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Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.

Prescribed burning for fuel reduction is a major strategy for reducing the risk from unplanned fire. Although there are theoretical studies suggesting that prescribed fire has a strong negative influence on the subsequent area of unplanned fire (so-called leverage), many empirical studies find a more modest influence. Here, I develop a series of simulations to explore the landscape drivers of leverage. Leverage declines with treatment level in a nonlinear, \"decay\" relationship, implying diminishing effectiveness. The spatial configuration of the prescribed fire treatment has a major effect: long linear (gridded) barriers are far more effective than patch barriers, but gaps in the grid lead to large reductions in leverage. However, the extent of unplanned fires in the landscape has the largest influence such that a landscape with 3% annual extent has only one-fifth of the leverage of a landscape with 28%. Leverage decreases with the probability of spread, suggesting that treatment is less effective when fire weather is severe. For gridded designs, leverage increases with the size of individual fires, but this is not the case for patch designs. These results agree well with recent empirical studies finding that prescribed burning has only a modest effect on subsequent unplanned fire in many biomes. They also help to explain why those empirical studies report lower effectiveness than many simulation studies. In practice, leverage values >1 (replacement of unplanned with planned fire) are hard to achieve.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.