ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS.

We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.

Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features.

This article presents a novel approach to clustering and feature selection for categorical time series via interpretable frequency-domain features. A distance measure is introduced based on the spectral envelope and optimal scalings, which parsimoniously characterize prominent cyclical patterns in categorical time series. Using this distance, partitional clustering algorithms are introduced for accurately clustering categorical time series. These adaptive procedures offer simultaneous feature selection for identifying important features that distinguish clusters and fuzzy membership when time series exhibit similarities to multiple clusters. Clustering consistency of the proposed methods is investigated, and simulation studies are used to demonstrate clustering accuracy with various underlying group structures. The proposed methods are used to cluster sleep stage time series for sleep disorder patients in order to identify particular oscillatory patterns associated with sleep disruption.

Early detection of ovarian cancer by wavelet analysis of protein mass spectra.

Accurate and efficient detection of ovarian cancer at early stages is critical to ensure proper treatments for patients. Among the first-line modalities investigated in studies of early diagnosis are features distilled from protein mass spectra. This method, however, considers only a specific subset of spectral responses and ignores the interplay among protein expression levels, which can also contain diagnostic information. We propose a new modality that automatically searches protein mass spectra for discriminatory features by considering the self-similar nature of the spectra. Self-similarity is assessed by taking a wavelet decomposition of protein mass spectra and estimating the rate of level-wise decay in the energies of the resulting wavelet coefficients. Level-wise energies are estimated in a robust manner using distance variance, and rates are estimated locally via a rolling window approach. This results in a collection of rates that can be used to characterize the interplay among proteins, which can be indicative of cancer presence. Discriminatory descriptors are then selected from these evolutionary rates and used as classifying features. The proposed wavelet-based features are used in conjunction with features proposed in the existing literature for early stage diagnosis of ovarian cancer using two datasets published by the American National Cancer Institute. Including the wavelet-based features from the new modality results in improvements in diagnostic performance for early-stage ovarian cancer detection. This demonstrates the ability of the proposed modality to characterize new ovarian cancer diagnostic information.

Association between proteomic biomarkers and myocardial fibrosis measured by MRI: the multi-ethnic study of atherosclerosis.

BACKGROUND: Cardiac magnetic resonance imaging (CMR) determines the extent of interstitial fibrosis, measured by increased extracellular volume (ECV), and replacement fibrosis with late gadolinium myocardial enhancement (LGE). Despite advances in detection, the pathophysiology of subclinical myocardial fibrosis is incompletely understood. Targeted proteomic discovery technologies enable quantification of low abundance circulating proteins to elucidate cardiac fibrosis mechanisms. METHODS: Using a cross-sectional design, we selected 92 LGE+ cases and 92 LGE- demographically matched controls from the Multi-Ethnic Study of Atherosclerosis. Similarly, we selected 156 cases from the highest ECV quartile and matched with 156 cases from the lowest quartile. The plasma serum proteome was analyzed using proximity extension assays to determine differential regulation of 92 proteins previously implicated with cardiovascular disease. Results were analyzed using volcano plots of statistical significance vs. magnitude of change and Bayesian additive regression tree (BART) models to determine importance. FINDINGS: After adjusting for false discovery, higher ECV was significantly associated with 17 proteins. Using BART, Plasminogen activator inhibitor 1, Insulin-like growth factor-binding protein 1, and N-terminal pro-B-type natriuretic peptide were associated with higher ECV after accounting for other proteins and traditional cardiovascular risk factors. In contrast, no circulating proteins were associated with replacement fibrosis. INTERPRETATIONS: Our results suggest unique circulating proteomic signatures associated with interstitial fibrosis emphasizing its systemic influences. With future validation, protein panels may identify patients who may develop interstitial fibrosis with progression to heart failure. FUNDING: This research was supported by contracts and grants from NHLBI, NCATS and the Inova Heart and Vascular Institute.

Preliminary evidence that misalignment between sleep and circadian timing alters risk-taking preferences.

Decision-making has been shown to suffer when circadian preference is misaligned with time of assessment; however, little is known about how misalignment between sleep timing and the central circadian clock impacts decision-making. This study captured naturally occurring variation in circadian alignment (i.e., alignment of sleep-wake timing with the central circadian clock) to examine if greater misalignment predicts worse decision-making. Over the course of 2 weeks, 32 late adolescent drinkers (aged 18-22 years; 61% female; 69% White) continuously wore actigraphs and completed two overnight in-laboratory visits (Thursday and Sunday) in which both dim-light melatonin onset (DLMO) and behavioural decision-making (risk taking, framing, and strategic reasoning tasks) were assessed. Sleep-wake timing was assessed by actigraphic midsleep from the 2 nights prior to each in-laboratory visit. Alignment was operationalised as the phase angle (interval) between average DLMO and average midsleep. Multilevel modelling was used to predict performance on decision-making tasks from circadian alignment during each in-laboratory visit; non-linear associations were also examined. Shorter DLMO-midsleep phase angle predicted greater risk-taking under conditions of potential loss (B = -0.11, p = 0.06), but less risk-taking under conditions of potential reward (B = 0.14, p = 0.03) in a curvilinear fashion. Misalignment did not predict outcomes in the framing and strategic reasoning tasks. Findings suggest that shorter alignment in timing of sleep with the central circadian clock (e.g., phase-delayed misalignment) may impact risky decision-making, further extending accumulating evidence that sleep/circadian factors are tied to risk-taking. Future studies will need to replicate findings and experimentally probe whether manipulating alignment influences decision-making.

Adaptive Bayesian sum of trees model for covariate-dependent spectral analysis.

This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible-jump MCMC techniques. For high-dimensional covariates, a sparsity-inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age-related changes in power spectra of stride interval time series in the presence of other covariates.

Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD).

Attention deficit hyperactivity disorder (ADHD) is a common cognitive disorder affecting children. ADHD can interfere with educational, social, and emotional development, so early detection is essential for obtaining proper care. Standard ADHD diagnostic protocols rely heavily on subjective assessments of perceived behavior. An objective diagnostic measure would be a welcome development and potentially aid in accurately and efficiently diagnosing ADHD. Analysis of pupillary dynamics has been proposed as a promising alternative method of detecting affected individuals effectively. This study proposes a method based on the self-similarity of pupillary dynamics and assesses its strength as a potential diagnostic biomarker. Localized discriminatory features are developed in the wavelet domain and selected via a rolling window method to build classifiers. The application on a task-based pupil diameter time series dataset of children aged 10-12 years shows that the proposed method achieves greater than 78% accuracy in detecting ADHD. Comparing with a recent approach that constructs features in the original data domain, the proposed wavelet-based classifier achieves more accurate ADHD classification with fewer features. The findings suggest that the proposed diagnostic procedure involving interpretable wavelet-based self-similarity features of pupil diameter data can potentially aid in improving the efficacy of ADHD diagnosis.

Proteomic differences among patients with heart failure taking furosemide or torsemide.

BACKGROUND: Loop diuretics are commonly used for patients with heart failure (HF) but it remains unknown if one loop diuretic is clinically superior. HYPOTHESIS: Biomarkers and proteomics provide insight to how different loop diuretics may differentially affect outcomes. METHODS: Blood and urine were collected from outpatients with HF who were taking torsemide or furosemide for >30 days. Differences were assessed in cardiac, renal, and inflammatory biomarkers and soluble protein panels using the Olink Cardiovascular III and inflammation panels. RESULTS: Of 78 subjects, 55 (71%) were treated with furosemide and 23 (29%) with torsemide, and 25 provided a urine sample (15 treated with furosemide, 10 with torsemide). Patients taking torsemide were older (68 vs 64 years) with a lower mean eGFR (46 vs 54 ml/min/1.73 m2 ), a higher proportion were women (39% vs 24%) and Black (43% vs 27%). In plasma, levels of hs-cTnT, NT-proBNP, and hsCRP were not significantly different between groups. In urine, there were significant differences in urinary albumin, ß-2M, and NGAL, with higher levels in the torsemide-treated patients. Of 184 proteins testing in Olink panels, in plasma, 156 (85%) were higher in patients taking torsemide but none were significantly different after correcting for false discovery. CONCLUSIONS: We show differences in urinary biomarkers but few differences in plasma biomarkers among HF patients on different loop diuretics. Olink technology can detect differences in plasma protein levels from multiple biologic domains. These findings raise the importance of defining differences in mechanisms of action of each diuretic in an appropriately powered study.

Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings.

This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These quantities characterize oscillatory patterns in a categorical time series as the largest possible power at each frequency, or spectral envelope, obtained by assigning numerical values, or scalings, to categories that optimally emphasize oscillations at each frequency. Our procedure combines these two quantities to produce an interpretable and parsimonious feature-based classifier that can be used to accurately determine group membership for categorical time series. Classification consistency of the proposed method is investigated, and simulation studies are used to demonstrate accuracy in classifying categorical time series with various underlying group structures. Finally, we use the proposed method to explore key differences in oscillatory patterns of sleep stage time series for patients with different sleep disorders and accurately classify patients accordingly. The code for implementing the proposed method is available at https://github.com/zedali16/envsca.

Manufacturing a chimpanzee adenovirus-vectored SARS-CoV-2 vaccine to meet global needs.

Manufacturing has been the key factor limiting rollout of vaccination during the COVID-19 pandemic, requiring rapid development and large-scale implementation of novel manufacturing technologies. ChAdOx1 nCoV-19 (AZD1222, Vaxzevria) is an efficacious vaccine against SARS-CoV-2, based upon an adenovirus vector. We describe the development of a process for the production of this vaccine and others based upon the same platform, including novel features to facilitate very large-scale production. We discuss the process economics and the "distributed manufacturing" approach we have taken to provide the vaccine at globally-relevant scale and with international security of supply. Together, these approaches have enabled the largest viral vector manufacturing campaign to date, providing a substantial proportion of global COVID-19 vaccine supply at low cost.

Whole-Body Reactive Agility Metrics to Identify Football Players With a Core and Lower Extremity Injury Risk.

Clinical prediction models are useful in addressing several orthopedic conditions with various cohorts. American football provides a good population for attempting to predict injuries due to their relatively high injury rate. Physical performance can be assessed a variety of ways using an assortment of different tests to assess a diverse set of metrics, which may include reaction time, speed, acceleration, and deceleration. Asymmetry, the difference between right and left performance has been identified as a possible risk factor for injury. The purpose of this study was to determine the whole-body reactive agility metrics that would identify Division I football players who were at elevated risk for core, and lower extremity injuries (CLEI). This cohort study utilized 177 Division I football players with a total of 57 CLEI suffered who were baseline tested prior to the season. Single-task and dual-task whole-body reactive agility movements in lateral and diagonal direction reacting to virtual reality targets were analyzed separately. Receiver operator characteristic (ROC) analyses narrowed the 34 original predictor variables to five variables. Logistic regression analysis determined the three strongest predictors of CLEI for this cohort to be: lateral agility acceleration asymmetry, lateral flanker deceleration asymmetry, and diagonal agility reaction time average. Univariable analysis found odds ratios to range from 1.98 to 2.75 for these predictors of CLEI. ROC analysis had an area under the curve of 0.702 for any combination of two or more risk factors produced an odds ratio of 5.5 for risk of CLEI. These results suggest an asymmetry of 8-15% on two of the identified metrics or a slowed reaction time of ≥0.787 s places someone at increased risk of injury. Sixty-three percent (36/57) of the players who sustained an injury had ≥2 positive predictors In spite of the recognized limitation, these finding support the belief that whole-body reactive agility performance can identify Division I football players who are at elevated risk for CLEI.

A cross-sectional investigation of SARS-CoV-2 seroprevalence and associated risk factors in children and adolescents in the United States.

BACKGROUND: Pediatric SARS-CoV-2 data remain limited and seropositivity rates in children were reported as <1% early in the pandemic. Seroepidemiologic evaluation of SARS-CoV-2 in children in a major metropolitan region of the US was performed. METHODS: Children and adolescents ≤19 years were enrolled in a cross-sectional, observational study of SARS-CoV-2 seroprevalence from July-October 2020 in Northern Virginia, US. Demographic, health, and COVID-19 exposure information was collected, and blood analyzed for SARS-CoV-2 spike protein total antibody. Risk factors associated with SARS-CoV-2 seropositivity were analyzed. Orthogonal antibody testing was performed, and samples were evaluated for responses to different antigens. RESULTS: In 1038 children, the anti-SARS-CoV-2 total antibody positivity rate was 8.5%. After multivariate logistic regression, significant risk factors included Hispanic ethnicity, public or absent insurance, a history of COVID-19 symptoms, exposure to person with COVID-19, a household member positive for SARS-CoV-2 and multi-family or apartment dwelling without a private entrance. 66% of seropositive children had no symptoms of COVID-19. Secondary analysis included orthogonal antibody testing with assays for 1) a receptor binding domain specific antigen and 2) a nucleocapsid specific antigen had concordance rates of 80.5% and 79.3% respectively. CONCLUSIONS: A much higher burden of SARS-CoV-2 infection, as determined by seropositivity, was found in children than previously reported; this was also higher compared to adults in the same region at a similar time. Contrary to prior reports, we determined children shoulder a significant burden of COVID-19 infection. The role of children's disease transmission must be considered in COVID-19 mitigation strategies including vaccination.

Seropositivity of COVID-19 among asymptomatic healthcare workers: A multi-site prospective cohort study from Northern Virginia, United States.

BACKGROUND: Because of their direct patient contact, healthcare workers (HCW) face an unprecedented risk of exposure to COVID-19. The aim of this study was to examine incidence of COVID-19 disease among asymptomatic HCW and community participants in Northern Virginia during 6 months of follow-up. METHODS: This is a prospective cohort study that enrolled healthy HCW and residents who never had a symptomatic COVID-19 infection prior to enrolment from the community in Northern Virginia from April to November 2020. All participants were invited to enrol in study, and they were followed at 2-, and 6-months intervals. Participants were evaluated by commercial chemiluminescence SARS-CoV-2 serology assays as part of regional health system and public health surveillance program to monitor the spread of COVID-19 disease. FINDINGS: Of a total of 1,819 asymptomatic HCW enrolled, 1,473 (96%) had data at two-months interval, and 1,323 (73%) participants had data at 6-months interval. At baseline, 21 (1.15%) were found to have prior COVID-19 exposure. At two-months interval, COVID-19 rate was 2.8% and at six months follow-up, the overall incidence rate increased to 4.8%, but was as high as 7.9% among those who belong to the youngest age group (20-29 years). Seroconversion rates in HCW were comparable to the seropositive rates in the Northern Virginia community. The overall incidence of COVID-19 in the community was 4.5%, but the estimate was higher among Hispanic ethnicity (incidence rate = 15.3%) potentially reflecting different socio-economic factors among the community participants and the HCW group. Using cross-sectional logistic regression and spatio-temporal mixed effects models, significant factors that influence the transmission rate among HCW include age, race/ethnicity, resident ZIP-code, and household exposure, but not direct patient contact. INTERPRETATION: In Northern Virginia, the seropositive rate of COVID-19 disease among HCW was comparable to that in the community.

Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.

This article introduces a flexible nonparametric approach for analyzing the association between covariates and power spectra of multivariate time series observed across multiple subjects, which we refer to as multivariate conditional adaptive Bayesian power spectrum analysis (MultiCABS). The proposed procedure adaptively collects time series with similar covariate values into an unknown number of groups and nonparametrically estimates group-specific power spectra through penalized splines. A fully Bayesian framework is developed in which the number of groups and the covariate partition defining the groups are random and fit using Markov chain Monte Carlo techniques. MultiCABS offers accurate estimation and inference on power spectra of multivariate time series with both smooth and abrupt dynamics across covariate by averaging over the distribution of covariate partitions. Performance of the proposed method compared with existing methods is evaluated in simulation studies. The proposed methodology is used to analyze the association between fear of falling and power spectra of center-of-pressure trajectories of postural control while standing in people with Parkinson's disease.

Relationship between kinematic gait parameters during three gait modifications designed to reduce peak knee abduction moment.

PURPOSE: Gait modifications designed to change a single kinematic parameter have reduced first peak internal knee abduction moment (PKAM). Prior research suggests unintended temporospatial and kinematic changes occur naturally while performing these modifications. We aimed to investigate i) the concomitant kinematic and temporospatial changes and ii) the relationship between gait parameters during three gait modifications (toe-in, medial knee thrust, and trunk lean gait). METHODS: Using visual real-time biofeedback, we collected 10 trials for each modification using individualized target gait parameters based on participants' baseline mean and standard deviation. Repeated measures ANOVA was performed to determine significant differences between conditions. Mixed effects linear regression models were then used to estimate the linear relationships among variables during each gait modification. All modifications reduced KAM by at least 5%. RESULTS: Modifications resulted in numerous secondary changes between conditions such as increased knee abduction during toe-in gait and increased knee flexion with medial knee thrust. Within gait modifications, relationships between kinematic parameters were similar for toe-in gait and medial knee thrust (i.e. increased toe-in and decreased knee abduction), while increased trunk lean showed no relationship with any other kinematic parameters during trunk lean trials. CONCLUSION: Two main mechanisms were found as a result of this investigation; the first being a pattern of toeing-in, knee abduction, flexion, and internal hip rotation, while trunk lean modification presented as a separate gait pattern with limited secondary changes. Future studies should consider providing feedback on multiple linked parameters, as it may feel more natural and optimize KAM reductions.

Empirical Frequency Band Analysis of Nonstationary Time Series.

The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be optimal or justified for adequately summarizing information from a given time series process under current study. There is a dearth of methods for empirically constructing statistically optimal bands for a given signal. The goal of this article is to provide a standardized, unifying approach for deriving and analyzing customized frequency bands. A consistent, frequency-domain, iterative cumulative sum based scanning procedure is formulated to identify frequency bands that best preserve nonstationary information. A formal hypothesis testing procedure is also developed to test which, if any, frequency bands remain stationary. The proposed method is used to analyze heart rate variability of a patient during sleep and uncovers a refined partition of frequency bands that best summarize the time-varying power spectrum.

Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression.

The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable, person-specific RAR measures that can be estimated from actigraphy. Actigraphs are wearable devices that dynamically record acceleration and provide indirect measures of physical activity over time. One class of useful RAR measures are those that quantify variability around a mean circadian pattern. However, current parametric and nonparametric RAR measures used by applied researchers can only quantify variability from a limited or undefined number of rhythmic sources. The primary goal of this article is to consider a new measure of RAR variability: the log-power spectrum of stochastic error around a circadian mean. This functional measure quantifies the relative contributions of variability about a circadian mean from all possibly frequencies, including weekly, daily, and high-frequency sources of variation. It can be estimated through a two-stage procedure that smooths the log-periodogram of residuals after estimating a circadian mean. The development of this measure was motivated by a study of depression in older adults and revealed that slow, rhythmic variations in activity from a circadian pattern are correlated with depression symptoms.

Circadian misalignment and weekend alcohol use in late adolescent drinkers: preliminary evidence.

Alcohol use accelerates during late adolescence, predicting the development of alcohol use disorders (AUDs) and other negative outcomes. Identifying modifiable risk factors for alcohol use during this time could lead to novel prevention approaches. Burgeoning evidence suggests that sleep and circadian factors are cross-sectionally and longitudinally linked to alcohol use and problems, but more proximal relationships have been understudied. Circadian misalignment, in particular, is hypothesized to increase the risk for AUDs, but almost no published studies have included a biological measure of misalignment. In the present study, we aimed to extend existing research by assessing the relationship between adolescent circadian misalignment and alcohol use on a proximal timeframe (over two weeks) and by including three complementary measures of circadian alignment. We studied 36 healthy late (18-22 years old, 22 females) alcohol drinkers (reporting ≥1, standard drink per week over the past 30 days) over 14 days. Throughout the study, participants reported prior day's alcohol use and prior night's sleep each morning via smartphone and a secure, browser-based interface. Circadian phase was assessed via the dim light melatonin onset (DLMO) in the laboratory on two occasions (Thursday and Sunday nights) in counterbalanced order. The three measures of circadian alignment included DLMO-midsleep interval, "classic" social jet lag (weekday-weekend difference in midsleep), and "objective" social jet lag (weekday-weekend difference in DLMO). Multivariate imputation by chained equations was used to impute missing data, and Poisson regression models were used to assess associations between circadian alignment variables and weekend alcohol use. Covariates included sex, age, Thursday alcohol use, and Thursday sleep characteristics. As predicted, greater misalignment was associated with greater weekend alcohol use for two of the three alignment measures (shorter DLMO-midsleep intervals and larger weekday-weekend differences in midsleep), while larger weekday-weekend differences in DLMO were associated with less alcohol use. Notably, in contrast to expectations, the distribution of weekday-weekend differences in DLMO was nearly equally distributed between individuals advancing over the weekend and those delaying over the weekend. This unexpected finding plausibly reflects the fact that college students are not subject to the same systematically earlier weekday schedules observed in high school students and working adults. These preliminary findings support the need for larger, more definitive studies investigating the proximal relationships between circadian alignment and alcohol use among late adolescents.

Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series.

Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS). The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. CABS is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse.

In vitro physiologically based extraction test (PBET) and bioaccessibility of arsenic and lead from various mine waste materials.

In vivo models show that the bioavailability of soil contaminants varies between site and type of matrix. Studies demonstrated that assuming 100% bioavailability of arsenic (As) and lead (Pb) from soils and mine waste materials overestimates the risk associated with human exposure. In in vitro systems, the simulated bioavailability of a contaminant is referred to as the "bioaccessibility" and is used as an alternative quantitative indicator for in vivo derived bioavailability estimates. The general concept of the in vitro extraction test is to predict the bioavailability of inorganic substances from solid matrices by simulating the gastrointestinal tract (GIT) environment. The aims of this study were to: (1) investigate the bioaccessibility of As and Pb from various mine wastes, including tailings, heap leach, and waste rock, using a physiologically based extraction test (PBET); (2) validate the bioaccessibility values from PBET with in vivo bioavailability values measured using animal models; and (3) correlate PBET results with the bioavailability values measured from alternative in vivo models (rats and cattle, from Bruce, 2004). Significant correlation was observed between bioaccessibility values from PBET, and bioavailability values generated for both rats and cattle, demonstrating the potential to utilize PBET as a relatively inexpensive alternative to in vivo models for bioavailability assessment.