprotein interactions leading to large-scale aggregation.

Celebrity Cancer on Twitter: Mapping a Novel Opportunity for Cancer Prevention.

Social media platforms have the potential to facilitate the dissemination of cancer prevention and control messages following celebrity cancer diagnoses. However, cancer communicators have yet to systematically leverage these naturally occurring interventions on social media as these events are difficult to identify as they are unfolding and little research has analyzed their effect on social media conversations. In this study, we add to the research by analyzing how a celebrity cancer announcement influenced Twitter conversations in terms of the volume of social media messages and the type of content. Over a 9-day period, during which actor Ben Stiller announced that he had been treated for prostate cancer, we collected 1.2 million Twitter messages about cancer. We conducted automated content analyses to identify how often common cancer sites (prostate, breast, colon, or lung) were discussed. Then, we used manual content analysis on a sample of messages to identify cancer continuum content (awareness, prevention, early detection, diagnosis, treatment, survivorship, and end of life). Chi-square analyses were implemented to evaluate changes in cancer site and cancer continuum content before and after the announcement. We found that messages related to prostate cancer increased significantly more than expected for 2 days following Stiller's announcement. However, the number of cancer messages that described other cancer locations either did not increase or did not increase by the same magnitude. In terms of message content, results showed larger than expected increases in diagnosis messages. These results suggest opportunities to shape social media conversations following celebrity cancer announcements and increase prevention and early detection messages.

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common "black box" type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations.

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.