University of Connecticut

Autistic girls are often diagnosed late, missed, or misdiagnosed, which can negatively impact quality of life and mental health. Although research shows the social profiles of autistic girls differ from boys in systematic ways that might explain gaps in diagnosis, little is known about how autistic girls' social language compares to their same-sex non-autistic peers. This study investigated social words-words that make reference to other people-produced by 138 age- and IQ-matched autistic and non-autistic youth (ages 6-15) during one Autism Diagnostic Observation Schedule, Second Edition task. Girls used significantly more social words than boys across both diagnostic groups. There was a larger gap in social word production between autistic girls and non-autistic girls than autistic boys and non-autistic boys, with non-autistic girls using the most social words. Non-autistic girls' social language behavior-including their social word production-sets an especially high bar for autistic girls, who often report trying to blend in with other girls. Growing evidence of the distinct social language profiles of autistic and non-autistic girls versus boys should guide researchers and clinicians to assess autism in ways that are sensitive to sex-associated differences and develop interventions that consider the norms of youth's target social circles.Lay AbstractAutism is often diagnosed later in girls and women as compared to boys and men. More research is needed to understand how autism presents differently in girls. This study investigates how autistic and non-autistic youth aged 6 to 15 years use social words (e.g. "friend," "mom," "help," "talk") during an interview about friends, relationships, and marriage as part of an autism diagnostic assessment. Overall, girls used more social words compared to boys and talked more about friends. Specifically, non-autistic girls used the most social words in comparison with other groups. Highly social language produced by non-autistic girls may make it especially hard for autistic girls to blend in with other girls and could lead them to engage in more camouflaging behaviors to hide their autistic characteristics. With such different average social language behavior from girls and boys, researchers should consider adapting autism assessments and interventions to support the unique needs of autistic girls.

There is dual endemicity of malaria and helminthic infections in low- and middle-income countries. Coinfection with both infectious diseases may have compounding negative effects on the health of school-aged children (SAC) in endemic areas. The prevalence of coinfection with malaria and intestinal helminths, including soil-transmitted helminths, Schistosoma mansoni, and Hymenolepis nana, was assessed among SAC in rural northern Uganda. A school-based cross-sectional study was conducted at four randomly selected schools from June 2 to June 10, 2025 in Nwoya District, Uganda. Children aged 5-18 years provided stool and blood samples, and were surveyed to assess demographic, environmental, and health-related factors. Intestinal helminth diagnosis was performed using a modified Kato-Katz method (Odongo-Aginya stain); malaria was detected using Plasmodium falciparum (P. falciparum) histidine-rich protein 2 rapid diagnostic tests (RDTs) and blood smears. Overall, 190 SAC with a median age of 13 years were enrolled. Although 75.3% (n = 143) of SAC tested positive for P. falciparum on an RDT, only 13.2% (n = 25) had microscopically detected parasitemia. Intestinal helminth infections were uncommon and involved Ascaris lumbricoides (0.5%; n = 1), Hymenolepis nana (1.6%; n = 3), and Schistosoma mansoni (1.1%; n = 2). All SAC with intestinal helminth infections (3.2%; n = 6) had positive malaria RDT results. The high rate of malaria RDT positivity with negative blood smears may be due to persistent antigenemia post-treatment or false negative blood smear results due to chronic low-grade parasitemia. The low rate of malaria-helminth coinfection among SAC observed in the present study is supported by other recent studies in Uganda and suggests a high efficacy of national mass drug administration programming for the control of intestinal helminths.

Accurate short-term significant wave height forecasting is critical for coastal navigation, offshore energy operations, and hazard preparedness. However, operational wave forecast skill is often limited by uncertainty in wind forecasts. This study develops attention-based Long Short-Term Memory (LSTM) networks that explicitly address wind forecast uncertainty through noise-augmented training strategy and probabilistic modeling. Using wind and wave observations from the Western Long Island Sound (WLIS) buoy and operational North American Mesoscale (NAM) wind forecasts at Sikorsky Station, we characterize wind forecast errors and incorporate their statistical properties in model design. Three model variants are evaluated: (1) a perfect-future model trained with ideal future winds, (2) a noise-augmented model trained with controlled wind perturbations to enhance robustness, and (3) a probabilistic model that outputs both mean forecasts and uncertainty estimates. Results show that noise-augmented training reduced RMSE and MAE by approximately 7% compared to the perfect-future model and enhanced robustness to a broader range of wind forecast uncertainties. Furthermore, it led to smoother and more consistent performance across lead times. The probabilistic model achieved comparable accuracy while producing well-calibrated uncertainty estimates. Both models outperform a baseline relying solely on past buoy observations at all forecast horizons. These findings demonstrate that explicitly incorporating wind forecast uncertainty significantly improves wave height forecasting accuracy and reliability. The proposed framework offers a practical and robust pathway for operational wave forecasting systems under imperfect atmospheric forcing, enabling better-informed risk management and coastal decision-making. • Attention-based LSTM models using future winds outperform past-only baselines. • Noise-augmented training improves robustness to large wind forecast errors. • Probabilistic modeling provides calibrated uncertainty for risk-aware decisions. • Under forecast winds, proposed models reduce RMSE and MAE by about 7%.

Military surgical residency programs face unique challenges in preparing residents for the operational demands of combat casualty care. Over the past decade, significant educational innovations have been implemented to improve clinical competence and deployment readiness. To evaluate advancements in U.S. military surgical residency training between 2015 and 2025, focusing on educational interventions and their impact on resident preparedness across multiple domains of surgical education. A systematic review of literature from PubMed and Scopus was performed using defined MeSH and keyword criteria. Studies were included if they evaluated educational interventions involving surgical residents in U.S. military GME programs, reporting outcomes such as competency, readiness, or skill acquisition. Fifteen studies met inclusion criteria. Key innovations included simulation-based training in trauma, OB/GYN, and urology; deployment readiness curricula; live tissue and cadaveric courses; mentorship frameworks; and the integration of research platforms for skill sustainment. These interventions led to measurable improvements in confidence, performance, and readiness indices across residency programs. The past decade has seen a paradigm shift in military surgical education. Evidence based innovations are helping to align training with future operational demands, but further standardization and longitudinal outcomes research are needed.

Salt marshes in the northeastern United States support several specialized breeding bird species that are threatened by sea level rise (SLR) and coastal development, processes that drive habitat change and fragmentation. There have been rapid, widespread declines in some species, but mechanisms driving population change and whether declines continue remain unclear. We examined the influence of phenomena expected to modify salt marshes, including SLR, sediment delivery rates, and land use, on the population trajectories of saltmarsh breeding birds. We modeled population trajectories of 5 species with spatially extensive point count surveys conducted from Maine to Virginia from 2011 to 2022. We used Bayesian hierarchical abundance models and model selection to identify phenomena that had the strongest effect on population change. Clapper rails (Rallus crepitans) continued their long-term decline (-4.1%/year). Willets (Tringa semipalmata semipalmata) (2.6%/year) and saltmarsh sparrows (Ammospiza caudacuta) (4.1%/year) increased, and seaside (Ammospiza maritima) and Nelson's (Ammospiza nelsoni subvirgatus) sparrows exhibited no clear change in abundance. The estimated increase for saltmarsh sparrow was not consistent with trends over the previous 25years but aligned with prior demographic modeling, which predicted a short-term stabilization during the study years before an expected return to a long-term decline. Road density and other tidal restrictions near marshes were generally good predictors of abundance over the study period, as was marsh habitat composition. Local rates of SLR and sediment delivery were not as good predictors. During periods of relatively low rates of realized SLR, local-scale drivers of population trends had relatively stronger effects than global drivers on the persistence of several saltmarsh breeding birds. Conservation practitioners, however, should be attentive to global drivers, especially as rates of SLR accelerate in the future.

There are limited data on seasonal variations and outcomes in pulmonary embolism (PE). To understand the seasonal variations in PE outcomes. All adult (³18 years) non-elective admissions with a primary diagnosis of PE were identified using the National Inpatient Sample (2016-2022) and assessed by the season of admission-spring (March-May), summer (June-August), fall (September-November), and winter (December-February). The primary outcome was in-hospital mortality across the seasons. Secondary outcomes included total hospitalization costs, hospital length of stay, and discharge disposition. During the 7-year period, 1,278,754 PE admissions were identified. Spring, summer, fall, and winter had 24.1%, 25.0%, 25.3%, and 25.6% admissions, respectively (p < 0.001). The baseline and in-hospital characteristics were comparable across cohorts with no significant clinical differences noted. Mechanical thrombectomy rates were highest in fall (spring 2.5%, summer 3.0%, fall 3.5%, winter 2.7%, p < 0.001), whereas systemic thrombolysis (spring 2.9%, summer 3.0%, fall 2.9%, winter 3.1%, p = 0.04) was highest in the summer. Use of other therapies were comparable. There was no statistical difference in adjusted in-hospital mortality rates among seasons. There were slight variations in hospitalization costs, length of stay, and discharge dispositions across the four cohorts but were not clinically relevant. In this study, there was no significant seasonal variation in in-hospital mortality from PE.