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Carbon isotopic analysis is a useful tool for investigating paleoenvironments, as the pedogenic carbonate δ13C is related to δ13CSOM and to the proportions of C3/C4 plants. In this work we interpreted the paleoenvironmental conditions at the time of carbonate precipitation in soils formed under different climates and during different geological ages. Samples were taken from a Bk (PR1, Holocene) and from two Bkm horizons (PR2 and PR3, Pleistocene). When the mean δ13C plant values and the most plausible paleotemperatures were used in the evaluation, PR1 showed a lower percentage of C4 plants (48%) than Pleistocene soils (~53%), in agreement with paleoclimate changes. When instead the δ13C values of current plants were used for PR1, C4 plants ranged from 59 (12°C) to 66% (18°C), suggesting two possible interpretations: either plant species changed during the Holocene, or the plant mean values normally used in the literature are not suitable for Pleistocene reconstructions

PyPartMC is a Pythonic interface to PartMC, a stochastic, particle-resolved aerosol model implemented in Fortran. Both PyPartMC and PartMC are free, libre, and open-source. PyPartMC reduces the number of steps and mitigates the effort necessary to install and utilize the resources of PartMC. Without PyPartMC, setting up PartMC requires: working with UNIX shell, providing Fortran and C libraries, and performing standard Fortran and C source code configuration, compilation and linking. This can be challenging for those less experienced with computational research or those intending to use PartMC in environments where provision of UNIX tools is less straightforward (e.g., on Windows). PyPartMC offers a single-step installation/upgrade process of PartMC and all dependencies through the pip Python package manager on Linux, macOS, and Windows. This allows streamlined access to the unmodified and versioned Fortran internals of the PartMC codebase from both Python and other interoperable environments (e.g., Julia through PyCall). In particular, PyPartMC can be set up to handle the time-stepping loop for PartMC simulations making it possible to couple PartMC with other Python-interoperable packages, for either online diagnostics or additional simulation logic. Altogether, users of PyPartMC can setup, run, process and visualize output of PartMC simulations using a single general-purpose programming language.

Several organic geochemical proxies within a sediment core from Lake Petén Itzá, Guatemala, suggest intense human-induced degradation of the aquatic environment during the Maya Preclassic period from around 700 BCE to 200 CE

Calculations of the aerosol direct effect on climate rely on simulated aerosol fields. The model representation of aerosol mixing state potentially introduces large uncertainties into these calculations, since the simulated aerosol optical properties are sensitive to mixing state. In this study, we systematically quantified the impact of aerosol mixing state on aerosol optical properties using an ensemble of 1800 aerosol populations from particle-resolved simulations as a basis for Mie calculations for optical properties. Assuming the aerosol to be internally mixed within prescribed size bins caused overestimations of aerosol absorptivity and underestimations of aerosol scattering. Together, these led to errors in the populations' single scattering albedo of up to −22.3 % with a median of −0.9 %. The mixing state metric χ proved useful in relating errors in the volume absorption coefficient, the volume scattering coefficient and the single scattering albedo to the degree of internally mixing of the aerosol, with larger errors being associated with more external mixtures. At the same time, a range of errors existed for any given value of χ. We attributed this range to the extent to which the internal mixture assumption distorted the particles' black carbon content and the refractive index of the particle coatings. Both can vary for populations with the same value of χ. These results are further evidence of the important yet complicated role of mixing state in calculating aerosol optical properties.

The influence of climate on premodern civil conflict and societal instability is debated. Here, the authors combine archeological, historical, and paleoclimatic datasets to show that drought between 1400-1450 cal. CE escalated civil conflict at Mayapan, the largest Postclassic Maya capital of the Yucatán Peninsula.

A flexible treatment for gas- and aerosol-phase chemical processes has been developed for models of diverse scale, from box models up to global models. At the core of this novel framework is an “abstracted aerosol representation” that allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations (e.g., sectional, modal, or particle-resolved). This is accomplished by treating aerosols as a collection of condensed phases that are implemented according to the aerosol representation of the host model. The framework also allows multiple chemical processes (e.g., gas- and aerosol-phase chemical reactions, emissions, deposition, photolysis, and mass transfer) to be solved simultaneously as a single system. The flexibility of the model is achieved by (1) using an object-oriented design that facilitates extensibility to new types of chemical processes and to new ways of representing aerosol systems, (2) runtime model configuration using JSON input files that permits making changes to any part of the chemical mechanism without recompiling the model (this widely used, human-readable format allows entire gas- and aerosol-phase chemical mechanisms to be described with as much complexity as necessary), and (3) automated comprehensive testing that ensures stability of the code as new functionality is introduced. Together, these design choices enable users to build a customized multiphase mechanism without having to handle preprocessors, solvers, or compilers. Removing these hurdles makes this type of modeling accessible to a much wider community, including modelers, experimentalists, and educators. This new treatment compiles as a stand-alone library and has been deployed in the particle-resolved PartMC model and in the Multiscale Online AtmospheRe CHemistry (MONARCH) chemical weather prediction system for use at regional and global scales. Results from the initial deployment to box models of different complexity and MONARCH will be discussed, along with future extension to more complex gas–aerosol systems and the integration of GPU-based solvers.

Abstract Background The COVID-19 pandemic has considerably disrupted nearly all aspects of daily life, including healthcare delivery and clinical research. Because pragmatic clinical trials are often embedded within healthcare delivery systems, they may be at high risk of disruption due to the dual impacts on the conduct of both care and research. Methods We collected qualitative data using multiple methods to characterize the impact of COVID-19 on the research activities of 14 active pragmatic clinical trials in the National Institutes of Health (NIH) Health Care Systems Research Collaboratory. A COVID-19 impact questionnaire was administered electronically to principal investigators in June 2020. Text responses were analyzed thematically, and qualitative summaries were subsequently reviewed by five independent reviewers, who made iterative revisions. Additional COVID-19-related impacts were identified during virtual meetings with trial teams during April–July 2020 and combined with questionnaire responses for analysis. Results Impacts of the pandemic were broadly classified into two main types: healthcare operations and social distancing. In some instances, trial delays created statistical challenges, particularly with trials using stepped-wedge designs, and necessitated changing data collection strategies or modifying interventions. The majority of projects used existing stakeholder-driven approaches to adapt interventions. Several benefits of these adaptions were identified, including expanded outreach capabilities and ability to study virtual intervention delivery. All trial teams were able to adapt to pandemic-related modifications. Conclusion In a group of 14 ongoing pragmatic clinical trials, there was significant impact of COVID-19 on trial activities. Engaging appropriate stakeholders was critical to designing and implementing trial modifications and making continued safe progress toward meeting research objectives.

Changing climate and land use activity are altering inputs of colored dissolved organic carbon (cDOC) into lakes. Increased cDOC reduces water transparency (browning) and changes lake physicochemistry, with biological consequences. Identifying the drivers and effects of changing cDOC inputs is critical for mitigating the consequences of climate change on lake ecosystems through adaptive watershed management. This study focused on determining the drivers of lake browning by evaluating shifts in diatom assemblages in subtropical, oligotrophic Lake Annie (FL, United States), which is known to experience climate-driven oscillations in transparency associated with watershed inputs of cDOC. We combined long-term limnological monitoring data and paleolimnological techniques to determine how diatoms respond to changes in cDOC and to infer past cDOC fluctuations relative to records of past climate and land use changes in the watershed. Diatom assemblage composition in a 14-year phytoplankton dataset was strongly correlated with cDOC-driven transparency fluctuations. Likewise, diatom assemblages in the upper 35 cm of the sediment core, which corresponded to a 35-year lake monitoring record, were also strongly related to past water transparency, yielding a strong transfer function (paleo model, R2 = 0.72). When the model was applied to the diatom record from a 166-cm sediment core, diatom-inferred transparency and rates of nutrient input showed that localized ditching in the 1930s enhanced the effect of climate oscillations on water transparency, intensifying cyclical browning thereafter. Integration of long-term monitoring and paleoecological data provided valuable insights into the history of the aquatic ecosystem, enabling implementation of adaptive management strategies to contend with a changing climate.

In mammals, the cytokine hormone leptin promotes wound healing by increasing inflammation, cellular recruitment, angiogenic regrowth, and re-epithelialization; however, it is not known whether leptin has conserved actions on wound healing in other vertebrates. Here, we tested the hypothesis that leptin promotes both the quality and speed of wound healing in the South African clawed frog, Xenopus laevis. First, fluorescent immunohistochemistry using a polyclonal antibody specific to Xenopus leptin showed that in juvenile dorsal skin, leptin protein is expressed in the dorsal epidermal layer, as well in blood vessel endothelial cells and sensory nerves that run along the base of the dermis. Injection of recombinant Xenopus leptin (rXleptin) stimulates phosphorylated STAT3 (pSTAT3), indicative of leptin-activated JAK/STAT signaling in the epidermis. Similar to mammals, leptin protein expression increases at the wound site after injury of the epidermis. We then cultured “punch-in-a-punch” full-thickness dorsal skin explants in three doses of rXleptin (0, 10, and 100 ng/ml) and showed that leptin treatment doubled the rate of wound closure after 48 h relative to skin punches cultured without leptin. Food restriction prior to wound explant culture reduced the amount of wound closure, but leptin injection prior to euthanasia rescued closure to similar control levels. Leptin treatment also significantly reduced bacterial infection of these epidermal punches by 48 h in culture. This study shows that leptin is likely an endogenous promoter of wound healing in amphibians. Leptin-based therapies have the potential to expedite healing and reduce the incidence of secondary infections without toxicity issues, the threat of antibiotic resistance, or environmental antibiotic contamination. The conservation of leptin’s actions on wound healing also suggests that it may have similar veterinary applications for other exotic species.

Abstract Background The electronic health record (EHR) contains a wealth of clinical data that may be used to streamline the identification of potential clinical trial participants. However, there is little empirical information on site-level facilitators of and barriers to optimal use of EHR systems with respect to trial recruitment. Methods We conducted qualitative focus groups and quantitative surveys as part of the EHR Ancillary Study, which is being conducted alongside the multicenter, global, Harmony Outcomes Trial comparing albiglutide to standard care for the prevention of cardiovascular events in type 2 diabetes. Subject matter experts used findings from focus groups to draft a 20-question survey examining the use of the EHR for participant identification, common site recruitment strategies, and variation in perceived barriers to optimal use of the EHR. The final survey was fielded with 446 site investigators actively enrolling participants in the main trial. Results Nearly two-thirds of respondents were study coordinators (63.2%), 23.1% were principal investigators, and 13.7% held other research roles. Approximately half of the respondents reported using the EHR to find potential trial participants. Of these, 79.4% reported using EHR searches in conjunction with other recruitment methods, including reviewing of upcoming clinic schedules (75.3%) and contacting past trial participants (71.2%). Important barriers to optimal use of the EHR included the lack of availability of certain research-focused EHR modules and limitations on the ability to contact patients cared for by other providers. Of survey respondents who did not use the EHR to find potential participants, one-quarter reported that the EHR was not accessible in their country; this finding varied from 2.6% of respondents in North America to 50% of respondents in the Asia Pacific. Conclusions While EHR screening was commonly used for recruitment in a cardiovascular outcomes trial, important technical, governance, and regulatory barriers persist. Multifaceted, scalable, and customizable strategies are needed to support the optimal use of the EHR for trial participant identification. Trial registration ClinicalTrials.gov NCT02465515. Registered on 8 June 2015

Introduction: In the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election. Methods: With data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election. Results: Among 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate −7.2, 95% confidence interval, −7.8 to −6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate −0.8; 95% CI, −1.5 to −0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33). Conclusion: Counties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.

Background Sacubitril/Valsartan has been highly efficacious in randomized trials of heart failure with reduced ejection fraction (HFrEF). However, the effectiveness of sacubitril/valsartan in older patients hospitalized for HFrEF in real‐world US practice is unclear. Methods and Results This study included Medicare beneficiaries age ≥65 years who were hospitalized for HFrEF ≤40% in the Get With The Guidelines–Heart Failure registry between October 2015 and December 2018, and eligible for sacubitril/valsartan. Associations between discharge prescription of sacubitril/valsartan and clinical outcomes were assessed after inverse probability of treatment weighting and adjustment for other HFrEF medications. Overall, 1551 (10.9%) patients were discharged on sacubitril/valsartan. Of those not prescribed sacubitril/valsartan, 7857 (62.0%) were prescribed an angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker. Over 12‐month follow‐up, compared with a discharge prescription of angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker, sacubitril/valsartan was independently associated with lower all‐cause mortality (adjusted hazard ratio [HR], 0.82; 95% CI, 0.72–0.94; P=0.004) but not all‐cause hospitalization (adjusted HR, 0.97; 95% CI, 0.89–1.07; P=0.55) or heart failure hospitalization (adjusted HR, 1.04; 95% CI, 0.91–1.18; P=0.59). Patients prescribed sacubitril/valsartan versus those without a prescription had lower risk of all‐cause mortality (adjusted HR, 0.69; 95% CI, 0.60–0.79; P

Nutritional ingredients with defined mechanisms of action can be useful in the recovery of the body from the physical demands of a habitual training plan. The purpose of this study was to determine the effect of dietary supplementation with optimized curcumin, pomegranate ellagitannins, and MSM (R + MSM) on immune-associated mRNA during early recovery (i.e., up to 8 h post-exercise) following all-out running efforts (5-km, 10-km, and 21.1-km). Subjects (N = 14) were randomized to either a supplement (R + MSM) or a control group using an open label design. The study was completed over a period of 31-day prior to a scheduled half-marathon race. Venous blood samples were collected into PAXgene tubes at baseline, subsequent samples were collected at 2, 4, and 8 h after each running effort. A 574-plex mRNA Immunology Array (NanoString) was measured for each sample and ROSALIND® Advanced Analysis Software was used to examined changes in 31 annotated immune response pathways and specific mRNA changes. The greatest change in immune pathways occurred at 2 h (GSS > 3) followed by 4 h (GSS 2–3) and 8 h (GSS 1–2). R + MSM was associated with an increase in innate immunity (CAMP, LTF, TIRAP, CR1, IL1R1, CXCR1, PDCDILG2, and GNLY) and a blunted/smaller increase in damage-associated molecular pattern (DAMP) signaling/inflammation (TLR4, TLR5, S100A8, S100A9, and IFP35). We also found changes in immune-associated mRNA that have not been previously linked to exercise recovery (SOCS1, SOCS2, MME, CECAM6, MX1, IL-1R2, KLRD1, KLRK1, and LAMP3). Collectively these results demonstrate that supplementation with a combination of optimized curcumin, pomegranate ellagitannins, and methylsulfonylmethane resulted in changes that may improve biological recovery from all-out running efforts.

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass (Centropristis striata), summer flounder (Paralichthys dentatus), and Atlantic menhaden (Brevoortia tyrannus), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.

As highly mobile predators with extensive home ranges, some shark species often utilize a continuum of habitats across the continental shelf ranging from the surf zone to the open ocean. For many species, these cross-shelf distributions can change depending on ontogeny or seasonal conditions. Recent research has confirmed a white shark (Carcharodon carcharias) summer nursery off Long Island, New York; however, habitat characterization of this nursery has not yet been conducted nor has fine-scale analysis of vertical behavior. Between 2016 and 2019, 21 young-of-the-year and juvenile white sharks were fitted with satellite and acoustic tags to examine distribution and selection for a suite of oceanographic variables during their late summertime (i.e., August to October) residence in the New York Bight. Horizontal position estimates were used to extract a suite of environmental measurements via remote sensing platforms and were linked with vertical profiles to produce three-dimensional movements for a subset of individuals also fitted with pop-up satellite archival tags (n = 7). Sharks exhibited horizontal movements parallel to Long Island’s southern shoreline and coastal New Jersey, with distances from 0.1 to 131.5 km from shore. Log-likelihood chi-square analyses determined selection for waters with underlying bathymetry of 20–30 m, sea surface temperatures between 20.0 and 22.0°C, sea surface salinities between 31.0 and 32.0 ppt, and chlorophyll-a concentrations between 2.0 and 8.0 mg⋅m–3. Multiple individuals also traversed the mid- to outer shelf region after leaving the Montauk tagging area. Vertical depth profiles illustrated oscillations between the surface and 199 m of water, with an average swimming depth of 9.2 ± 8.9 m. Water column temperatures during these oscillations ranged between 7.9 and 26.2°C (mean = 19.5 ± 2.0°C) with several individuals traversing highly stratified regions presumably associated with a mid-shelf cold pool adjacent to the Hudson Shelf Valley. These results suggest young white sharks exhibit connectivity between the immediate shoreline and mid-continental shelf region, where they play important ecological roles as predators on a variety of species. Our study improves characterization of essential fish habitat for young white sharks and provides new insights into their reliance on this productive continental shelf ecosystem.

Abstract This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χa, between 38% and 94% for χo, and between 20% and 87% for χh with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

The PartMC-MOSAIC particle-resolved aerosol model was previously developed to predict the aerosol mixing state as it evolves in the atmosphere. However, the modeling framework was limited to a zero-dimensional box model approach without resolving spatial gradients in aerosol concentrations. This paper presents the development of stochastic particle methods to simulate turbulent diffusion and dry deposition of aerosol particles in a vertical column within the planetary boundary layer. The new model, WRF-PartMC-MOSAIC-SCM, resolves the vertical distribution of aerosol mixing state. We verified the new algorithms with analytical solutions for idealized test cases and illustrate the capabilities with results from a 2-day urban scenario that shows the evolution of black carbon mixing state in a vertical column.

Climate drying could have transformed ecosystems in southern Madagascar during recent millennia by contributing to the extinction of endemic megafauna. However, the extent of regional aridification during the past 2000 years is poorly known, as are the responses of endemic animals and economically important livestock to drying. We inferred ~1600 years of climate change around Lake Ranobe, SW Madagascar, using oxygen isotope analyses of monospecific freshwater ostracods (Bradleystrandesia cf. fuscata) and elemental analyses of lake core sediment. We inferred past changes in habitat and diet of introduced and extinct endemic megaherbivores using bone collagen stable isotope and 14C datasets (n = 63). Extinct pygmy hippos and multiple giant lemur species disappeared from the vicinity of Ranobe during a dry interval ~1000–700 cal yr BP, but the simultaneous appearance of introduced cattle, high charcoal concentrations, and other evidence of human activity confound inference of drought-driven extirpations. Unlike the endemic megafauna, relatively low collagen stable nitrogen isotope values among cattle suggest they survived dry intervals by exploiting patches of wet habitat. Although megafaunal extirpations coincided with drought in SW Madagascar, coupled data from bone and lake sediments do not support the hypothesis that extinct megafauna populations collapsed solely because of drought. Given that the reliance of livestock on mesic patches will become more important in the face of projected climate drying, we argue that sustainable conservation of spiny forests in SW Madagascar should support local livelihoods by ensuring that zebu have access to mesic habitat. Additionally, the current interactions between pastoralism and riparian habitats should be studied to help conserve the island’s biodiversity.

Abstract Background The clinical research enterprise is not producing the evidence decision makers arguably need in a timely and cost effective manner; research currently involves the use of labor-intensive parallel systems that are separate from clinical care. The emergence of pragmatic clinical trials (PCTs) poses a possible solution: these large-scale trials are embedded within routine clinical care and often involve cluster randomization of hospitals, clinics, primary care providers, etc. Interventions can be implemented by health system personnel through usual communication channels and quality improvement infrastructure, and data collected as part of routine clinical care. However, experience with these trials is nascent and best practices regarding design operational, analytic, and reporting methodologies are undeveloped. Methods To strengthen the national capacity to implement cost-effective, large-scale PCTs, the Common Fund of the National Institutes of Health created the Health Care Systems Research Collaboratory (Collaboratory) to support the design, execution, and dissemination of a series of demonstration projects using a pragmatic research design. Results In this article, we will describe the Collaboratory, highlight some of the challenges encountered and solutions developed thus far, and discuss remaining barriers and opportunities for large-scale evidence generation using PCTs. Conclusion A planning phase is critical, and even with careful planning, new challenges arise during execution; comparisons between arms can be complicated by unanticipated changes. Early and ongoing engagement with both health care system leaders and front-line clinicians is critical for success. There is also marked uncertainty when applying existing ethical and regulatory frameworks to PCTS, and using existing electronic health records for data capture adds complexity.

Background The angiotensin‐receptor/neprilysin inhibitor (ARNI) sacubitril/valsartan reduces hospitalization and mortality for patients with heart failure with reduced ejection fraction. However, adoption of ARNI into clinical practice has been slow. Factors influencing use of ARNI have not been fully elucidated. Using data from the Get With The Guidelines‐Heart Failure registry, Hospital Compare, Dartmouth Atlas, and the American Hospital Association Survey, we sought to identify hospital characteristics associated with patient‐level receipt of an ARNI prescription. Methods and Results We analyzed patients with heart failure with reduced ejection fraction who were eligible for ARNI prescription (ejection fraction≤40%, no contraindications) and hospitalized from October 1, 2015 through December 31, 2016. We used logistic regression to estimate the associations between hospital characteristics and patient ARNI prescription at hospital discharge, accounting for clustering of patients within hospitals using generalized estimating equation methods and adjusting for patient‐level covariates. Of 16 674 eligible hospitalizations from 210 hospitals, 1020 patients (6.1%) were prescribed ARNI at discharge. The median hospital‐level proportion of patients prescribed ARNI was 3.3% (Q1, Q3: 0%, 12.6%). After adjustment for patient‐level covariates, for‐profit hospitals had significantly higher odds of ARNI prescription compared with not‐for‐profit hospitals (odds ratio, 2.53; 95% CI, 1.05–6.10; P=0.04), and hospitals located in the Western United States had lower odds of ARNI prescription compared with those in the Northeast (odds ratio, 0.33; 95% CI, 0.13–0.84; P=0.02). Conclusions Relatively few hospital characteristics were associated with ARNI prescription at hospital discharge, in contrast to what has been observed in early adoption in other disease areas. Additional evaluation of barriers to implementing new evidence into heart failure practice is needed.

Background The 2013 American College of Cardiology/American Heart Association Cholesterol Treatment Guideline increased the number of primary prevention patients eligible for statin therapy, yet uptake of these guidelines has been modest. Little is known of how primary care provider (PCP) beliefs influence statin prescription. Methods and Results We surveyed 164 PCPs from a community‐based North Carolina network in 2017 about statin therapy. We evaluated statin initiation among the PCPs’ statin‐eligible patients between 2014 and 2015 without a previous prescription. Seventy‐two PCPs (43.9%) completed the survey. The median estimate of the relative risk reduction for high‐intensity statins was 45% (interquartile range, 25%–50%). A minority of providers (27.8%) believed statins caused diabetes mellitus, and only 16.7% reported always/very often discussing this with patients. Most PCPs (97.2%) believed that statins cause myopathy, and 72.3% reported always/very often discussing this with patients. Most (77.7%) reported always/very often using the 10‐year atherosclerotic cardiovascular disease risk calculator, although many reported that in most cases other risk factors or patient preferences influenced prescribing (59.8% and 43.1%, respectively). Of 6172 statin‐eligible patients, 22.3% received a prescription for a moderate‐ or high‐intensity statin at follow‐up. Providers reporting greater reliance on risk factors beyond atherosclerotic cardiovascular disease risk were less likely to prescribe statins. Conclusions Although beliefs and approaches to statin discussions vary among community PCPs, new prescription rates are low and minimally associated with those beliefs. These results highlight the complexity of increasing statin prescriptions for primary prevention and suggest that strategies to facilitate standardized discussions and to address external influences on patient beliefs warrant future study.

BackgroundData from randomized controlled trials and observational studies on older adults who take statins for primary prevention of atherosclerotic cardiovascular disease are limited. To determine the incidence of statin use in older adults with and without cardiovascular disease (CVD) and/or diabetes (DM), we conducted a descriptive observational study.MethodsThe cohort consisted of health plan members in the NIH Collaboratory Distributed Research Network aged >75 years who had continuous drug and medical benefits for ≥183 days during the study period, January 1, 2008- March 31, 2018. We defined DM and CVD using diagnosis codes, and identified statins using dispensing data. Statin use was considered incident if a member had no evidence of statin exposure in the claims during the previous 183 days, and the use was considered long-term if statins were supplied for ≥180 days. Incidence rates were reported among members with and without CVD and/or diabetes, and stratified by year, sex, and age group.ResultsAmong 757,569 eligible members, 109,306 older adults initiated statins and 54,624 became long-term users. Health plan members with CVD had the highest incidence of statin use (143.9 initiators per 1,000 member-years for CVD & DM; 114.5 initiators per 1,000 member-years for CVD & No DM). Among health plan members without CVD, those with DM had rates of statin use that were over two times higher than members without DM (76.1 versus 34.5 initiators per 1,000 member-years, respectively). Statin initiation remained steady throughout 2008-2016, was slightly higher in males, and declined with increasing age.ConclusionIncidence of statin use varied by CVD and DM comorbidity, and was lowest among those without CVD. These results highlight the potential clinical equipoise to conduct large pragmatic clinical trials to generate evidence that could be used to inform future blood cholesterol guidelines.

White sharks, Carcharodon carcharias, are often described as elusive, with little information available due to the logistical difficulties of studying large marine predators that make long-distance migrations across ocean basins. Increased understanding of aggregation patterns, combined with recent advances in technology have, however, facilitated a new breadth of studies revealing fresh insights into the biology and ecology of white sharks. Although we may no longer be able to refer to the white shark as a little-known, elusive species, there remain numerous key questions that warrant investigation and research focus. Although white sharks have separate populations, they seemingly share similar biological and ecological traits across their global distribution. Yet, white shark’s behavior and migratory patterns can widely differ, which makes formalizing similarities across its distribution challenging. Prioritization of research questions is important to maximize limited resources because white sharks are naturally low in abundance and play important regulatory roles in the ecosystem. Here, we consulted 43 white shark experts to identify these issues. The questions listed and developed here provide a global road map for future research on white sharks to advance progress toward key goals that are informed by the needs of the research community and resource managers.