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Abstract Rising atmospheric CO₂levels place terrestrial ecosystems under novel environmental conditions, and research in field settings is key to understanding how real plant communities will respond. Despite decades of progress in elevated CO₂(eCO₂) experiments, major gaps persist in our knowledge of plant responses to interacting influences of climate change, especially in areas outside North America and Western Europe.With a goal to expand access to field‐based eCO₂research, we designed, built, and tested TinyCO₂, a low‐cost field experiment for climate change research on plants. TinyCO₂features sixteen 0.62‐m²plot areas, half with ambient and half with elevated (+200 ppm) CO₂concentrations, and is suitable for short‐stature plants (≤0.5 m in height).Using a proportional‐integral control algorithm and constant sampling of air within the plots, TinyCO₂achieves consistent elevation of [CO₂] averaging +196.9 ppm. During testing, 95.1% of measured CO₂concentrations fell within 20% of the setpoint (ambient CO₂ + 200 ppm). A streamlined design and efficient use of instrumentation reduced the cost of the system to roughly one‐fifth of the cost of similar experiments from the past 30 years ($13.68 vs. $64.65 ppm⁻¹ m⁻², adjusted to 2024 USD).Our results demonstrate a system capable of precise and accurate field‐based CO₂elevation for significantly reduced cost. We envision the TinyCO₂design being implemented in a multitude of field‐based eCO₂studies, perhaps as part of a globally distributed collaborative network experiment. 

Abstract BackgroundEmerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. ObjectiveThe purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. MethodsA risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variables by the weight of evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. ResultsA model with 48 predictive variables yielded significant predictive performance of subsequent musculoskeletal injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at baseline and acute timepoints. At a specified false-positive rate of 6.67%, the model achieves a true-positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well-calibrated composite risk score. ConclusionsThese results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student athletes most at risk for post-concussion musculoskeletal injury. 

Abstract BackgroundEmerging evidence indicates an elevated risk of post-concussion musculoskeletal (MSK) injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. ObjectiveThe purpose of this study was to model post-concussion MSK injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. MethodsA risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery specific criteria, and outcomes from a diverse array of concussions assessments. The machine learning approach involved transforming variables by the Weight of Evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. ResultsA model with 48 predictive variables yielded significant predictive performance of subsequent MSK injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at Baseline and Acute timepoints. At a specified false positive rate of 6.67%, the model achieves a true positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well calibrated composite risk score. ConclusionThese results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student-athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student-athletes most at risk for post-concussion MSK injury. Key PointsThere is a well-established elevated risk of post-concussion subsequent musculoskeletal injury; however, prior efforts have failed to identify risk factors.This study developed a composite risk score model with an AUC of 0.82 from common concussion clinical measures and participant demographics.By identifying athletes at elevated risk, clinicians may be able to reduce injury risk through targeted injury risk reduction programs. 

ABSTRACT Identifying the physiological mechanisms by which plants are adapted to drought is critical to predict species responses to climate change. We measured the responses of leaf hydraulic and stomatal conductances (K_leafandg_s, respectively) to dehydration, and their association with anatomy, in seven species of CaliforniaCeanothusgrown in a common garden, including some of the most drought‐tolerant species in the semi‐arid flora. We tested for matching of maximum hydraulic supply and demand and quantified the role of decline ofK_leafin driving stomatal closure. AcrossCeanothusspecies, maximumK_leafandg_swere negatively correlated, and bothK_leafandg_sshowed steep declines with decreasing leaf water potential (i.e., a high sensitivity to dehydration). The leaf water potential at 50% decline ing_swas linked with a low ratio of maximum hydraulic supply to demand (i.e., maximumK_leaf:g_s). This sensitivity ofg_s, combined with low minimum epidermal conductance and water storage, could contribute to prolonged leaf survival under drought. The specialized anatomy of subg.Cerastesincludes trichomous stomatal crypts and pronounced hypodermis, and was associated with higher water use efficiency and water storage. Combining our data with comparative literature of other California species, species of subg. Cerastesshow traits associated with greater drought tolerance and reliance on leaf water storage relative to other California species. In addition to drought resistance mechanisms such as mechanical protection and resistance to embolism, drought avoidance mechanisms such as sensitive stomatal closure could contribute importantly to drought tolerance in dry‐climate adapted species. 

Many questions in leaf physiology, from mechanisms of water transport and stomatal responses to interpretation of gas exchange measurements, hinge on distributions of water potential among compartments in the leaf. A new tool, AquaDust, offers the possibility of quantifying those distributions at unprecedentedly small scales of organization 

Abstract Plants differ widely in how soil drying affects stomatal conductance (g_s) and leaf water potential (ψ_leaf), and in the underlying physiological controls. Efforts to breed crops for drought resilience would benefit from a better understanding of these mechanisms and their diversity. We grew 12 diverse genotypes of common bean (Phaseolus vulgarisL.) and four of tepary bean (P. acutifolius;a highly drought resilient species) in the field under irrigation and post‐flowering drought, and quantified responses ofg_sandψ_leaf, and their controls (soil water potential [ψ_soil], evaporative demand [Δw] and plant hydraulic conductance [K]). We hypothesised that (i) common beans would be more “isohydric” (i.e., exhibit strong stomatal closure in drought, minimisingψ_leafdecline) than tepary beans, and that genotypes with largerψ_leafdecline (more “anisohydric”) would exhibit (ii) smaller increases in Δw, due to less suppression of evaporative cooling by stomatal closure and hence less canopy warming, but (iii) largerKdeclines due toψ_leafdecline. Contrary to our hypotheses, we found that half of the common bean genotypes were similarly anisohydric to most tepary beans; canopy temperature was cooler in isohydric genotypes leading to smaller increases in Δwin drought; and that stomatal closure andKdecline were similar in isohydric and anisohydric genotypes.g_sandψ_leafwere virtually insensitive to drought in one tepary genotype (G40068). Our results highlight the potential importance of non‐stomatal mechanisms for leaf cooling, and the variability in drought resilience traits among closely related crop legumes. 

ImportanceSince 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) have recommended the pooled cohort equations (PCEs) for estimating the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). An AHA scientific advisory group recently developed the Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations, which incorporated kidney measures, removed race as an input, and improved calibration in contemporary populations. PREVENT is known to produce ASCVD risk predictions that are lower than those produced by the PCEs, but the potential clinical implications have not been quantified. ObjectiveTo estimate the number of US adults who would experience changes in risk categorization, treatment eligibility, or clinical outcomes when applying PREVENT equations to existing ACC and AHA guidelines. Design, Setting, and ParticipantsNationally representative cross-sectional sample of 7765 US adults aged 30 to 79 years who participated in the National Health and Nutrition Examination Surveys of 2011 to March 2020, which had response rates ranging from 47% to 70%. Main Outcomes and MeasuresDifferences in predicted 10-year ASCVD risk, ACC and AHA risk categorization, eligibility for statin or antihypertensive therapy, and projected occurrences of myocardial infarction or stroke. ResultsIn a nationally representative sample of 7765 US adults aged 30 to 79 years (median age, 53 years; 51.3% women), it was estimated that using PREVENT equations would reclassify approximately half of US adults to lower ACC and AHA risk categories (53.0% [95% CI, 51.2%-54.8%]) and very few US adults to higher risk categories (0.41% [95% CI, 0.25%-0.62%]). The number of US adults receiving or recommended for preventive treatment would decrease by an estimated 14.3 million (95% CI, 12.6 million-15.9 million) for statin therapy and 2.62 million (95% CI, 2.02 million-3.21 million) for antihypertensive therapy. The study estimated that, over 10 years, these decreases in treatment eligibility could result in 107 000 additional occurrences of myocardial infarction or stroke. Eligibility changes would affect twice as many men as women and a greater proportion of Black adults than White adults. Conclusion and RelevanceBy assigning lower ASCVD risk predictions, application of the PREVENT equations to existing treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults. 

Abstract PremiseThe adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. MethodsWe developed a method to quantify “amphistomy advantage” () as the log‐ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species ʻilima (Sida fallax). ResultsCoastal ʻilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and have lower CO₂conductance through the internal airspace and (2) that they benefit more because they have similar conductance on each surface, as opposed to most conductance being through the lower surface. ConclusionsThis is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO₂supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in “sun” leaves, but the mechanistic basis remains uncertain. 

Abstract Leaf surface conductance to water vapor and CO2 across the epidermis (gleaf) strongly determines the rates of gas exchange. Thus, clarifying the drivers of gleaf has important implications for resolving the mechanisms of photosynthetic productivity and leaf and plant responses and tolerance to drought. It is well recognized that gleaf is a function of the conductances of the stomata (gs) and of the epidermis + cuticle (gec). Yet, controversies have arisen around the relative roles of stomatal density (d) and size (s), fractional stomatal opening (α; aperture relative to maximum), and gec in determining gleaf. Resolving the importance of these drivers is critical across the range of leaf surface conductances, from strong stomatal closure under drought (gleaf,min), to typical opening for photosynthesis (gleaf,op), to maximum achievable opening (gleaf,max). We derived equations and analyzed a compiled database of published and measured data for approximately 200 species and genotypes. On average, within and across species, higher gleaf,min was determined 10 times more strongly by α and gec than by d and negligibly by s; higher gleaf,op was determined approximately equally by α (47%) and by stomatal anatomy (45% by d and 8% by s), and negligibly by gec; and higher gleaf,max was determined entirely by d. These findings clarify how diversity in stomatal functioning arises from multiple structural and physiological causes with importance shifting with context. The rising importance of d relative to α, from gleaf,min to gleaf,op, enables even species with low gleaf,min, which can retain leaves through drought, to possess high d and thereby achieve rapid gas exchange in periods of high water availability.