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Operational psychology plays a unique role in supporting issues of nationa security, national defense, and public safety. In this book, authors Mark A. Staal and Sally C. Harvey, both operational psychologists and retired military colonels, lead a team of experts explaining the field, its many roles, and how it is expanding--back cover.

Large-scale wetland restoration often focuses on repairing the hydrologic connections degraded by anthropogenic modifications. Of these hydrologic connections, groundwater discharge is an important target, as these surface water ecosystem control points are important for thermal stability, among other ecosystem services. However, evaluating the effectiveness of the restoration activities on establishing groundwater discharge connection is often difficult over large areas and inaccessible terrain. Unoccupied aircraft systems (UAS) are now routinely used for collecting aerial imagery and creating digital surface models (DSM). Lightweight thermal infrared (TIR) sensors provide another payload option for generation of sub-meter-resolution aerial TIR orthophotos. This technology allows for the rapid and safe survey of groundwater discharge areas. Aerial TIR water-surface data were collected in March 2019 at Tidmarsh Farms, a former commercial cranberry peatland located in coastal Massachusetts, USA (41°54′17″ N 70°34′17″ W), where stream and wetland restoration actions were completed in 2016. Here, we present a 0.4 km2 georeferenced, temperature-calibrated TIR orthophoto of the area. The image represents a mosaic of nearly 900 TIR images captured by UAS in a single morning with a total flight time of 36 min and is supported by a DSM derived from UAS-visible imagery. The survey was conducted in winter to maximize temperature contrast between relatively warm groundwater and colder ambient surface environment; lower-density groundwater rises above cool surface waters and thus can be imaged by a UAS. The resulting TIR orthomosaic shows fine detail of seepage distribution and downstream influence along the several restored channel forms, which was an objective of the ecological restoration design. The restored stream channel has increased connectivity to peatland groundwater discharge, reducing the ecosystem thermal stressors. Such aerial techniques can be used to guide ecological restoration design and assess post-restoration outcomes, especially in settings where ecosystem structure and function is governed by groundwater and surface water interaction.

Recent studies with cyprinid and characin (superorder Ostariophysi) fishes suggest that purine-N-oxides function as chemical alarm cues (alarm pheromones) and that the nitrogen oxide functional group acts as the chief molecular trigger. To further test the hypothesis that the nitrogen-oxide functional group is evolutionarily conserved as an active component of the Ostariophysan alarm pheromone system, we exposed juvenile channel catfish (Ictalurus punctatus, Siluriformes) to conspecific skin extract, hypoxanthine-3-N-oxide (the putative alarm pheromone) and a suite of structurally and functionally similar compounds. Conspecific skin extract and hypoxanthine-3-N-oxide elicited significant increases in species typical antipredator behaviors. A structurally dissimilar compound possessing a nitrogen oxide functional group (pyridine-N-oxide) elicited a significant, but less intense alarm response. Compounds lacking a nitrogen oxide functional group were not significantly different from control stimuli. In addition, two non-Ostariophysan species known to possess chemical alarm cues (convict cichlids, Acrchocentrus nigrofasciatus, Cichlidae, Acanthopterygii and rainbow trout, Oncorhynchus mykiss, Salmonidae, Protacanthopterygii) did not show any increase in antipredator behavior in response to hypoxanthine-3-N-oxide. These data demonstrate the conservation of chemical alarm cues within at least three orders of the superorder Ostariophysi.

During their first year of growth yellow perch, Perca flavescens, undergo an ontogenetic niche shift from invertebrate feeding to piscivory. They also undergo a similar shift in their response to heterospecific alarm cues, switching from anti-predator to foraging behaviour. We conducted laboratory trials to determine whether yellow perch experience a comparable ontogenetic shift in their response to conspecific alarm cues. When exposed to either young-of-year (YOY) or adult perch skin extract, YOY perch responded with decreased time in motion and number of feeding attempts as well as increased time spent with spines erect and latency to first feeding attempt, all of which are indicative of an anti-predator response. Adult perch, when exposed to the same cues, responded with increased time spent moving and number of feeding attempts as well as decreased time spent with spines erect and latency to first feeding attempt, indicative of a foraging response. These data suggest that yellow perch undergo an ontogenetic niche shift in response to conspecific alarm cues.[PUBLICATION ABSTRACT]

Methoxy radicals (CH3O•), formed as intermediates during methane oxidation, may play an underexplored but locally significant role in the atmospheric oxidation of dimethyl sulfide (DMS), a key sulfur-containing compound emitted primarily by marine phytoplankton. This study presents a comprehensive computational investigation of the reaction mechanisms and kinetics of DMS oxidation initiated by CH3O•, using density functional theory B3LYP-D3(BJ)/6-311++G(3df,3pd), CCSD(T)/6-311++G(3df,3pd), and UCBS-QB3 methods. Our calculations show that DMS reacts with CH3O• via hydrogen atom abstraction to form the methyl-thiomethylene radical (CH3SCH2•), with a rate constant of 3.05 × 10−16 cm3/molecule/s and a Gibbs free energy barrier of 14.2 kcal/mol, which is higher than the corresponding barrier for reaction with hydroxyl radicals (9.1 kcal/mol). Although less favorable kinetically, the presence of CH3O• in localized, methane-rich environments may still allow it to contribute meaningfully to DMS oxidation under specific atmospheric conditions. While the short atmospheric lifetime of CH3O• limits its global impact on large-scale atmospheric sulfur cycling, in marine layers where methane and DMS emissions overlap, CH3O• may play a meaningful role in forming sulfur dioxide and downstream sulfate aerosols. These secondary organic aerosols lead to cloud condensation nuclei (CCN) formation, subsequent changes in cloud properties, and can thereby influence local radiative forcing. The study’s findings underscore the importance of incorporating CH3O• driven oxidation pathways into atmospheric models to enhance our understanding of regional sulfur cycling and its impacts on local air quality, cloud properties and radiative forcing. These findings provide mechanistic insights that improve data interpretation for atmospheric models and extend predictions of localized variations in sulfur oxidation, aerosol formation, and radiative forcing in methane-rich environments.

Large-scale wetland restoration often focuses on repairing the hydrologic connections degraded by anthropogenic modifications. Of these hydrologic connections, groundwater discharge is an important target, as these surface water ecosystem control points are important for thermal stability, among other ecosystem services. However, evaluating the effectiveness of the restoration activities on establishing groundwater discharge connection is often difficult over large areas and inaccessible terrain. Unoccupied aircraft systems (UAS) are now routinely used for collecting aerial imagery and creating digital surface models (DSM). Lightweight thermal infrared (TIR) sensors provide another payload option for generation of sub-meter-resolution aerial TIR orthophotos. This technology allows for the rapid and safe survey of groundwater discharge areas. Aerial TIR water-surface data were collected in March 2019 at Tidmarsh Farms, a former commercial cranberry peatland located in coastal Massachusetts, USA (41°54'17\" N 70°34'17\" W), where stream and wetland restoration actions were completed in 2016. Here, we present a 0.4 km2 georeferenced, temperature-calibrated TIR orthophoto of the area. The image represents a mosaic of nearly 900 TIR images captured by UAS in a single morning with a total flight time of 36 min and is supported by a DSM derived from UAS-visible imagery. The survey was conducted in winter to maximize temperature contrast between relatively warm groundwater and colder ambient surface environment; lower-density groundwater rises above cool surface waters and thus can be imaged by a UAS. The resulting TIR orthomosaic shows fine detail of seepage distribution and downstream influence along the several restored channel forms, which was an objective of the ecological restoration design. The restored stream channel has increased connectivity to peatland groundwater discharge, reducing the ecosystem thermal stressors. Such aerial techniques can be used to guide ecological restoration design and assess post-restoration outcomes, especially in settings where ecosystem structure and function is governed by groundwater and surface water interaction.

Patients with COPD have increased peripheral airway resistance. In this study, increased peripheral airway resistance was strongly associated with a reduction in the number of terminal bronchioles rather than narrowing of airways. Direct measurement of the distribution of resistance in the lower respiratory tract has established that small airways (i.e., <2 mm in internal diameter) become the major sites of obstruction in patients with chronic obstructive pulmonary disease (COPD). 1 – 3 Resistance to flow through tubes is inversely related to the reduction in the radius raised to the fourth to fifth power. Since loss of half of such airways will only double the total peripheral resistance because of their parallel arrangement, 4 the increase in peripheral airway resistance by a factor of 4 to 40, as has been reported in patients with COPD, 1 is . . .

To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88-98%), sensitivity 92% (95% CI: 79-100%), specificity 94% (95% CI: 91-96%), and the area under the ROC curve 0.94 (95% CI: 0.89-1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.

ObjectiveTo examine association of COVID-19 with incident cardiovascular events in 17 871 UK Biobank cases between March 2020 and 2021.MethodsCOVID-19 cases were defined using health record linkage. Each case was propensity score-matched to two uninfected controls on age, sex, deprivation, body mass index, ethnicity, diabetes, prevalent ischaemic heart disease (IHD), smoking, hypertension and high cholesterol. We included the following incident outcomes: myocardial infarction, stroke, heart failure, atrial fibrillation, venous thromboembolism (VTE), pericarditis, all-cause death, cardiovascular death, IHD death. Cox proportional hazards regression was used to estimate associations of COVID-19 with each outcome over an average of 141 days (range 32–395) of prospective follow-up.ResultsNon-hospitalised cases (n=14 304) had increased risk of incident VTE (HR 2.74 (95% CI 1.38 to 5.45), p=0.004) and death (HR 10.23 (95% CI 7.63 to 13.70), p<0.0001). Individuals with primary COVID-19 hospitalisation (n=2701) had increased risk of all outcomes considered. The largest effect sizes were with VTE (HR 27.6 (95% CI 14.5 to 52.3); p<0.0001), heart failure (HR 21.6 (95% CI 10.9 to 42.9); p<0.0001) and stroke (HR 17.5 (95% CI 5.26 to 57.9); p<0.0001). Those hospitalised with COVID-19 as a secondary diagnosis (n=866) had similarly increased cardiovascular risk. The associated risks were greatest in the first 30 days after infection but remained higher than controls even after this period.ConclusionsIndividuals hospitalised with COVID-19 have increased risk of incident cardiovascular events across a range of disease and mortality outcomes. The risk of most events is highest in the early postinfection period. Individuals not requiring hospitalisation have increased risk of VTE, but not of other cardiovascular-specific outcomes.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses. While population-based screening would be ideal for early identification, available screening tools have limited accuracy. This study aims to determine whether machine learning models applied to health administrative and birth registry data can identify young children (aged 18 months to 5 years) who are at increased likelihood of developing ASD. We assembled the study cohort using individually linked maternal-newborn data from the Better Outcomes Registry and Network (BORN) Ontario database. The cohort included all live births in Ontario, Canada between April 1st, 2006, and March 31st, 2018, linked to datasets from Newborn Screening Ontario (NSO), Prenatal Screening Ontario (PSO), and Canadian Institute for Health Information (CIHI) (Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS)). The NSO and PSO datasets provided screening biomarker values and outcomes, while DAD and NACRS contained diagnosis codes and intervention codes for mothers and offspring. Extreme Gradient Boosting models and large-scale ensembled Transformer deep learning models were developed to predict ASD diagnosis between 18 and 60 months of age. Leveraging explainable artificial intelligence methods, we determined the impactful factors that contribute to increased likelihood of ASD at both an individual- and population-level. The final study cohort included 707,274 mother-offspring pairs, with 10,956 identified cases of ASD. The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. We determine that our model can be used to identify an enriched pool of children with the greatest likelihood of developing ASD, demonstrating the feasibility of this approach.This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. Ensemble transformer models applied to health administrative and birth registry data offer a promising avenue for universal ASD screening. Such early detection enables targeted and formal assessment for timely diagnosis and early access to resources, support, or therapy.