Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by difficulties in social communication and having repetitive behaviors or interests. Sensory processing disorder (SPD) is a term used to describe a collection of symptoms stemming from atypical neural integration of sensory information. Evidence from animal models suggests a significant role of GABAergic signaling in the amygdala in regulating behaviors related to anxiety. Previous research suggests that an imbalance in inhibitory and excitatory signaling in neural connections is present in ASD, a neurodevelopmental disorder that is associated with high rates of co-morbid anxiety (up to 40%). This current study investigated the relationship between anxiety and neurochemical profiles in high-functioning children between 8 and 12 years of age with ASD and a clinical comparison group, children with SPD. Our hypothesis is that there are underlying biological differences in amygdalar functioning between children with ASD and those with SPD, despite these two groups sharing similarities in sensory sensitivity and comorbid anxiety. To explore this hypothesis, a composite of anxiety symptoms was created (α = 0.790) using questions from the Child Sensory Profile-2, Vineland Adaptive Behavior Scales, and the DSM-5 Cross-Cutting Symptom Measure. Presence of anxiety was also investigated using the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS). Using a 3T MR system, brain metabolite concentrations (i.e., γ-Amino Butyric Acid (GABA) levels) in the left amygdala-hippocampal region were measured using single-voxel proton magnetic resonance imaging (1H-MRS). We ran a regression analysis looking at the interaction between anxiety symptoms and amygdalar GABA levels in the ASD and SPD groups while controlling for autism symptoms.

Digital media and mobile applications used for leisure have become increasingly interwoven into people’s daily lives. Although playful mobile applications and their effects on society have been studied from a psychological, business, and computer science perspective, they have not been heavily engaged in the field of geography. Using Pokémon GO as a case study, this work aims to explore the ties between the corporate history and digital data of Niantic (the company that made Pokémon GO) and the effect it imposes on the “real world”. It aims to conduct a spatial analysis of pokéstops and pokégyms in King County, Washington. In order to do this, a discourse analysis of online documents from Niantic, newspapers, social media, and fan websites recounting the company's history with previous applications and press interviews was conducted. In addition, a hotspot spatial analysis of Pokémon GO (pokéstops and pokégyms) overlaid on American Community Survey data was created. The purpose of this analysis is to see potential spatial inequalities that the application may have created. The findings show Niantic’s lack of ethical consideration by how it deals with users data and privacy and as a result shows how the users have responded to issues in the past. The results also illustrate the skewing of data toward urban tech centers and the relative sparsity in rural areas, rendering use of the app pointless for certain users. Pokémon GO is the first location-based augmented reality mobile application to garner worldwide attention, and many application developers will likely use it as a point of reference when creating their own applications. It is imperative that developers and researchers learn from this application’s successes as well as its failures.

Prevention of aquatic invasive species transmission by recreational fishing and boating is a fundamental management challenge. These activities can entrain non-native plants and animals via tangled lines, bait buckets, or hull encrustation, leading to introductions into new waterbodies. With hundreds of millions of people participating in fishing trips each year, understanding angler movement behavior can provide critical insight into the most effective locations and scales at which to apply preventative measures. Angler behavior is often inferred from infrequently and sparsely conducted surveys that provide limited spatial and temporal insight into this challenge. Here we capitalize on a big data opportunity provided by ReelSonar’s recently launched iBobber, a sonar-enabled bobber with over 3,000,000 records of fishing location, water depth, and environmental variables collected over three years. By quantifying geographic patterns of fishing activities and assessing how these patterns change seasonally, we explore angler behavior in terms of fishing frequency and distance traveled between sites, and characterize the attributes of fished ecosystems. Our study offers novel insight into spatiotemporal patterns of angler behavior and carries important implications for predicting and preventing future transmission of aquatic invasive species via recreational fishing.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, as well as restrictive or repetitive behaviors and interests. Some individuals with autism demonstrate deficits in face memory and recognition, which may contribute to overall social impairments. Increasing evidence also suggests that ASD is associated with abnormal sensory processing, including within the olfaction pathway, although results vary as to which components of olfaction, including odor identification, might be affected. Both odor identification and face recognition have been associated with activity in the orbitofrontal cortex (OFC), a brain region possibly implicated in ASD. The current study aims to measure odor identification and face memory performance in children with ASD compared to children who are typically developing (TD) and children with sensory processing difficulties (SPD), and assess whether there is a relationship between performance on face memory and odor identification tasks. Odor identification will be measured using University of Pennsylvania Smell Identification Test (UPSIT), a 40 item “scratch n’ sniff” activity, and face recognition will be assessed using the Wechsler Memory Scale III (WMS III); children will complete both the Faces I and Faces II subtests, which assess immediate recognition of faces and delayed recognition of faces respectively. Data collection for this study is ongoing; final analyses will consist of a Student’s t-test to assess for group differences on the odor identification and face recognition tasks, and Pearson’s correlation analyses to detect any significant associations between these tasks, using a significance threshold of α=.05. We hypothesize that children with ASD will show reduced performance on both the odor identification task and face recognition tasks compared to the TD and SPD groups, and that performance on the UPSIT will be strongly correlated to WMS III scores, reflecting the shared underlying role of the OFC.

During development, profound changes occur in the liver from a hematopoietic organ to a major organ for xenobiotic biotransformation. Developmental regulation of epigenetic modifiers, such as factors involved in DNA methylation and histone modifications, may modify the transcriptional output of drug-metabolizing enzymes. The goal of this study was to develop an in vitro model to recapitulate human liver development using HepaRG cells and implement this model in investigating the developmental regulation of epigenetic modifiers and drug-metabolizing enzymes. Previously, HepaRG cells were maintained in proliferative phase and were tested up to 14 days at fully differentiated stage. In this study, we collected cells during differentiation from 0 to 14 days, and isolated total RNA for RT-qPCR of epigenetic modifiers and drug-metabolizing enzymes. Among a panel of 92 epigenetic genes, there were 52 genes differentially regulated at earlier time points as compared to the fully differentiated stage. Most of these epigenetic modifiers increased during differentiation and reached a plateau either at Day 7 or Day 14; whereas the class II major histocompatibility complex transactivator (CIITA), which has an acetyltransferase domain, gradually decreased during differentiation. The adult hepatocyte marker albumin gradually increased and reached a peak at Day 7, which decreased thereafter. The mRNA of the fetal-specific drug-metabolizing enzyme cytochrome P450 3A7 (CYP3A7) was high between Day 2 and Day 3 but decreased rapidly thereafter. Conversely, CYPA3A4 mRNA was increased till Day 7, when peak mRNA levels were observed, and decreased thereafter. In conclusion, the present study is a first step in establishing a working model to investigate human liver development in vitro.

Global temperatures have been on the rise since preindustrial times due to an increased concentration of carbon dioxide in our atmosphere. Learning about how past climates have responded to changes in carbon dioxide concentrations is important to understand how our current climate will respond to atmospheric changes. Previous studies have tried to constrain the warming that occurred after the Last Glacial Maximum (LGM, ~20,000 years ago), for example in the Central Rocky Mountains, USA, where glacial modeling-based estimates suggest temperature change from the LGM to the modern (interglacial) climate was 5-10 °C. However, these glacier-based temperature estimates were influenced by other factors such as precipitation and seasonality, giving them large uncertainties. This study will use carbonate samples from soils from the LGM and modern interglacial (<3,000 years before present) to develop a more precise estimate of the amount of warming since the LGM. We will measure the clumped oxygen and carbon isotopes of samples collected from the Central Rocky Mountains and arid Western United States. The clumped isotopes measure soil temperature directly, providing a robust proxy for temperature change. This study will also investigate the time of year soil carbonates form, which is important for interpreting the soil temperature recorded by clumped isotopes. Through the use of clumped isotopes we will improve temperature change estimates, which will help improve climate models for the future.