A population model-based linear-quadratic Gaussian compensator for the control of intravenously infused alcohol studies and withdrawal symptom prophylaxis using transdermal sensing.

An output feedback LQG compensator (combined controller and state estimator) for the regulation of intravenous-infused alcohol studies and treatment using a noninvasive transdermal alcohol biosensor is developed. The design is based on a population model involving an abstract semi-linear parabolic hybrid reaction-diffusion system involving coupled partial and ordinary differential equations with random parameters known only up to their distributions. The scheme developed is based on a weak formulation of the model equations in an appropriately constructed Gelfand triple of Bochner spaces wherein the unknown random parameters are treated as additional spatial variables. Implementation relies on a Galerkin-based approximation and convergence theory and an abstract formulation involving linear semigroups of operators. The model is fit and validated using laboratory collected human subject data and the method of moments. The results of numerical simulations of controlled intravenous alcohol infusion are presented and discussed.

A genetically informed longitudinal study of early-life temperament and childhood aggression.

The present study examined the longitudinal associations between three dimensions of temperament - activity, affect-extraversion, and task orientation - and childhood aggression. Using 131 monozygotic and 173 dizygotic (86 same-sex) twin pairs from the Louisville Twin Study, we elucidated the ages, from 6 to 36 months, at which each temperament dimension began to correlate with aggression at age 7. We employed latent growth modeling to show that developmental increases (i.e., slopes) in activity were positively associated with aggression, whereas increases in affect-extraversion and task orientation were negatively associated with aggression. Genetically informed models revealed that correlations between temperament and aggression were primarily explained by common genetic variance, with nonshared environmental variance accounting for a small proportion of each correlation by 36 months. Genetic variance explained the correlations of the slopes of activity and task orientation with aggression. Nonshared environmental variance accounted for almost half of the correlation between the slopes of affect-extraversion and aggression. Exploratory analyses revealed quantitative sex differences in each temperament-aggression association. By establishing which dimensions of temperament correlate with aggression, as well as when and how they do so, our work informs the development of future child and family interventions for children at highest risk of aggression.

Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol.

The existence and consistency of a maximum likelihood estimator for the joint probability distribution of random parameters in discrete-time abstract parabolic systems was established by taking a nonparametric approach in the context of a mixed effects statistical model using a Prohorov metric framework on a set of feasible measures. A theoretical convergence result for a finite dimensional approximation scheme for computing the maximum likelihood estimator was also established and the efficacy of the approach was demonstrated by applying the scheme to the transdermal transport of alcohol modeled by a random parabolic partial differential equation (PDE). Numerical studies included show that the maximum likelihood estimator is statistically consistent, demonstrated by the convergence of the estimated distribution to the "true" distribution in an example involving simulated data. The algorithm developed was then applied to two datasets collected using two different transdermal alcohol biosensors. Using the leave-one-out cross-validation (LOOCV) method, we found an estimate for the distribution of the random parameters based on a training set. The input from a test drinking episode was then used to quantify the uncertainty propagated from the random parameters to the output of the model in the form of a 95 error band surrounding the estimated output signal.

Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: A population model-based LQG approach in Hilbert space.

LQG control in Hilbert space, a novel approach for random abstract parabolic systems, and new transdermal alcohol biosensor technology are combined to yield tracking controllers that can be used to automate inpatient management of alcohol withdrawal syndrome and human subject intravenous alcohol infusion studies, and to blindly deconvolve blood or breath alcohol concentration from biosensor measured transdermal alcohol level. The approach taken is based on a full-body alcohol population model in the form of a random, nonlinear, hybrid system of ordinary and partial differential equations and its abstract formulation in a Gelfand triple of Bochner spaces. The efficacy of the approach is demonstrated through simulation studies based on laboratory collected drinking data.

Remember This: Age Moderation of Genetic and Environmental Contributions to Verbal Episodic Memory from Midlife through Late Adulthood.

It is well documented that memory is heritable and that older adults tend to have poorer memory performance than younger adults. However, whether the magnitudes of genetic and environmental contributions to late-life verbal episodic memory ability differ from those at earlier ages remains unresolved. Twins from 12 studies participating in the Interplay of Genes and Environment in Multiple Studies (IGEMS) consortium constituted the analytic sample. Verbal episodic memory was assessed with immediate word list recall (N = 35,204 individuals; 21,792 twin pairs) and prose recall (N = 3,805 individuals; 2,028 twin pairs), with scores harmonized across studies. Average test performance was lower in successively older age groups for both measures. Twin models found significant age moderation for both measures, with total inter-individual variance increasing significantly with age, although it was not possible definitively to attribute the increase specifically to either genetic or environmental sources. Pooled results across all 12 studies were compared to results where we successively dropped each study (leave-one-out) to assure results were not due to an outlier. We conclude the models indicated an overall increase in variance for verbal episodic memory that was driven by a combination of increases in the genetic and nonshared environmental parameters that were not independently statistically significant. In contrast to reported results for other cognitive domains, differences in environmental exposures are comparatively important for verbal episodic memory, especially word list learning.

Social anxiety and alcohol consumption: The role of social context.

Research has identified social anxiety as a risk factor for the development of alcohol use disorder. However, studies have produced equivocal findings regarding the relationship between social anxiety and drinking behaviors in authentic drinking environments. This study examined how social-contextual features of real-world drinking contexts might influence the relationship between social anxiety and alcohol consumption in everyday settings. At an initial laboratory visit, heavy social drinkers (N = 48) completed the Liebowitz Social Anxiety Scale. Participants were then outfitted with a transdermal alcohol monitor individually-calibrated for each participant via laboratory alcohol-administration. Over the next seven days, participants wore this transdermal alcohol monitor and responded to random survey prompts (6x/day), during which they provided photographs of their surroundings. Participants then reported on their levels of social familiarity with individuals visible in photographs. Multilevel models indicated a significant interaction between social anxiety and social familiarity in predicting drinking, b = -0.004, p =.003 Specifically, among participants higher in social anxiety, drinking increased as social familiarity decreased b = -0.152, p <.001, whereas among those lower in social anxiety, this relationship was non-significant, b = 0.007, p =.867. Considered alongside prior research, findings suggest that the presence of strangers within a given environment may play a role in the drinking behavior of socially anxious individuals.

Uncertainty Quantification in Estimating Blood Alcohol Concentration From Transdermal Alcohol Level With Physics-Informed Neural Networks.

We develop an approach to estimate a blood alcohol signal from a transdermal alcohol signal using physics-informed neural networks (PINNs). Specifically, we use a generative adversarial network (GAN) with a residual-augmented loss function to estimate the distribution of unknown parameters in a diffusion equation model for transdermal transport of alcohol in the human body. We design another PINN for the deconvolution of the blood alcohol signal from the transdermal alcohol signal. Based on the distribution of the unknown parameters, this network is able to estimate the blood alcohol signal and quantify the uncertainty in the form of conservative error bands. Finally, we show how a posterior latent variable can be used to sharpen these conservative error bands. We apply the techniques to an extensive dataset of drinking episodes and demonstrate the advantages and shortcomings of this approach.

Substance Use Descriptive Norms and Behaviors among US College Students: Findings from the Healthy Minds Study.

BACKGROUND: Social norms have been associated with alcohol use in college populations; however, more research is needed to confirm the associations between social norms and a range of substance use behaviors during the COVID-19 pandemic. METHODS: We analyzed data from the Healthy Minds Study (September 2020-December 2020), a non-probability sample administered online to college students. We used multivariable logistic regression to test for associations between respondents' perceptions of substance use behaviors in their respective colleges and their own substance use behaviors, adjusting for age, gender, race/ethnicity, and international student status. RESULTS: We found that those who overestimated the prevalence of alcohol use, cigarette use, cannabis use, and vaping were significantly more likely to use these substances when compared with those who did not overestimate. These associations persisted even when using different prevalence estimates of substance use, though some associations lost statistical significance when applying the survey weights to account for non-response. CONCLUSION: College students overestimated the prevalence of substance use in their respective colleges, even during the early stages of the pandemic when social interactions were limited, and these beliefs were associated with substance use. Future studies may test the utility of campaigns to alter perceptions of social norms and interventions that use personalized normative feedback to reduce substance use during pandemics.

Estimating Likelihood of Dementia in the Absence of Diagnostic Data: A Latent Dementia Index in 10 Genetically Informed Studies.

BACKGROUND: Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available. OBJECTIVE: We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results. METHODS: Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available. RESULTS: Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis. CONCLUSION: This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.

Blood and Breath Alcohol Concentration from Transdermal Alcohol Biosensor Data: Estimation and Uncertainty Quantification via Forward and Inverse Filtering for a Covariate-Dependent, Physics-Informed, Hidden Markov Model.

Transdermal alcohol biosensors that do not require active participation of the subject and yield near continuous measurements have the potential to significantly enhance the data collection abilities of alcohol researchers and clinicians who currently rely exclusively on breathalyzers and drinking diaries. Making these devices accessible and practical requires that transdermal alcohol concentration (TAC) be accurately and consistently transformable into the well-accepted measures of intoxication, blood/breath alcohol concentration (BAC/BrAC). A novel approach to estimating BrAC from TAC based on covariate-dependent physics-informed hidden Markov models with two emissions is developed. The hidden Markov chain serves as a forward full-body alcohol model with BrAC and TAC, the two emissions, assumed to be described by a bivariate normal which depends on the hidden Markovian states and person-level and session-level covariates via built-in regression models. An innovative extension of hidden Markov modeling is developed wherein the hidden Markov model framework is regularized by a first-principles PDE model to yield a hybrid that combines prior knowledge of the physics of transdermal ethanol transport with data-based learning. Training, or inverse filtering, is effected via the Baum-Welch algorithm and 256 sets of BrAC and TAC signals and covariate measurements collected in the laboratory. Forward filtering of TAC to obtain estimated BrAC is achieved via a new physics-informed regularized Viterbi algorithm which determines the most likely path through the hidden Markov chain using TAC alone. The Markovian states are decoded and used to yield estimates of BrAC and to quantify the uncertainty in the estimates. Numerical studies are presented and discussed. Overall good agreement between BrAC data and estimates was observed with a median relative peak error of 22% and a median relative area under the curve error of 25% on the test set. We also demonstrate that the physics-informed Viterbi algorithm eliminates non-physical artifacts in the BrAC estimates.

An Abstract Parabolic System-Based Physics-Informed Long Short-Term Memory Network for Estimating Breath Alcohol Concentration from Transdermal Alcohol Biosensor Data.

The problem of estimating breath alcohol concentration based on transdermal alcohol biosensor data is considered. Transdermal alcohol concentration provides a promising alternative to classical methods such as breathalyzers or drinking diaries. A physics-informed long Short-term memory (LSTM) network with covariates for the solution of the estimation problem is developed. The data-driven nature of an LSTM is augmented with a first principles physics-based population model for the diffusion of ethanol through the epidermal layer of the skin. The population model in an abstract parabolic framework appears as part of a regularization term in the loss function of the LSTM. While learning, the model is encouraged to both fit the data and to produce physically meaningful outputs. To deal with the high variation observed in the data, a mechanism for the uncertainty quantification of the estimates based on a recently discovered relation between Monte-Carlo dropout and Bayesian learning is used. The physics-based population model and the LSTM are trained and tested using controlled laboratory collected breath and transdermal alcohol data collected in four sessions from 40 orally dosed participants (50% female, ages 21 - 33 years, 35% BMI above 25.0) resulting in 256 usable drinking episodes partitioned into training and testing sets. Body measurement (e.g. BMI, hip to waist ratio, etc.), personal (e.g. sex, age, race, etc.), drinking behavior (e.g. frequent, rarely, etc.), and environmental (e.g. temperature, humidity, etc.) covariates were also collected from participants. The importance of various covariates in the estimation is investigated using Shapley values. It is shown that the physics-informed LSTM network can be successfully applied to drinking episodes from both the training and test set, and that the physics-based information leads to better generalization ability on new drinking episodes with the uncertainty quantification yielding credible bands that effectively capture the true signal. Compared to two machine learning models from previous studies, the proposed model reduces relative L2 error in estimated breath alcohol concentration by 58% and 72%, and relative peak error by 33% and 76%.

Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach.

The posterior distribution (PD) of random parameters in a distributed parameter-based population model for biosensor measured transdermal alcohol is estimated. The output of the model is transdermal alcohol concentration (TAC), which, via linear semigroup theory can be expressed as the convolution of blood or breath alcohol concentration (BAC or BrAC) with a filter that depends on the individual participant or subject, the biosensor hardware itself, and environmental conditions, all of which can be considered to be random under the presented framework. The distribution of the input to the model, the BAC or BrAC, is also sequentially estimated. A Bayesian approach is used to estimate the PD of the parameters conditioned on the population sample's measured BrAC and TAC. We then use the PD for the parameters together with a weak form of the forward random diffusion model to deconvolve an individual subject's BrAC conditioned on their measured TAC. Priors for the model are obtained from simultaneous temporal population observations of BrAC and TAC via deterministic or statistical methods. The requisite computations require finite dimensional approximation of the underlying state equation, which is achieved through standard finite element (i.e., Galerkin) techniques. The posteriors yield credible regions, which remove the need to calibrate the model to every individual, every sensor, and various environmental conditions. Consistency of the Bayesian estimators and convergence in distribution of the PDs computed based on the finite element model to those based on the underlying infinite dimensional model are established. Results of human subject data-based numerical studies demonstrating the efficacy of the approach are presented and discussed.

Effects of stomach content on the breath alcohol concentration-transdermal alcohol concentration relationship.

INTRODUCTION: Wearable devices that obtain transdermal alcohol concentration (TAC) could become valuable research tools for monitoring alcohol consumption levels in naturalistic environments if the TAC they produce could be converted into quantitatively-meaningful estimates of breath alcohol concentration (eBrAC). Our team has developed mathematical models to produce eBrAC from TAC, but it is not yet clear how a variety of factors affect the accuracy of the models. Stomach content is one factor that is known to affect breath alcohol concentration (BrAC), but its effect on the BrAC-TAC relationship has not yet been studied. METHODS: We examine the BrAC-TAC relationship by having two investigators participate in four laboratory drinking sessions with varied stomach content conditions: (i) no meal, (ii) half and (iii) full meal before drinking, and (iv) full meal after drinking. BrAC and TAC were obtained every 10 min over the BrAC curve. RESULTS: Eating before drinking lowered BrAC and TAC levels, with greater variability in TAC across person-device pairings, but the BrAC-TAC relationship was not consistently altered by stomach content. The mathematical model calibration parameters, fit indices, and eBrAC curves and summary score outputs did not consistently vary based on stomach content, indicating that our models were able to produce eBrAC from TAC with similar accuracy despite variations in the shape and magnitude of the BrAC curves under different conditions. DISCUSSION AND CONCLUSIONS: This study represents the first examination of how stomach content affects our ability to model estimates of BrAC from TAC and indicates it is not a major factor.

Alcohol Consumption, Cardiovascular-Related Conditions, and ALDH2*2 Ethnic Group Prevalence in Asian Americans.

BACKGROUND: Little is known about the relationships between alcohol consumption and cardiovascular disease (CVD) and related chronic conditions in Asian Americans and how such risk relationships vary among their subgroups. We examine these relationships in Asian Americans and their moderation by ethnic prevalence of a variant the aldehyde dehydrogenase gene: ALDH2*2. METHODS: Multiple logistic regression modeling was performed using a nationally representative sample of Asian-American adults aged 30 to 70 (n = 1,720) from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 2 (2004 to 2005) and 3 (2012 to 2013). Outcomes considered were diabetes, hypertension, high cholesterol, CVD, any of the 3 conditions (i.e., diabetes, high cholesterol, and CVD) documented to have a J-shaped relationship with drinking (CVDRC3), and any of the CVD-related conditions (ANYCVD). Demographic and socioeconomic characteristics, health insurance coverage, and other lifestyle risk factors (smoking and obesity/overweight) were adjusted. Analyses were stratified by gender. RESULTS: Alcohol consumption level was positively associated only with hypertension in Asian males, with consuming 7 to 14 drinks per week associated with more than double the risk of lifetime abstinence. For females, alcohol consumption had a dose-response relationship with high cholesterol and CVDRC3. Membership in the higher ALDH2*2 ethnic group overall was associated with lower risk of CVD-related conditions. However, compared to abstainers in lower ALDH2*2 group, females in the higher ALDH2*2 group who consumed more than 7 drinks per week had a higher risk of diabetes, hypertension, CVDRC3, and ANYCVD. CONCLUSIONS: Asian Americans may have increased risk of CVD-related conditions at relatively low alcohol consumption levels. Among Asian-American females, in particular, any amount of drinking may increase risk for high cholesterol or any of the CVD-related conditions previously documented to have a curvilinear relationship with drinking. These risks may be particularly elevated for those in ethnic groups with a high prevalence of ALDH2*2.

Exploring the Perceived Risks and Benefits of Heroin Use among Young People (18-24 Years) in Mauritius: Economic Insights from an Exploratory Qualitative Study.

The decreasing age of young people injecting illicit drugs is an under-reported challenge for the prevention of HIV transmission worldwide. Young people aged 15-24 years represent 1 in 5 persons living with HIV in Mauritius where the epidemic is driven by injecting drug use and risky sexual behaviours. We recruited 22 heroin users aged 18-24 and 5 service providers working in harm reduction (HR) for the present study. Qualitative data were collected through unstructured interviews. We adopted an economic framework and an inductive approach to the analysis, which implied revising codes and themes. The risks heroin users described as consumers of illicit drugs and as clients of HR services could not be analyzed in isolation. Polydrug use emerged as a recurrent coping mechanism resulting from the changing dynamics within the heroin market. The risks faced by women went beyond addiction and infection with HIV. How participants viewed the risks and benefits linked to using heroin was greatly influenced by gaps in knowledge that left room for uncertainty and reinforcing mechanisms such as peer influence. The study shows that qualitative research can produce in-depth socio-behavioural insights required to produce more effective services for young people.

Estimating the Distribution of Random Parameters in a Diffusion Equation Forward Model for a Transdermal Alcohol Biosensor.

We estimate the distribution of random parameters in a distributed parameter model with unbounded input and output for the transdermal transport of ethanol in humans. The model takes the form of a diffusion equation with the input being the blood alcohol concentration and the output being the transdermal alcohol concentration. Our approach is based on the idea of reformulating the underlying dynamical system in such a way that the random parameters are now treated as additional space variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating the diffusivity in a multi-dimensional diffusion equation and thus well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods may all be employed. We use our technique to estimate a bivariate normal distribution based on data for multiple drinking episodes from a single subject.

Estimation of the Distribution of Random Parameters in Discrete Time Abstract Parabolic Systems with Unbounded Input and Output: Approximation and Convergence.

A finite dimensional abstract approximation and convergence theory is developed for estimation of the distribution of random parameters in infinite dimensional discrete time linear systems with dynamics described by regularly dissipative operators and involving, in general, unbounded input and output operators. By taking expectations, the system is re-cast as an equivalent abstract parabolic system in a Gelfand triple of Bochner spaces wherein the random parameters become new space-like variables. Estimating their distribution is now analogous to estimating a spatially varying coefficient in a standard deterministic parabolic system. The estimation problems are approximated by a sequence of finite dimensional problems. Convergence is established using a state space-varying version of the Trotter-Kato semigroup approximation theorem. Numerical results for a number of examples involving the estimation of exponential families of densities for random parameters in a diffusion equation with boundary input and output are presented and discussed.

Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for Inverse Problems.

Three methods for the estimation of blood or breath alcohol concentration (BAC/BrAC) from biosensor measured transdermal alcohol concentration (TAC) are evaluated and compared. Specifically, we consider a system identification/quasi-blind deconvolution scheme based on a distributed parameter model with unbounded input and output for ethanol transport in the skin and compare it to two more conventional system identification and filtering/deconvolution techniques for ill-posed inverse problems, one based on frequency domain methods, and the other on a time series approach using an ARMA input/output model. Our basis for comparison are five statistical measures of interest to alcohol researchers and clinicians: peak BAC/BrAC, time of peak BAC/BrAC, the ascending and descending slopes of the BAC/BrAC curve, and the area underneath the BAC/BrAC curve.

Applying a novel population-based model approach to estimating breath alcohol concentration (BrAC) from transdermal alcohol concentration (TAC) biosensor data.

Alcohol biosensor devices have been developed to unobtrusively measure transdermal alcohol concentration (TAC), the amount of ethanol diffusing through the skin, in nearly continuous fashion in naturalistic settings. Because TAC data are affected by physiological and environmental factors that vary across individuals and drinking episodes, there is not an elementary formula to convert TAC into easily interpretable metrics such as blood and breath alcohol concentrations (BAC/BrAC). In our prior work, we addressed this conversion problem in a deterministic way by developing physics/physiological-based models to convert TAC to estimated BrAC (eBrAC), in which the model parameter values were individually determined for each person wearing a specific transdermal sensor using simultaneously collected TAC (via a biosensor) and BrAC (via a breath analyzer) during a calibration episode. We found these individualized parameter values produced relatively good eBrAC curves for subsequent drinking episodes, but our results also indicated the models were not fully capturing the dynamics of the system and variations across drinking episodes. Here, we report on a novel mathematical framework to improve our ability to model eBrAC from TAC data that uses aggregate population data instead of individualized calibration data to determine model parameter values via a random diffusion equation. We first provide the theoretical mathematical basis for our approach, and then test the efficacy of this method using datasets of contemporaneous BrAC/TAC measurements obtained by a) a single subject during multiple drinking episodes and b) multiple subjects during single drinking episodes. For each dataset, we used a set of drinking episodes to construct the population model, and then ran the model with another set of randomly selected test episodes. We compared raw TAC data to model-simulated TAC curve, breath analyzer BrAC data to model eBrAC curve with 75% credible bands, episode summary scores of peak BrAC, times of peak BrAC, and area under the drinking curve also with 75% credible intervals, and report the percent of the raw BrAC captured within the eBrAC curve credible bands. We also display results when stratifying the data based on the relationship between the raw BrAC and TAC data. Results indicate the population-based model is promising, with better fit within a single participant when stratifying episodes. This study provides initial proof-of-concept for constructing, fitting, and using a population-based model to obtain estimates and error bands for BrAC from TAC. The advancements in this study, including new applications of math, the development of a population-based model with error bars, and the production of corresponding MATLAB codes, represent a major step forward in our ability to produce quantitatively- and temporally-accurate estimates of BrAC from TAC biosensor data.