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Abstract Effective wildfire prevention includes actions to deliberately target different wildfire causes. However, the cause of an increasing number of wildfires is unknown, hindering targeted prevention efforts. We developed a machine learning model of wildfire ignition cause across the western United States on the basis of physical, biological, social, and management attributes associated with wildfires. Trained on wildfires from 1992 to 2020 with 12 known causes, the overall accuracy of our model exceeded 70% when applied to out‐of‐sample test data. Our model more accurately separated wildfires ignited by natural versus human causes (93% accuracy), and discriminated among the 11 classes of human‐ignited wildfires with 55% accuracy. Our model attributed the greatest percentage of 150,247 wildfires from 1992 to 2020 for which the ignition source was unknown to equipment and vehicle use (21%), lightning (20%), and arson and incendiarism (18%).

ABSTRACT Background and Aims Patient‐reported outcome measures (PROMs) are increasingly used in primary care practices; however, the broad characteristics of this population pose unique challenges. Generic PROMs (e.g., EQ‐5D‐5L) measure general health status from the patient's perspective, contributing towards a comprehensive evaluation of health programming. This study describes the integration of the EQ‐5D‐5L within program evaluation in primary care in Alberta, Canada, using two program examples. Methods A retrospective, longitudinal, observational design was used. The Edmonton O‐day'min Primary Care Network (EOPCN) routinely collects the EQ‐5D‐5L in their exercise and active living and nutrition programs. Data collected between January 1, 2021 and March 31, 2022 was analyzed descriptively, by dimension, index, and visual analog scale (VAS) scores. Minimally important differences were used to interpret index (0.04) and VAS scores (7.0) and the Pareto Classification of Health Change was applied to the dimensions. Effect size of change in index and VAS scores and population comparisons were examined. Results In the exercise and active living program (n = 301), 72% were female with a mean age of 57 (±16) years, and 90.2% reported any problems (levels 2–5) in pain/discomfort. The mean index and VAS scores were 0.74 (±0.18) and 66.5 (±18.9), respectively. Among those with repeated measurement (n = 112), the greatest improvement was in usual activities (17%); the greatest deterioration was in pain/discomfort (25%). In the nutrition program (n = 573), 71% were female with a mean age of 50 (±17) years, and 75.9% reported any problems in pain/discomfort. The mean index and VAS scores were 0.77 (±0.19) and 70.1 (±15.6), respectively. Among those with repeated measurement (n = 212), the greatest improvement was in anxiety/depression (16%); the greatest deterioration was in pain/discomfort (26%). In both, health status was lower than the general Alberta population norms. No baseline characteristics were found to be statistically significant. Conclusion These results inform future programming, to help patient needs.

Background HIV is associated with increased risk of heart failure (HF) but data regarding phenotypes of HF and outcomes after HF diagnosis, especially within the safety net where half of people with HIV in the United States receive care, are less clear. Methods and Results Using an electronic health record cohort of all individuals with HF within a municipal safety‐net system from 2001 to 2019 linked to the National Death Index Plus, we compared HF phenotypes, all‐cause mortality, HF hospitalization, and cause of death for individuals with and without HIV. Among people with HF (n=14 829), 697 individuals had HIV (4.7%). People with HIV were diagnosed with HF 10 years younger on average. A higher proportion of people with HIV had a reduced ejection fraction at diagnosis (37.9% versus 32.7%). Adjusted for age, sex, and risk factors, coronary artery disease on angiography was similar by HIV status. HIV was associated with 55% higher risk of all‐cause mortality (hazard ratio [HR], 1.55 [95% CI, 1.37–1.76]; P

Abstract Terrestrial carnivorans, with their diverse diets and unique adaptations such as the carnassial tooth, offer insights into the connections between functional traits and the climatic and environmental conditions they inhabit. They shed light on functional trait‐environment relationships at the highest trophic levels across a broad range of environmental conditions. In this study, we evaluate the relationship between relative blade length (RBL) of the lower carnassial tooth, a key dietary adaptation among terrestrial carnivorans for slicing and grinding food items, and climate. We propose RBL as an ecometric trait and test the hypothesis that community‐level RBL is correlated with climate and mediated by environmental effects on food availability. Our findings show that communities with higher mean and broader variance of RBL are typically located in warmer and wetter climates, suggesting a relationship between carnivoran dietary diversity and climate. Conversely, communities with a lower mean and narrower variance of RBL predominantly occupy cooler, drier places. This indicates that community‐level carnivoran dietary traits have the potential to serve as indicators of environmental conditions. Given the robust fossil record associated with carnivorans, we also show how RBL can be used as a proxy for reconstructing paleoclimates by examining trait change at seven sites in North America to estimate changes in temperature and precipitation over time in relation to changes in carnivoran community assembly. Understanding the nature of trait‐environment relationships can help us anticipate biological impacts of ongoing environmental change and the geographic regions at the greatest risk of ecological disruption.

Abstract Advancing technology in radiation therapy can significantly influence clinical practice and improvement of patient outcomes. In the process, innovations in technology require that changes to clinical practice are adopted within education settings. This commentary aims to describe the uptake of new technology and ways that academics provide an up‐to‐date curriculum when today's innovation is tomorrow's contemporary practice.

Abstract The sagebrush biome is a dryland region in the western United States experiencing rapid transformations to novel ecological states. Threat‐based approaches for managing anthropogenic and ecosystem threats have recently become prominent, but successfully mitigating threats depends on the ecological resilience of ecosystems. We used a spatially explicit approach for prioritizing management actions that combined a threat‐based model with models of resilience to disturbance and resistance to annual grass invasion. The threat‐based model assessed geographic patterns in sagebrush ecological integrity (SEI) to identify core sagebrush, growth opportunity, and other rangeland areas. The resilience and resistance model identified ecologically relevant climate and soil water availability indicators from process‐based ecohydrological models. The SEI areas and resilience and resistance indicators were consistent—the resilience and resistance indicators showed generally positive relationships with the SEI areas. They also were complementary—SEI areas provided information on intact sagebrush areas and threats, while resilience and resistance provided information on responses to disturbances and management actions. The SEI index and resilience and resistance indicators provide the basis for prioritizing conservation and restoration actions and determining appropriate strategies. The difficulty and time required to conserve or restore SEI areas increase as threats increases and resilience and resistance decrease.

Abstract In the United Kingdom, woodland planting and soil and crop management are being promoted as approaches to tackling flooding. Although evidence is limited, it is thought tree planting and regenerative agriculture practices such as crop–herbal ley pasture rotations increase infiltration, soil water storage and evapotranspiration, potentially reducing flooding. A process‐based soil–water–vegetation model was coupled with a semi‐distributed groundwater model to explore the impact of these interventions on peak and low flows in a large, groundwater‐dominated catchment. Land use change and management were found to have limited potential to reduce flooding in this setting. Herbal ley–crop rotations produced a 2 years, and levels of woodland planting judged to be the realistic maximum produced reductions of 0.2%–2.6%, depending on tree species. Broadscale spruce planting was the only scenario to produce significant reductions in peak flow (16.0%–24.7% at return periods 1–15 years); however, the level of spruce planting required to achieve these reductions was estimated to reduce Q95 flow by ~39%, which would likely have negative implications for water security and ecological river flows. The impact of land‐based natural flood management interventions for flood prevention in large, permeable catchments should not be overstated.

Background Cardiovascular disease is a risk factor for cognitive impairment. Evidence links both lower and higher concentration of the circulating opioid pro‐enkephalin A (PENK‐A) with stroke risk. We studied the association of plasma PENK‐A with incident cognitive impairment. Methods and Results REGARDS (Reasons for Geographic and Racial Differences in Stroke) is a prospective cohort study of 30 239 adults enrolled from 2003 to 2007. Baseline PENK‐A was measured in a nested case–control study of 462 participants who developed cognitive impairment over 4.7 years, and 556 controls. Logistic regression and spline plots adjusted for confounders estimated odds ratios (ORs) of cognitive impairment by baseline PENK‐A. Interaction terms tested for differences in associations by age, sex, and race. Baseline PENK‐A was comparable between cases and controls. There were significant differences in the association of PENK‐A with cognitive impairment by sex and age (adjusted P=0.003 and 0.06, respectively). In women but not men, spline plots showed that higher and lower PENK‐A were associated with decreased odds of cognitive impairment (ORs for 10th and 90th percentiles versus median, 0.65 [95% CI, 0.43–0.96] and 0.64 [95% CI, 0.41–0.99]), with no difference by age. In men ≥65 years of age but not younger men, higher PENK‐A was associated with decreased odds for cognitive impairment (OR for fourth versus first quartile 0.47 [95% CI, 0.22–0.99]); this pattern was not confirmed with spline plotting. Conclusions High and low levels of circulating opioid PENK‐A were associated with decreased odds of future cognitive impairment in specific subgroups. Additional research is warranted to understand the biology underlying this association and the observed differences by sex.

Abstract The aim was to explore various national and international clinical decision‐making tools and dose comparison methods used for selecting cancer patients for proton versus X‐ray radiation therapy. To address this aim, a literature search using defined scoping review methods was performed in Medline and Embase databases as well as grey literature. Articles published between 1 January 2015 and 4 August 2020 and those that clearly stated methods of proton versus X‐ray therapy patient selection and those published in English were eligible for inclusion. In total, 321 studies were identified of which 49 articles met the study’s inclusion criteria representing 13 countries. Six different clinical decision‐making tools and 14 dose comparison methods were identified, demonstrating variability within countries and internationally. Proton therapy was indicated for all paediatric patients except those with lymphoma and re‐irradiation where individualised model‐based selection was required. The most commonly reported patient selection tools included the Normal Tissue Complication Probability model, followed by cost‐effectiveness modelling and dosimetry comparison. Model‐based selection methods were most commonly applied for head and neck clinical indications in adult cohorts (48% of studies). While no ‘Gold Standard’ currently exists for proton therapy patient selection with variations evidenced globally, some of the patient selection methods identified in this review can be used to inform future practice in Australia. As literature was not identified from all countries where proton therapy centres are available, further research is needed to evaluate patient selection methods in these jurisdictions for a comprehensive overview.

Abstract Cultural ecosystem services (CES), a key aspect of nature's contributions to people, remain a challenge to incorporate into decision making. One contributing factor is the difficulty of defining and describing these, due partly to: ongoing poor understanding of what drives people to interact with nature, a lack of appropriate data to quantify these interactions, and basic difficulties in measuring and modelling the complex array of social, psychological and behavioural attributes which help explain people's actions. In this study we present a framework which develops the concepts of cultural capital, social capital and human capital as specific forms of human‐centred capital, in the context of their contribution to understanding CES. Each form of capital encompasses separate attributes of beneficiaries. Testing the framework with data from a separate trans‐disciplinary study illustrated that the framework was readily applicable to specific situations. A measure of cultural capital, EcoCentrism, explained more variation than a suite of seven demographic variables. Applying the framework also showed that despite using a wide range of explanatory variables, a large proportion of observed variation remained unaccounted for. This suggests that more work is needed to understand and to develop metrics which can measure additional factors which underlie peoples’ motivations to engage with nature. The framework is applicable to other types of ecosystem service, and may also be useful for exploring relational values. A free Plain Language Summary can be found within the Supporting Information of this article.

The global COVID‐19 pandemic has impacted all facets of life. In medical radiation science (MRS) education, the effects on continuity of learning were felt by educators, students and clinical supervisors both nationally and internationally. The focus of this commentary is on the common elements that impacted MRS students, specifically related to cancelled clinical placements and the interruption to their academic progress at university. An outline is provided of some innovative strategies implemented by universities and clinical departments to support students’ academic progress, continuity of clinical experiences, their transition from students to practitioners and overall strategies to support student wellbeing. The recent published literature illustrates novel responses to shared challenges faced, and an opportunity to learn from collective experiences.

Background Computational fluid dynamics has shown good agreement with contrast‐enhanced magnetic resonance imaging measurements in cardiovascular disease applications. We have developed a biomechanical model of microvascular perfusion using contrast‐enhanced magnetic resonance imaging signal intensities derived from skeletal calf muscles to study peripheral artery disease (PAD). Methods and Results The computational microvascular model was used to study skeletal calf muscle perfusion in 56 individuals (36 patients with PAD, 20 matched controls). The recruited participants underwent contrast‐enhanced magnetic resonance imaging and ankle‐brachial index testing at rest and after 6‐minute treadmill walking. We have determined associations of microvascular model parameters including the transfer rate constant, a measure of vascular leakiness; the interstitial permeability to fluid flow which reflects the permeability of the microvasculature; porosity, a measure of the fraction of the extracellular space; the outflow filtration coefficient; and the microvascular pressure with known markers of patients with PAD. Transfer rate constant, interstitial permeability to fluid flow, and microvascular pressure were higher, whereas porosity and outflow filtration coefficient were lower in patients with PAD than those in matched controls (all P values ≤0.014). In pooled analyses of all participants, the model parameters (transfer rate constant, interstitial permeability to fluid flow, porosity, outflow filtration coefficient, microvascular pressure) were significantly associated with the resting and exercise ankle‐brachial indexes, claudication onset time, and peak walking time (all P values ≤0.013). Among patients with PAD, interstitial permeability to fluid flow, and microvascular pressure were higher, while porosity and outflow filtration coefficient were lower in treadmill noncompleters compared with treadmill completers (all P values ≤0.001). Conclusions Computational microvascular model parameters differed significantly between patients with PAD and matched controls. Thus, computational microvascular modeling could be of interest in studying lower extremity ischemia.

Abstract Many animal personality traits have implicit movement‐based definitions and can directly or indirectly influence ecological and evolutionary processes. It has therefore been proposed that animal movement studies could benefit from acknowledging and studying consistent interindividual differences (personality), and, conversely, animal personality studies could adopt a more quantitative representation of movement patterns. Using high‐resolution tracking data of three‐spined stickleback fish (Gasterosteus aculeatus), we examined the repeatability of four movement parameters commonly used in the analysis of discrete time series movement data (time stationary, step length, turning angle, burst frequency) and four behavioral parameters commonly used in animal personality studies (distance travelled, space use, time in free water, and time near objects). Fish showed repeatable interindividual differences in both movement and behavioral parameters when observed in a simple environment with two, three, or five shelters present. Moreover, individuals that spent less time stationary, took more direct paths, and less commonly burst travelled (movement parameters), were found to travel farther, explored more of the tank, and spent more time in open water (behavioral parameters). Our case study indicates that the two approaches—quantifying movement and behavioral parameters—are broadly equivalent, and we suggest that movement parameters can be viewed as “micropersonality” traits that give rise to broad‐scale consistent interindividual differences in behavior. This finding has implications for both personality and movement ecology research areas. For example, the study of movement parameters may provide a robust way to analyze individual personalities in species that are difficult or impossible to study using standardized behavioral assays.

Abstract Accurate prediction of the remaining useful life (RUL) in Lithium‐ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)‐ based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non‐linear characteristics and non‐Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16.

Abstract Maintaining fire‐dependent habitat for species of conservation concern often requires a balancing act between the short‐term costs of direct mortality caused by fires and the long‐term benefits of ensuring high‐quality habitat. To reduce risk to threatened populations, land managers may need to adjust the frequency with which they burn sites and, likely, mitigate the short‐term costs of prescribed fires by not burning during a species' life stages that may be especially sensitive to fires. Few, if any, studies have investigated burn regimes for butterflies in longleaf pine forests—a once dominant habitat in the southeast United States—despite a long history and frequent usage of prescribed burns in these ecosystems. We surveyed a recently discovered metapopulation of frosted elfin (Callophrys irus) butterflies residing in longleaf pine forest over seven years at sites across nine management units with differing fire return intervals and fire seasonalities. We observed dramatic population declines after burns, with no adults observed at some sites in subsequent years. Our analyses demonstrate that populations may need at least three to 4 years to recover, and that burns in the spring, when frosted elfin eggs and larvae are found on hostplants, should be avoided if possible. In fact, our model suggests that the largest spring‐burned sites have fewer than 20% of the adult elfins the year following a fire than do sites burned in other seasons. Land managers seeking to conserve frosted elfins and other butterflies with similar life histories may need to burn at times of the year that reduce direct mortality and with fire return intervals that are longer than the one‐ to two‐year intervals recommended by some for maximizing the diversity of the herbaceous understory in longleaf pine forests.

Abstract Ecometrics is the study of community‐level functional trait–environment relationships. We use ecometric analyses to estimate paleoenvironment and to investigate community‐level functional changes through time. We evaluate four methods that have been used or have the potential to be used in ecometric analyses for estimating paleoenvironment to determine whether there have been systematic differences in paleoenvironmental estimation due to choice of the estimation method. Specifically, we evaluated linear regression, polynomial regression, nearest neighbor, and maximum‐likelihood methods to explore the predictive ability of the relationship for a well‐known ecometric dataset of mammalian herbivore hypsodonty metrics (molar tooth crown to root height ratio) and annual precipitation. Each method was applied to 43 Pleistocene fossil sites and compared to annual precipitation from global climate models. Sites were categorized as glacial or interglacial, and paleoprecipitation estimates were compared to the appropriate model. Estimation methods produce results that are highly correlated with log precipitation and estimates from the other methods (p

Abstract Introduction This study aimed to determine a maximal pelvic separation and waist circumference in pelvic patients to guide radiation therapists in acquiring kilovoltage (kV) planar images of acceptable quality for treatment verification. Methods A pelvic anthropomorphic phantom modified with different bolus thicknesses was imaged at various default kV exposure settings. Radiation therapists rated image quality and acceptance/rejection of these images for treatment verification. Results Sixteen radiation therapists participated in the study. Image quality was inversely proportional to phantom size. AP and lateral kV images were acceptable for treatment verification up to a waist circumference of 143 cm. Conclusions Exposure settings for kV image verification of large patients should be individualised to avoid unnecessary patient radiation dose through repeated imaging.

Abstract Targeted capture and enrichment approaches have proven effective for phylogenetic study. Ultraconserved elements (UCEs) in particular have exhibited great utility for phylogenomic analyses, with the software package phyluce being among the most utilized pipelines for UCE phylogenomics, including probe design. Despite the success of UCEs, it is becoming increasing apparent that diverse lineages require probe sets tailored to focal taxa in order to improve locus recovery. However, factors affecting probe design and methods for optimizing probe sets to focal taxa remain underexplored. Here, we use newly available beetle (Coleoptera) genomic resources to investigate factors affecting UCE probe set design using phyluce. In particular, we explore the effects of stringency during initial design steps, as well as base genome choice on resulting probe sets and locus recovery. We found that both base genome choice and initial bait design stringency parameters greatly alter the number of resultant probes included in final probe sets and strongly affect the number of loci detected and recovered during in silico testing of these probe sets. In addition, we identify attributes of base genomes that correlated with high performance in probe design. Ultimately, we provide a recommended workflow for using Phyluce to design an optimized UCE probe set that will work across a targeted lineage, and use our findings to develop a new, open‐source UCE probe set for beetles of the suborder Adephaga.

Abstract Pre‐existing cardiovascular disease (CVD) increases the morbidity and mortality of COVID‐19 and is strongly associated with poor disease outcomes. However, SARS‐CoV‐2 infection can also trigger de novo acute and chronic cardiovascular disease. Acute cardiac complications include arrhythmia, myocarditis and heart failure, which are significantly associated with higher in‐hospital mortality. The possible mechanisms by which SARS‐CoV‐2 causes this acute cardiac disease include direct damage caused by viral invasion of cardiomyocytes as well as indirect damage through systemic inflammation. The long‐term cardiac complications associated with COVID‐19 are incompletely characterised and thought to include hypertension, arrhythmia, coronary atherosclerosis and heart failure. Although some cardiac‐related symptoms can last over 6 months, the effect of these complications on long‐term patient health remains unclear. The risk factors associated with long‐term cardiovascular disease remain poorly defined. Determining which patients are most at‐risk of long‐term cardiovascular disease is vital so that targeted follow‐up and patient care can be provided. The aim of this review was to summarise the current evidence of the acute and long‐term cardiovascular consequences of SARS‐CoV‐2 infection and the mechanisms by which SARS‐CoV‐2 may cause cardiovascular disease.

Abstract Objectives Thrombotic and microvascular complications are frequently seen in deceased COVID‐19 patients. However, whether this is caused by direct viral infection of the endothelium or inflammation‐induced endothelial activation remains highly contentious. Methods Here, we use patient autopsy samples, primary human endothelial cells and an in vitro model of the pulmonary epithelial–endothelial cell barrier. Results We show that primary human endothelial cells express very low levels of the SARS‐CoV‐2 receptor ACE2 and the protease TMPRSS2, which blocks their capacity for productive viral infection, and limits their capacity to produce infectious virus. Accordingly, endothelial cells can only be infected when they overexpress ACE2, or are exposed to very high concentrations of SARS‐CoV‐2. We also show that SARS‐CoV‐2 does not infect endothelial cells in 3D vessels under flow conditions. We further demonstrate that in a co‐culture model endothelial cells are not infected with SARS‐CoV‐2. Endothelial cells do however sense and respond to infection in the adjacent epithelial cells, increasing ICAM‐1 expression and releasing pro‐inflammatory cytokines. Conclusions Taken together, these data suggest that in vivo, endothelial cells are unlikely to be infected with SARS‐CoV‐2 and that infection may only occur if the adjacent pulmonary epithelium is denuded (basolateral infection) or a high viral load is present in the blood (apical infection). In such a scenario, whilst SARS‐CoV‐2 infection of the endothelium can occur, it does not contribute to viral amplification. However, endothelial cells may still play a key role in SARS‐CoV‐2 pathogenesis by sensing adjacent infection and mounting a pro‐inflammatory response to SARS‐CoV‐2.