Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations

High-speed imaging and wavefront sensing with an infrared avalanche photodiode array

Infrared avalanche photodiode arrays represent a panacea for many branches of astronomy by enabling extremely low-noise, high-speed and even photon-counting measurements at near-infrared wavelengths. We recently demonstrated the use of an early engineering-grade infrared avalanche photodiode array that achieves a correlated double sampling read noise of 0.73 e- in the lab, and a total noise of 2.52 e- on sky, and supports simultaneous high-speed imaging and tip-tilt wavefront sensing with the Robo-AO visible-light laser adaptive optics system at the Palomar Observatory 1.5-m telescope. We report here on the improved image quality achieved simultaneously at visible and infrared wavelengths by using the array as part of an image stabilization control-loop with adaptive-optics sharpened guide stars. We also discuss a newly enabled survey of nearby late M-dwarf multiplicity as well as future uses of this technology in other adaptive optics and high-contrast imaging applications.Comment: Accepted to Astrophysical Journal. 8 pages, 3 figures and 1 tabl

Utilizing Mixed Graphical Network Models to Explore Parent Psychological Symptoms and Their Centrality to Parent Mental Health in Households with High Child Screen Usage

Especially among adolescents, screens are being used more than ever. In conjunction with this trend, mental illness is increasingly prevalent among both adults and children, and parental psychological problems are shown to be associated with children\u27s TV watching, video watching, and gaming (Pulkki-Råback et al., 2022). This study aims to approach parent mental illness symptom by symptom to explore which specific symptoms are most central to parent psychological problems in households where children show high screen time behaviors. We draw from the Adolescent Brain Cognitive Development Study (ABCD Study®), a nationwide sample of 11,875 children aged 10-13 collected by the National Institute of Mental Health. We utilize Mixed Graphical Models (MGMs) on both polychoric and dichotomized data, using the Extended Bayesian Information Criterion to choose the best models. Within our polychoric data, we pinpoint “I feel worthless and inferior” as a symptom with both high bridge betweenness and strength between symptom communities within high screen time household networks. Within binary high child screen time networks, we find “I have trouble making decisions” as a parent symptom with high bridge strength and betweenness that is central to the overall structure of the network. Finally, we believe our approach could be more successfully applied to other psychological datasets with more nonzero responses to parent psychological symptoms to further illuminate parent symptoms that are important in households with high child screen time. Our analyses do not establish causality because our data is cross-sectional

Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment

Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R 2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R 2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference

Impact of California's Transitional Kindergarten Program, 2013-14

Transitional kindergarten (TK)—the first year of a two-year kindergarten program for California children who turn 5 between September 2 and December 2—is intended to better prepare young five-year-olds for kindergarten and ensure a strong start to their educational career. To determine whether this goal is being achieved, American Institutes for Research (AIR) is conducting an evaluation of the impact of TK in California. The goal of this study is to measure the success of the program by determining the impact of TK on students' readiness for kindergarten in several areas. Using a rigorous regression discontinuity (RD) research design,1 we compared language, literacy, mathematics, executive function, and social-emotional skills at kindergarten entry for students who attended TK and for students who did not attend TK. Overall, we found that TK had a positive impact on students' kindergarten readiness in several domains, controlling for students' age differences. These effects are over and above the experiences children in the comparison group had the year before kindergarten, which for more than 80 percent was some type of preschool program

Constraints on terrestrial planet formation timescales and equilibration processes in the Grand Tack scenario from Hf-W isotopic evolution

We examine 141 N-body simulations of terrestrial planet late-stage accretion that use the Grand Tack scenario, coupling the collisional results with a hafnium-tungsten (Hf-W) isotopic evolution model. Accretion in the Grand Tack scenario results in faster planet formation than classical accretion models because of higher planetesimal surface density induced by a migrating Jupiter. Planetary embryos that grow rapidly experience radiogenic ingrowth of mantle $^{182}$W that is inconsistent with the measured terrestrial composition, unless much of the tungsten is removed by an impactor core that mixes thoroughly with the target mantle. For physically Earth-like surviving planets, we find that the fraction of equilibrating impactor core $k_\text{core} \geq 0.6$ is required to produce results agreeing with observed terrestrial tungsten anomalies (assuming equilibration with relatively large volumes of target mantle material; smaller equilibrating mantle volumes would require even larger $k_\text{core}$). This requirement of substantial core re-equilibration may be difficult to reconcile with fluid dynamical predictions and hydrocode simulations of mixing during large impacts, and hence this result does not favor the rapid planet building that results from Grand Tack accretion.Comment: 34 pages, 5 figures, published in EPS

The discovery and dynamical evolution of an object at the outer edge of Saturn's A ring

This work was supported by the Science and Technology Facilities Council (Grant No. ST/F007566/1) and we are grateful to them for financial assistance. C.D.M. is also grateful to the Leverhulme Trust for the award of a Research Fellowshippublisher PDF not permitted, withdraw

Second generation Robo-AO instruments and systems

The prototype Robo-AO system at the Palomar Observatory 1.5-m telescope is the world's first fully automated laser adaptive optics instrument. Scientific operations commenced in June 2012 and more than 12,000 observations have since been performed at the ~0.12" visible-light diffraction limit. Two new infrared cameras providing high-speed tip-tilt sensing and a 2' field-of-view will be integrated in 2014. In addition to a Robo-AO clone for the 2-m IGO and the natural guide star variant KAPAO at the 1-m Table Mountain telescope, a second generation of facility-class Robo-AO systems are in development for the 2.2-m University of Hawai'i and 3-m IRTF telescopes which will provide higher Strehl ratios, sharper imaging, ~0.07", and correction to {\lambda} = 400 nm.Comment: 11 pages, 4 figures, 3 table

Inhibitor binding mode and allosteric regulation of Na+-glucose symporters.

Sodium-dependent glucose transporters (SGLTs) exploit sodium gradients to transport sugars across the plasma membrane. Due to their role in renal sugar reabsorption, SGLTs are targets for the treatment of type 2 diabetes. Current therapeutics are phlorizin derivatives that contain a sugar moiety bound to an aromatic aglycon tail. Here, we develop structural models of human SGLT1/2 in complex with inhibitors by combining computational and functional studies. Inhibitors bind with the sugar moiety in the sugar pocket and the aglycon tail in the extracellular vestibule. The binding poses corroborate mutagenesis studies and suggest a partial closure of the outer gate upon binding. The models also reveal a putative Na+ binding site in hSGLT1 whose disruption reduces the transport stoichiometry to the value observed in hSGLT2 and increases inhibition by aglycon tails. Our work demonstrates that subtype selectivity arises from Na+-regulated outer gate closure and a variable region in extracellular loop EL5

Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model\u27s performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD