Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that often results in deficits in communication, social skills, and emotion regulation. Additional concerns include disruptions to the sleep wake cycle that results from circadian rhythm dysfunction. 40% of individuals with ASD also have clinically significant anxiety, which tends to exacerbate pre-existing behavioral issues and social deficits. Previous studies suggest an association between increased sleep dysfunction and increased issues with anxiety in typically developing (TD) adults, and have insinuated a possible bidirectional relationship between the two. This study aims to look at the relationship between sleep quality and anxiety in adults with and without ASD. 89 adults (ASD=39) from a longitudinal five-site NIH-funded study on sex differences in autism were included. Participants completed the Munich Chronotype Questionnaire (MCTQ), a measure of the amount of sleep, based on sleep-wake times, and the Screen for Adult/Child Anxiety Related Disorders for a measure of generalized anxiety. Analysis will include (1) independent sample t-tests to examine group differences in anxiety and sleep duration and (2) correlations between sleep duration (MCTQ) and generalized anxiety subscore from the SCAARED measure for the group with ASD and the typically developing group. I hypothesize that individuals with ASD compared to TD will demonstrate higher anxiety scores and worse sleep quality. I also hypothesize that there will be a correlation between higher anxiety scores and shorter sleep duration. Additionally we will explore sex differences in anxiety and sleep. If sleep quality is related to anxiety, this might support the increased use of sleep behavioral interventions to improve mental health in individuals with autism spectrum disorders.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communicative impairments and sensory sensitivities. Additionally, the physical and social changes that occur with puberty may be a turbulent time for adolescents. Earlier pubertal timing has been correlated with higher internalizing mental health symptoms for neurotypical girls with earlier onset of menarche in females being tied with higher rates of depression that persists into adulthood. This study investigates the relation between pubertal timing and internalizing mental health problems for autistic and non-autistic female adolescents in a longitudinal study. 23 female ASD participants (ages 8 to 17) and 42 female neurotypical participants (ages 8 to 17) from a NIH-funded project investigating sex and gender differences in individuals with autism are included. Participant data was collected at a second timepoint, 3 to 8 years later. Data on pubertal development was collected using the Pubertal Development Scale, a parent or self-report measure of physical development. Depression and anxiety were assessed using the Child Behavior Check List, a parent-report behavioral checklist of mental health symptoms at the first time point, and a self-report version of the CBCL at the second time point. First, we examine pubertal timing variation by calculating residuals of a pubertal maturation by time regression plot. Second, we will investigate the relationship between puberty timing and depression and anxiety using a correlation test. To further analyze this relationship between pubertal timing predicting future depression and anxiety, we will run a multiple regression test. I predict that the relationship between pubertal timing and depression and anxiety will be greater for autistic girls than for neurotypical girls. This study’s data can add a neurodiverse perspective on how pubertal timing impacts mental health in females and could provide evidence for the need of interventions and additional support to adolescent females with ASD.

Autism Spectrum Disorders (ASD) refer to neurodevelopmental difficulties in communication and social interaction. It is thought that 94% of autistic adults have used camouflaging behaviors at some point in their lives, meaning that they have developed certain behaviors to blend in the social world and to “hide” their autistic differences. Camouflaging behaviors include: masking - hiding the autistic features; compensation - practicing certain behaviors to compensate for certain social shortcomings; and assimilation - trying to fit in so they are not singled out (Hull et al., 2018). We are interested in the relationship between camouflaging behaviors and social communication in individuals with and without autism. Data from 85 participants (42 ASD, 48 females) ranging from 15 to 23 years old from the NIH funded study on sex and differences in autism were included in the analysis. Autism diagnosis was confirmed via standardized tests and all participants had an IQ of 70 or higher. Participants completed the Camouflaging Autistic Traits Questionnaire (CAT-Q), a 25-item questionnaire that tests the degree of using camouflaging strategies, and Vineland Adaptive Behavior Scales, a parental interview that informs the diagnosis of intellectual and developmental disabilities. We predict significantly higher camouflaging behaviors and lower socialization skills in the autistic group compared to the non-autistic group. We predict a positive correlation between CAT-Q scores and Vineland socialization scores in the autistic group, since by resembling their peers will make their parents report better social skills. We also predict that the correlation between masking and social skills will be higher in females than males in both groups, as females are found to have higher social motivations (Cook, Ogden, & Winstone, 2018). Camouflaging may prevent others from recognizing the symptoms of autism and fail to get diagnosis. Therefore, it is important to detect camouflaging behaviors so autistic children get timely treatments.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by differences in social interaction and communication, as well as repetitive behaviors (APA 2013). Per Kanne and Mazurek (2011), 56% of 1380 children and adolescents diagnosed with ASD engaged in some form of aggression towards family, teachers, or peers. Aggressive behaviors can prevent youth with ASD from being able to engage in learning opportunities and community events. An important factor in decreasing rates and severity of aggression is through the identification of social and environmental factors that may impact the stability of aggressive behaviors. The aim of this study is to look at factors that may impact the stability of aggressive behaviors in a longitudinal sample of autistic and non-autistic youth. 44 participants (22 ASD) aged 8 to 18 years, from the NIH funded longitudinal study on sex differences in autism were included in the analysis. Aggression was measured using the Child Behavior Checklist (CBCL), a parental report measure on problem behaviors, which includes items related to self aggression (self injurious behaviors) and other-aggression (aggression towards peers and adults). Data was collected at baseline and 3 to 8 years later. We expect low IQ and younger age will be related to higher aggressive scores on CBCL at baseline. We predict that youth with greater social improvement between timepoints and use of psychotropic medication will have lower levels of aggression over time. Identification of factors that impact changes in aggression over time can aid in implementing interventions to aggression and improve quality of life.

Meadow and wetland areas serve as critical habitats for many native species in the Pacific Northwest and provide countless ecosystem services such as carbon sequestration, sediment removal, and increased biodiversity. However, most wetland and meadows inventories are incomplete or are biased towards those that have been mapped on the ground and those that are easy to detect directly in aerial and satellite imagery. Furthermore, due to habitat loss and climate change effects, the current state of these ecosystems is highly dynamic and unknown. Remote sensing provides landscape analysis capabilities to map current habitats and compile and analyze variables such as elevation, slope, soil type, wetness, and seasonal glacial recession that may predict where these fragile habitats could be. The goal of our research was to identify and map wetlands and meadows in Mount Rainier National Park. Using topographic, vegetative, hydrologic, and soil indices paired with data points to train the machine learning model, we get an output of wetland and meadow probability. Our work, co-produced with National Park Service biologists, contributes to the database of potential wetland ecosystems in Mount Rainier National Park. and improves remote sensing methodology to predict meadow habitats. WIP tool outputs of predicted wetlands and meadows had a high overall accuracy with documented wetlands and meadows in Mount Rainier National Park. Ultimately, by providing models, processes, and continuing to add to knowledge of wetland and meadow habitats, we anticipate a large potential for expansion to other NPS-protected lands.

The ocean and atmosphere interact through air-sea exchanges of heat and energy across the air-sea interface. These air-sea fluxes have important implications on global weather and climate patterns. Because estimation of covarying turbulent variations is not feasible in Numerical Weather Prediction (NWP) models, the turbulent air-sea exchanges are typically estimated using bulk air-sea flux algorithms based on state variables. However there are large differences in the values estimated by different NWP and even when they agree, without a reference data set, it may be that all NWP are equally biased. For this project, I used in situ observations collected by Saildrone Uncrewed Surface Vehicles (USV) in the central tropical Pacific to assess bulk flux estimates from multiple atmospheric reanalyses including NCEP Climate Forecast System Reanalysis (CFSR), ECMWF Reanalysis v5 (ERA5), NCEP/NCAR Reanalysis 1 (NCEP1), and NCEP/DOE Reanalysis 2 (NCEP2). Preliminary results, based upon hourly, spatially-interpolated, co-located values that are then made into 24-hour “daily” averages, indicate that all of the reanalyses had a strong correlation with USV observations for net heat flux and net SWR, but the correlation was much weaker (0.5 to 0.7) for other flux components and very weak (~0.25) for the net longwave radiation for NCEP1 and NCEP2. The root mean square errors for the 24-hour-averaged differences were 55 to 66 W/m^2 for solar radiation and 20 to 30 W/m^2 for latent heat flux. In my analysis of my results, I looked at the differences region by region for each of the flux components and state variables as well as for each of the products. As Saildrone technology becomes more widely used and more intercomparison studies such as this are conducted, observations from Saildrones could eventually be integrated into NWP models, possibly improving forecast accuracy.