Indiana University Bloomington

Background: Despite the growing interest in the construct of drinker identity and empirical evidence for its role in drinking behavior, there is a paucity of papers that evaluate and integrate the results of studies on drinker identity, leaving a gap in our knowledge of the importance of the drinker identity construct. The current paper addresses this gap by reviewing and integrating the results of the studies of drinker identity. Methods: The scoping review identified, retrieved, and evaluated the existing literature regarding drinker identity. English language studies from EBSCOHost, PubMed, ScienceDirect, and Scopus databases were reviewed. Studies were included in the review if they were data-based studies or theoretical publications with drinker identity as the primary topic published in peer-reviewed journals. Studies were reviewed and coded based on their reported methodology and findings and codes were used to integrate and present findings. Results: This review advances this line of research in four ways. First, the operationalization of drinker identity is evaluated by examining the theoretical frameworks defining the construct. Second, the conceptualization and measurement of drinker identity is assessed, with suggestions for future measurement research. Third, an integrated framework of predictors, outcomes, moderators, and mediators is presented. Finally, the research gaps, future recommendations, and clinical implications are discussed. Conclusions: There is a need for continued research, specifically research which aims to standardize and improve measurement of drinker identity, considers longitudinal and developmental processes, and broadens the research samples and settings.

pioids are commonly used for treating chronic pain. However, with continued use, they may induce tolerance and/or hyperalgesia, which limits therapeutic efficacy. The human mechanisms of opioid-induced tolerance and hyperalgesia are significantly understudied, in part, because current models cannot fully recapitulate human pathology. Here, we engineered novel human spinal microphysiological systems (MPSs) integrated with plug-and-play neural activity sensing for modeling human nociception and opioid-induced tolerance. Each spinal MPS consists of a flattened human spinal cord organoid derived from human stem cells and a 3D printed organoid holder device for plug-and-play neural activity measurement. We found that the flattened organoid design of MPSs not only reduces hypoxia and necrosis in the organoids, but also promotes their neuron maturation, neural activity, and functional development. We further demonstrated that prolonged opioid exposure resulted in neurochemical correlates of opioid tolerance and hyperalgesia, as measured by altered neural activity, and downregulation of μ-opioid receptor expression of human spinal MPSs. The MPSs are scalable, cost-effective, easy-to-use, and compatible with commonly-used well-plates, thus allowing plug-and-play measurements of neural activity. We believe the MPSs hold a promising translational potential for studying human pain etiology, screening new treatments, and validating novel therapeutics for human pain medicine.

Fully Homomorphic Encryption (FHE) enables secure offloading of computations to untrusted cloud servers as it allows computing on encrypted data. However, existing well-known FHE schemes suffer from heavy performance overheads. Thus numerous accelerations based on FPGAs, ASICs, and GPUs have been proposed. Compared to FPGAs and ASICs, GPUs have obvious advantages in productivity and development costs. And also, GPUs have already been widely deployed in commercial cloud or supercomputing centers. Therefore, we present HE-Booster, an efficient GPU-based FHE acceleration design. For single-GPU acceleration, a thorough systematic design is exploited to map five common phases in typical FHE schemes to the GPU parallel architecture. In particular, inspired by the regular architecture of NTT/INTT, a novel inter-thread local synchronization is proposed to exploit thread-level parallelism. For multi-GPU acceleration, we propose a scalable parallelization design that exploits data-level parallelism through fine-grained data partition under different representations. Finally, experiments on 1 NVIDIA GPU demonstrate that our work outperforms 251.7×, 78.5× and 164.9× than three mainstream CPU-based libraries HElib, SEAL, and PALISADE, and up to 170.5× speedup is obtained compared to the GPU-accelerated library cuHE. What's more, performing 8 homomorphic multiplications on 8 GPUs can deliver up to a 7.66× performance boost compared to a single-GPU implementation.

Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments. In this work, we treat visual navigable space segmentation as a scene decomposition problem and propose P olyline S egmentation V ariational autoencoder Net work (PSV-Net), a representation learning-based framework for learning the navigable space segmentation in a self-supervised manner. Current segmentation techniques heavily rely on fully-supervised learning strategies which demand a large amount of pixel-level annotated images. In this work, we propose a framework leveraging a Variational AutoEncoder (VAE) and an AutoEncoder (AE) to learn a polyline representation that compactly outlines the desired navigable space boundary. Through extensive experiments, we validate that the proposed PSV-Net can learn the visual navigable space with no or few labels, producing an accuracy comparable to fully-supervised state-of-the-art methods that use all available labels. In addition, we show that integrating the proposed navigable space segmentation model with a visual planner can achieve efficient mapless navigation in real environments.

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Recent results demonstrate that inducing an abstract representation of target analogs at retrieval time aids access to analogous situations with mismatching surface features (i.e., the late abstraction principle). A limitation of current implementations of this principle is that they either require the external provision of target-specific information or demand very high intellectual effort. Experiment 1 demonstrated that constructing an idealized situation model of a target problem increases the rate of correct solutions compared with constructing either concrete simulations or no simulations. Experiment 2 confirmed that these results were based on an advantage for accessing the base analog, and not merely an advantage of idealized simulations for understanding the target problem in its own terms. This target idealization strategy has broader applicability than prior interventions based on the late abstraction principle because it can be achieved by a greater proportion of participants and without the need to receive target-specific information. We present a computational model, SampComp, that predicts successful retrieval of a stored situation to understand a target based on the overlap of a random, but potentially biased, sample of features from each. SampComp is able to account for the relative benefits of base and target idealization, and their interaction.

This article offers an overview of a special issue that focuses on the reciprocal relationship between the digital tools and spaces that we use and the methodologies and methods that we take up in designing and carrying out a qualitative research study. In this editorial, we situate the special issue in the methodological literature around technologies and qualitative inquiry. We also provide an overview of the seven included articles, noting how each article offers new perspectives on the consequences of adopting digital research workflows.

Strong statistical capacity is a prerequisite for producing reliable statistics that helps monitor a country’s governance and economic performance. This is particularly relevant for a large number of poorer countries, which have weaker statistical capacity but have to rely more on these statistics for various objectives such as monitoring poverty reduction or reporting to international donors. We present the Statistical Performance Indicators and Index (SPI) as the World Bank’s new official tool to measure country statistical capacity. The SPI is conceptually motivated, builds on a mathematical foundation, and significantly expands the number of indicators and the number of covered countries compared to its predecessor. The new index has a strong correlation with other common development indicators such as GDP per capita, governance, human capital, poverty, and inequality. It can also accommodate future improvements as the global data landscape evolves.

Introduction: Three medications are Food and Drug Administration approved for the treatment of opioid use disorder (OUD); however, these medications are underused within prisons, which elevates the risk of relapse and overdose when persons with opioid use disorder (POUD) are released. Research is scant regarding the multilevel factors associated with POUDs' willingness to initiate medication treatment for opioid use disorder (MOUD) while in prison and their continued engagement in treatment after release. Furthermore, rural and urban populations have not been compared. The Geographic variation in Addiction Treatment Experiences (GATE) study seeks to identify multilevel factors (ie, individual, personal network, and structural factors) influencing prison-based extended-release injectable naltrexone (XR-NTX) and buprenorphine initiation and will examine predictors of postrelease MOUD use and adverse outcomes (ie, relapse, overdose, recidivism) among both rural and urban POUDs. Methods and analysis: This mixed methods study employs a social ecological framework. A prospective observational longitudinal cohort study is being conducted with 450 POUDs using survey and social network data collected in prison, immediately postrelease, 6 months postrelease and 12 months postrelease to identify multilevel rural-urban variation in key outcomes. In-depth qualitative interviews are being conducted with POUDs, prison-based treatment staff and social service clinicians. To maximise rigour and reproducibility, we employ a concurrent triangulation strategy, whereby qualitative and quantitative data contribute equally to the analysis and are used for cross-validation when examining scientific aims. Ethics and dissemination: The GATE study was reviewed and approved by the University of Kentucky's Institutional Review Board prior to implementation. Findings will be disseminated through presentations at scientific and professional association conferences, peer-reviewed journal publications and a summary aggregate report submitted to the Kentucky Department of Corrections.

Self-regulation can facilitate modifications in lifestyle to promote behavioral change. However, little is known about whether adaptive interventions promote improvement in self-regulatory, dietary, and physical activity outcomes among slow treatment responders. A stratified design with an adaptive intervention for slow responders was implemented and evaluated. Adults ≥ 21 years old with prediabetes were stratified to the standard Group Lifestyle Balance intervention (GLB; n = 79) or the adaptive GLB Plus intervention (GLB + ; n = 105) based on first-month treatment response. Intake of total fat was the only study measure that significantly differed between groups at baseline (P = 0.0071). GLB reported greater improvement in self-efficacy for lifestyle behaviors, goal satisfaction with weight loss, and very active minutes of activity than GLB + (all P < 0.01) at 4-months. Both groups reported significant improvement in self-regulatory outcomes and reduction in energy and fat intake (all P < 0.01). An adaptive intervention can improve self-regulation and dietary intake when tailored to early slow treatment responders.

SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n = 55,406 unique patients) using the University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality from 01/01/2020 to 04/26/2022. Variants were identified using the CDC data tracker. Increased odds of death were observed amongst unvaccinated individuals and fully vaccinated, partially vaccinated, or individuals who received any vaccination during multiple waves of the pandemic. Vaccination status was associated with survival and a decreased frequency of many respiratory features. More recent SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.

The purpose of the study was to examine preservice music teachers’ degree of preparedness, self-efficacy, and commitment to incorporating popular music into their P-12 music classrooms, as well as the relationship among the constructs in popular music teaching. Preservice music educators ( N = 81) from 23 music education programs in the United States completed a researcher-constructed questionnaire to rate their preparedness, self-efficacy, and commitment to incorporate popular music. Participants reported a moderate degree of preparedness to teach popular music and moderate popular music teaching self-efficacy. Moreover, participants rated their commitment to incorporating popular music relatively high. Correlation analyses revealed significant and positive correlations between preparedness and self-efficacy, as well as preparedness and commitment. In addition, a positive correlation was found between teachers’ self-efficacy for popular music teaching and their commitment to incorporating popular music. Additional findings include significant correlations between preservice educators’ preparedness to teach various musical genres and their self-efficacy.

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning. Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier. Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC. Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.

The dynamics of binary stars provides a unique avenue to gather insight into the study of the structure and dynamics of star clusters and galaxies. In this paper, we present the results of a set of N-body simulations aimed at exploring the evolution of binary stars during the early evolutionary phases of ultra-faint dwarf galaxies (UFD). In our simulations, we assume that the stellar component of the UFD is initially dynamically cold and evolves towards its final equilibrium after undergoing the violent relaxation phase. We show that the early evolutionary phases of the UFD significantly enhance the disruption of wide binaries and leave their dynamical fingerprints on the semi-major axis distribution of the surviving binaries as compared to models initially in equilibrium. An initially thermal eccentricity distribution is preserved except for the widest binaries for which it evolves towards a superthermal distribution; for a binary population with an initially uniform eccentricity distribution, memory of this initial distribution is rapidly lost for most binaries as wider binaries evolve to approach a thermal/superthermal distribution. The evolution of binaries is driven both by tidal effects due to the potential of the UFD dark matter halo and collisional effects associated to binary-binary/single star encounters. Collisional effects are particularly important within the clumpy substructure characterizing the system during its early evolution; in addition to enhancing binary ionization and evolution of the binary orbital parameters, encounters may lead to exchanges of either of the primordial binary components with one of the interacting stars.

The overall objective of this study was to develop cost-effective treatment processes for 1,4-dioxane removal that were safe and easy to scale up. Degradation of 1,4-dioxane was conducted and compared for the first time by heterogeneous photocatalysis and a photo-Fenton-like process under cool white fluorescent light in mild conditions, using two types of commercial nanoparticles-titanium dioxide (TiO2) and nanoscale zero-valent iron (nZVI), respectively. Both types of nanoparticles removed >99.9% of 1,4-dioxane in a short period of time. Hydroxyl radicals (·OH), superoxide radicals (·O2-), and hydrogen peroxide (H2O2) were detected in both degradation processes; photogenerated holes (h+) were critical in the degradation of 1,4-dioxane by the photocatalytic process using TiO2. 1,4-Dioxane can be degraded at pH 7 in TiO2/light system and at pH 3 in nZVI/light system, and faster degradation of 1,4-dioxane at even higher concentration was achieved in the former system. Increase in light intensity accelerated 1,4-dioxane degradation, which followed first order kinetics in both systems. In wastewater effluent, the removal of 1,4-dioxane was slower than that in deionized water, which likely reflected the complex compositions of the wastewater effluent. Under combined UVA and visible light illumination, a two-stage degradation process was proposed for 1,4-dioxane for the first time by TiO2 nanoparticles; this study also demonstrated for the first time 1,4-dioxane degradation by the photo-Fenton-like process using nZVI. The cost-effective solutions using commercial nanoparticles under fluorescent light developed in this study can be potentially applied to treat water contaminated by high concentrations of 1,4-dioxane in large-scale.

We present an inelastic neutron scattering study of liquid and solid hydrogen carried out using the wide Angular Range Chopper Spectrometer at Oak Ridge National Laboratory. From the observed dynamic structure factor, we obtained empirical estimates of the molecular mean-squared displacement and average translational kinetic energy. We find that the former quantity increases with temperature, indicating that a combination of thermal and quantum effects is important near the liquid-solid phase transition, contrary to previous measurements. We also find that the kinetic energy drops dramatically on melting of the crystals, a consequence of the large increase in molar volume together with the Heisenberg indeterminacy principle. Our results are compared with quantum Monte Carlo simulations based on different model potentials. In general, there is good agreement between our findings and theoretical predictions based on the Silvera-Goldman and Buck potentials.