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.

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.

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.

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.

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.

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.

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.

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 , obtained by assigning numerical values, or , to categories that optimally emphasize oscillations at each frequency.

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.

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.

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.

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.

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.

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).