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Idealized test cases for the dynamical cores of atmospheric general circulation models are informative tools to assess the accuracy of the numerical designs and investigate the general characteristics of atmospheric motions. A new test case is introduced that is built upon a baroclinically unstable base state with an added orographic barrier. The topography is analytically prescribed and acts as a trigger of both baroclinic Rossby waves and inertia–gravity waves on a rotating, regular-sized planet. Both dry and idealized moist configurations are suggested. The latter utilizes the Kessler warm-rain precipitation scheme. The test case enhances the complexity of the existing test suite hierarchy and focuses on the impacts of two midlatitudinal mountain ridges on the circulation. Selected simulation examples from four dynamical cores are shown. These are the Spectral Element and Finite Volume dynamical cores, which are part of the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM), versions 2.1.3 and 2.2, and the Cubed-Sphere Finite Volume dynamical cores, which is new to CESM version 2.2. In addition, the Model for Prediction Across Scales (MPAS) is tested. The overall flow patterns agree well in the four dynamical cores, but the details can vary greatly. The examples highlight the broad palette of use cases for the test case and reveal physics–dynamics coupling issues.

This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations – a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100 km atmosphere and land, a 0.125∘ river-routing model, and 14→60 km ocean and sea ice. By design, the computational cost of NARRM is ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 10 %–20 % of a globally uniform high-resolution model at 25 km.A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air–sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land–atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This paper presents a novel approach to probabilistic forecasting for pedestrians based on weighted sums of ordinary differential equations that are learned from historical trajectory information within a fixed scene. The resulting algorithm is embarrassingly parallel and is able to work at real-time speeds using a naive Python implementation. The quality of predicted locations of agents generated by the proposed algorithm is validated on a variety of examples and considerably higher than existing state of the art approaches over long time horizons.

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Gwyn Hughes of Llandudno submitted this photograph of Llandudno Secondary Modern School football team in 1948 which is included in the archive of Welsh...

This study investigated the association between high-speed running (HSR) and sprint running (SR) and injuries within elite soccer players. The impact of intermittent aerobic fitness as measured by the end speed of the 30–15 intermittent fitness test (30–15VIFT) and high chronic workloads (average 21-day) as potential mediators of injury risk were also investigated. Observational Cohort Study. 37 elite soccer players from one elite squad were involved in a one-season study. Training and game workloads (session-RPE×duration) were recorded in conjunction with external training loads (using global positioning system technology) to measure the HSR (>14.4kmh−1) and SR (>19.8kmh−1) distance covered across weekly periods during the season. Lower limb injuries were also recorded. Training load and GPS data were modelled against injury data using logistic regression. Odds ratios (OR) were calculated with 90% confidence intervals based on 21-day chronic training load status (sRPE), aerobic fitness, HSR and SR distance with these reported against a reference group. Players who completed moderate HSR (701–750-m: OR: 0.12, 90%CI: 0.08–0.94) and SR distances (201–350-m: OR: 0.54, 90%CI: 0.41–0.85) were at reduced injury risk compared to low HSR (≤674-m) and SR (≤165-m) reference groups. Injury risk was higher for players who experienced large weekly changes in HSR (351–455-m; OR: 3.02; 90%CI: 2.03–5.18) and SR distances (between 75–105-m; OR: 6.12, 90%CI: 4.66–8.29). Players who exerted higher chronic training loads (≥2584 AU) were at significantly reduced risk of injury when they covered 1-weekly HSR distances of 701–750m compared to the reference group of <674m (OR=0.65, 90% CI 0.27–0.89). When intermittent aerobic fitness was considered based on 30–15VIFT performance, players with poor aerobic fitness had a greater risk of injury than players with better-developed aerobic fitness. Exposing players to large and rapid increases in HSR and SR distances increased the odds of injury. However, higher chronic training loads (≥2584 AU) and better intermittent aerobic fitness off-set lower limb injury risk associated with these running distances in elite soccer players.

The objective of the investigation was to observe the impact of player wellbeing on the training output of elite soccer players. Prospective cohort design. Forty-eight soccer players (age: 25.3±3.1years; height: 183±7cm; mass: 72±7kg) were involved in this single season observational study across two teams. Each morning, pre-training, players completed customised perceived wellbeing questionnaires. Global positioning technology devices were used to measure external load (total distance, total high-speed running distance, high speed running, player load, player load slow, maximal velocity, maximal velocity exposures). Players reported ratings of perceived exertion using the modified Borg CR-10 scale. Integrated training load ratios were also analysed for total distance:RPE, total high speed distance:RPE player load:RPE and player load slow:RPE respectively. Mixed-effect linear models revealed significant effects of wellbeing Z-score on external and integrated training load measures. A wellbeing Z-score of −1 corresponded to a −18±2m (−3.5±1.1%), 4±1m (−4.9±2.1%,) 0.9±0.1kmh−1 (−3.1±2.1%), 1±1 (−4.6±2.9%), 25±3AU (−4.9±3.1%) and 11±0.5AU (−8.9±2.9%) reduction in total high speed distance, high speed distance, maximal velocity, maximal velocity exposures, player load and player load slow respectively. A reduction in wellbeing impacted external:internal training load ratios and resulted in −0.49±0.12mmin−1, −1.20±0.08mmin−1,−0.02±0.01AUmin−1 in total distance:RPE, total high speed distance:RPE and player load slow:RPE respectively. The results suggest that systematic monitoring of player wellbeing within soccer cohorts can provide coaches with information about the training output that can be expected from individual players during a training session.

We provide a rich multi-contrast microstructural MRI dataset acquired on an ultra-strong gradient 3T Connectom MRI scanner comprising 5 repeated sets of MRI microstructural contrasts in 6 healthy human participants. The availability of data sets that support comprehensive simultaneous assessment of test-retest reliability of multiple microstructural contrasts (i.e., those derived from advanced diffusion, multi-component relaxometry and quantitative magnetisation transfer MRI) in the same population is extremely limited. This unique dataset is offered to the imaging community as a test-bed resource for conducting specialised analyses that may assist and inform their current and future research. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Our data demonstrate high reproducibility of several microstructural MRI measures across scan sessions as shown by intra-class correlation coefficients and coefficients of variation. To illustrate a potential use of the MICRA dataset, we computed sample sizes required to provide sufficient statistical power a priori across different white matter pathways and microstructure measures for different statistical comparisons. We also demonstrate whole brain white matter voxel-wise repeatability in several microstructural maps. The MICRA dataset will be of benefit to researchers wishing to conduct similar reliability tests, power estimations or to evaluate the robustness of their own analysis pipelines.

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine. A systematic review of evidence, across the key pillars of prevention, diagnosis, treatment and prognosis, outlines milestones that need to be met to enable the broad clinical implementation of precision medicine in diabetes care.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31 st December 2018. We used linked demographic information, and secondary care electronic health records from 1 st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.