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Autism spectrum disorder (ASD) is a neurodevelopmental disability affecting 1 in 44 children nationally. Timely referral to intervention and support services for ASD has consistently demonstrated significant long-term positive effects on symptoms and subsequent skills and family outcomes. This paper highlights a novel and innovative approach of short-term consultations for best practices to ultimately increase access to timely ASD evaluations and support through a state-wide school telehealth initiative. The goal of the initiative was to facilitate rapid access to ASD evaluation and support (within 2 weeks of referral in school-age youth) among under-resourced school systems in Central Texas. First, this paper provides background relative to current disparities that many children face when attempting to obtain ASD evaluations and support. Second, a statewide legislative response is described that created a mental health consortium targeting increased access to broad-based mental health services and supports, particularly in underserved areas. Next, the Texas Child Health Access Through Telemedicine (TCHATT) program is outlined with a particular focus on day-to-day operationality and concentrated efforts to increase mental health literacy among school teams relative to ASD symptoms and co-occurring behavioral health concerns. Year 1 utilization data metrics (September 2020-May 2021) and representative case examples are presented. Finally, early "lessons learned" and future directions are discussed.

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca 2 + , Cl − , and Na + . We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. [ABSTRACT FROM AUTHOR]

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2. [ABSTRACT FROM AUTHOR]