A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis

Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm as well as approximate MML estimation procedures such as variational inference (VI) are computationally time-consuming when the sample size and the number of latent factors are very large. In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors. The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA. The IWAE approximates the MML estimator using an importance sampling technique wherein increasing the number of importance-weighted (IW) samples drawn during fitting improves the approximation, typically at the cost of decreased computational efficiency. We provide a real data application that recovers results aligning with psychological theory across random starts. Via simulation studies, we show that the IWAE yields more accurate estimates as either the sample size or the number of IW samples increases (although factor correlation and intercepts estimates exhibit some bias) and obtains similar results to MH-RM in less time. Our simulations also suggest that the proposed approach performs similarly to and is potentially faster than constrained joint maximum likelihood estimation, a fast procedure that is consistent when the sample size and the number of items simultaneously tend to infinity.Comment: 30 pages; 12 figures; accepted for publication in Psychometrik

A Note on the Use of Mixture Models for Individual Prediction

Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model

Semiclassical Concepts in Magnetoelectronics

Semiclassical theories of electron and spin transport in metallic magnetic structures are reviewed with emphasis on the role of disorder and electronic band structures in the current perpendicular to the interface plane (CPP) transport configuration.Comment: Proceedings of the NEC Symposium on "Spin-related Quantum Transport in Mesoscopic Systems", to be published in the Journal of Materials Science and Engineering

Addiction History Associates with the Propensity to Form Habits

Learned habitual responses to environmental stimuli allow efficient interaction with the environment, freeing cognitive resources for more demanding tasks. However, when the outcome of such actions is no longer a desired goal, established stimulus-response (S-R) associations, or habits, must be overcome. Among people with substance use disorders (SUDs), difficulty in overcoming habitual responses to stimuli associated with their addiction in favor of new, goal-directed behaviors, contributes to relapse. Animal models of habit learning demonstrate that chronic self-administration of drugs of abuse promotes habitual responding beyond the domain of compulsive drug seeking. However, whether a similar propensity toward domain-general habitual responding occurs in humans with SUDs has remained unclear. To address this question, we used a visuomotor S-R learning and re-learning task, the Hidden Association Between Images Task (HABIT), which employs abstract visual stimuli and manual responses. This task allows us to measure new S-R association learning, well-learned S-R association execution, and includes a response contingency change manipulation to quantify the degree to which responding is habit-based, rather than goal-directed. We find that people with SUDs learn new S-R associations as well as healthy control subjects do. Moreover, people with an SUD history slightly outperform controls in S-R execution. In contrast, people with SUDs are specifically impaired in overcoming well-learned S-R associations; those with SUDs make a significantly greater proportion of perseverative errors during well-learned S-R replacement, indicating the more habitual nature of their responses. Thus, with equivalent training and practice, people with SUDs appear to show enhanced domain-general habit formation

Activin and TGFβ use diverging mitogenic signaling in advanced colon cancer.

BackgroundUnderstanding cell signaling pathways that contribute to metastatic colon cancer is critical to risk stratification in the era of personalized therapeutics. Here, we dissect the unique involvement of mitogenic pathways in a TGFβ or activin-induced metastatic phenotype of colon cancer.MethodMitogenic signaling/growth factor receptor status and p21 localization were correlated in primary colon cancers and intestinal tumors from either AOM/DSS treated ACVR2A (activin receptor 2) -/- or wild type mice. Colon cancer cell lines (+/- SMAD4) were interrogated for ligand-induced PI3K and MEK/ERK pathway activation and downstream protein/phospho-isoform expression/association after knockdown and pharmacologic inhibition of pathway members. EMT was assessed using epithelial/mesenchymal markers and migration assays.ResultsIn primary colon cancers, loss of nuclear p21 correlated with upstream activation of activin/PI3K while nuclear p21 expression was associated with TGFβ/MEK/ERK pathway activation. Activin, but not TGFβ, led to PI3K activation via interaction of ACVR1B and p85 independent of SMAD4, resulting in p21 downregulation. In contrast, TGFβ increased p21 via MEK/ERK pathway through a SMAD4-dependent mechanism. While activin induced EMT via PI3K, TGFβ induced EMT via MEK/ERK activation. In vivo, loss of ACVR2A resulted in loss of pAkt, consistent with activin-dependent PI3K signaling.ConclusionAlthough activin and TGFβ share growth suppressive SMAD signaling in colon cancer, they diverge in their SMAD4-independent pro-migratory signaling utilizing distinct mitogenic signaling pathways that affect EMT. p21 localization in colon cancer may determine a dominant activin versus TGFβ ligand signaling phenotype warranting further validation as a therapeutic biomarker prior to targeting TGFβ family receptors

A neuronal relay mediates a nutrient responsive gut/fat body axis regulating energy homeostasis in adult Drosophila

The control of systemic metabolic homeostasis involves complex inter-tissue programs that coordinate energy production, storage, and consumption, to maintain organismal fitness upon environmental challenges. The mechanisms driving such programs are largely unknown. Here, we show that enteroendocrine cells in the adult Drosophila intestine respond to nutrients by secreting the hormone Bursicon α, which signals via its neuronal receptor DLgr2. Bursicon α/DLgr2 regulate energy metabolism through a neuronal relay leading to the restriction of glucagon-like, adipokinetic hormone (AKH) production by the corpora cardiaca and subsequent modulation of AKH receptor signaling within the adipose tissue. Impaired Bursicon α/DLgr2 signaling leads to exacerbated glucose oxidation and depletion of energy stores with consequent reduced organismal resistance to nutrient restrictive conditions. Altogether, our work reveals an intestinal/neuronal/adipose tissue inter-organ communication network that is essential to restrict the use of energy and that may provide insights into the physiopathology of endocrine-regulated metabolic homeostasis

A mobile app to identify lifestyle indicators related to undergraduate mental health (smart healthy campus): Observational app-based ecological momentary assessment

Background: Undergraduate studies are challenging, and mental health issues can frequently occur in undergraduate students,straining campus resources that are already in demand for somatic problems. Cost-effective measures with ubiquitous devices,such as smartphones, offer the potential to deliver targeted interventions to monitor and affect lifestyle, which may result inimprovements to student mental health. However, the avenues by which this can be done are not particularly well understood,especially in the Canadian context.Objective: The aim of this study is to deploy an initial version of the Smart Healthy Campus app at Western University, Canada,and to analyze corresponding data for associations between psychosocial factors (measured by a questionnaire) and behaviorsassociated with lifestyle (measured by smartphone sensors).Methods: This preliminary study was conducted as an observational app-based ecological momentary assessment. Undergraduatestudents were recruited over email, and sampling using a custom 7-item questionnaire occurred on a weekly basis.Results: First, the 7-item Smart Healthy Campus questionnaire, derived from fully validated questionnaires-such as the BriefResilience Scale; General Anxiety Disorder-7; and Depression, Anxiety, and Stress Scale-21-was shown to significantly correlatewith the mental health domains of these validated questionnaires, illustrating that it is a viable tool for a momentary assessmentof an overview of undergraduate mental health. Second, data collected through the app were analyzed. There were 312 weeklyresponses and 813 sensor samples from 139 participants from March 2019 to March 2020; data collection concluded whenCOVID-19 was declared a pandemic. Demographic information was not collected in this preliminary study because of technicallimitations. Approximately 69.8% (97/139) of participants only completed one survey, possibly because of the absence of anyincentive. Given the limited amount of data, analysis was not conducted with respect to time, so all data were analyzed as a singlecollection. On the basis of mean rank, students showing more positive mental health through higher questionnaire scores tendedto spend more time completing questionnaires, showed more signs of physical activity based on pedometers, and had their devicesrunning less and plugged in charging less when sampled. In addition, based on mean rank, students on campus tended to reportmore positive mental health through higher questionnaire scores compared with those who were sampled off campus. Some datafrom students found in or near residences were also briefly examined.Conclusions: Given these limited data, participants tended to report a more positive overview of mental health when on campusand when showing signs of higher levels of physical activity. These early findings suggest that device sensors related to physical activity and location are useful for monitoring undergraduate students and designing interventions. However, much more sensordata are needed going forward, especially given the sweeping changes in undergraduate studies due to COVID-19

Beyond Screen Time: Assessing Recreational Sedentary Behavior among Adolescent Girls

Most studies of sedentary behavior have focused on television use or screen time. This study aims to examine adolescent girls' participation in a variety of recreational sedentary behaviors (e.g., talking on the phone and hanging around), and their association with physical activity (PA), dietary behaviors, and body mass index. Data were from a sample of 283 adolescent girls. Recreational sedentary behavior, PA, and dietary behaviors were self-reported, and girls' height and weight were measured. Over 95% of girls engaged in at least one recreational sedentary behavior during the recall period. Watching television and hanging around were the most common behaviors. Watching television, using the Internet, and hanging around were associated with less PA; watching television, hanging around, and talking on the phone were associated with less healthful dietary behaviors. No associations were found with body mass index. Interventions may benefit from capitalizing on and intervening upon girls' common recreational sedentary behaviors

The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change

Longitudinal models are becoming increasingly prevalent in the behavioral sciences, with key advantages including increased power, more comprehensive measurement, and establishment of temporal precedence. One particularly salient strength offered by longitudinal data is the ability to disaggregate between-person and within-person effects in the regression of an outcome on a time-varying covariate. However, the ability to disaggregate these effects has not been fully capitalized upon in many social science research applications. Two likely reasons for this omission are the general lack of discussion of disaggregating effects in the substantive literature and the need to overcome several remaining analytic challenges that limit existing quantitative methods used to isolate these effects in practice. This review explores both substantive and quantitative issues related to the disaggregation of effects over time, with a particular emphasis placed on the multilevel model. Existing analytic methods are reviewed, a general approach to the problem is proposed, and both the existing and proposed methods are demonstrated using several artificial data sets. Potential limitations and directions for future research are discussed, and recommendations for the disaggregation of effects in practice are offered