Climate warming alters photosynthetic responses to elevated CO2 in prairie plants.

PREMISE: The impact of elevated CO2 concentration ([CO2 ]) and climate warming on plant productivity in dryland ecosystems is influenced strongly by soil moisture availability. We predicted that the influence of warming on the stimulation of photosynthesis by elevated [CO2 ] in prairie plants would operate primarily through direct and indirect effects on soil water. METHODS: We measured light-saturated photosynthesis (Anet ), stomatal conductance (gs ), maximum Rubisco carboxylation rate (Vcmax ), maximum electron transport capacity (Jmax ) and related variables in four C3 plant species in the Prairie Heating and CO2 Enrichment (PHACE) experiment in southeastern Wyoming. Measurements were conducted over two growing seasons that differed in the amount of precipitation and soil moisture content. RESULTS: Anet in the C3 subshrub Artemisia frigida and the C3 forb Sphaeralcea coccinea was stimulated by elevated [CO2 ] under ambient and warmed temperature treatments. Warming by itself reduced Anet in all species during the dry year, but stimulated photosynthesis in S. coccinea in the wet year. In contrast, Anet in the C3 grass Pascopyrum smithii was not stimulated by elevated [CO2 ] or warming under wet or dry conditions. Photosynthetic downregulation under elevated [CO2 ] in this species countered the potential stimulatory effect under improved water relations. Warming also reduced the magnitude of CO2 -induced down-regulation in this grass, possibly by sustaining high levels of carbon utilization. CONCLUSIONS: Direct and indirect effects of elevated [CO2 ] and warming on soil water was an overriding factor influencing patterns of Anet in this semi-arid temperate grassland, emphasizing the important role of water relations in driving grassland responses to global change.

Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors.

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 µg m-3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.

Structural brain correlates of childhood trauma with replication across two large, independent community-based samples.

INTRODUCTION: Childhood trauma and adversity are common across societies and have strong associations with physical and psychiatric morbidity throughout the life-course. One possible mechanism through which childhood trauma may predispose individuals to poor psychiatric outcomes is via associations with brain structure. This study aimed to elucidate the associations between childhood trauma and brain structure across two large, independent community cohorts. METHODS: The two samples comprised (i) a subsample of Generation Scotland (n=1,024); and (ii) individuals from UK Biobank (n=27,202). This comprised n=28,226 for mega-analysis. MRI scans were processed using Free Surfer, providing cortical, subcortical, and global brain metrics. Regression models were used to determine associations between childhood trauma measures and brain metrics and psychiatric phenotypes. RESULTS: Childhood trauma associated with lifetime depression across cohorts (OR 1.06 GS, 1.23 UKB), and related to early onset and recurrent course within both samples. There was evidence for associations between childhood trauma and structural brain metrics. This included reduced global brain volume, and reduced cortical surface area with highest effects in the frontal (ß=-0.0385, SE=0.0048, p(FDR)=5.43x10-15) and parietal lobes (ß=-0.0387, SE=0.005, p(FDR)=1.56x10-14). At a regional level the ventral diencephalon (VDc) displayed significant associations with childhood trauma measures across both cohorts and at mega-analysis (ß=-0.0232, SE=0.0039, p(FDR)=2.91x10-8). There were also associations with reduced hippocampus, thalamus, and nucleus accumbens volumes. DISCUSSION: Associations between childhood trauma and reduced global and regional brain volumes were found, across two independent UK cohorts, and at mega-analysis. This provides robust evidence for a lasting effect of childhood adversity on brain structure.

Population-level variation of Fraxinus americana (white ash) is influenced by precipitation differences across the native range.

We investigated the potential of Fraxinus americana L. to adapt to climate change by comparing diameter growth, survival and physiological status of 44 populations of 30-year-old trees originating from throughout the species range and grown in a common garden. Populations originating closest to the latitude of the common garden had the most rapid diameter growth and the highest survival. Among populations originating within a narrow latitudinal band along an east-west gradient of decreasing precipitation, those from the drier western end were best adapted to the dry climate of the common garden site, as judged by survival, stem circumference, leaf and wood carbon isotope ratios, leaf mass per area and leaf nitrogen concentration. These findings suggest that eastern populations may not perform well under the hotter and drier conditions predicted by climate change scenarios. Moreover, in the event of significant climate change, the short-term acclimation responses (within a generation) of F. americana may be insufficient to ensure the continued vigor or survival of this species throughout much of its present range.

Supporting 'Baby Friendly': a quality improvement initiative for the management of transitional neonatal hypoglycaemia.

We describe a quality improvement initiative conducted in a medium-sized district general hospital with a neonatal intensive care unit, which involved working with the multidisciplinary team to create a 'Baby Friendly' neonatal hypoglycaemia pathway with implementation of dextrose gel as a first-line treatment. As a result of the project, formula supplementation rates and admissions for transitional hypoglycaemia were reduced and breastfeeding rates at 3 months improved. This initiative demonstrates that evidence-based guidelines with multidisciplinary team input can improve standards of care.

Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel.

The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy-two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry.