Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects' trajectories marginally for each functional covariate while using similarities in subjects' auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.

Interfacial Compatibility of Core-Shell Cellulose Nanocrystals for Improving Dynamic Covalent Adaptable Networks' Fracture Resistance in Nanohybrid Vitrimer Composites.

The development of polymeric nanocomposites with dynamic covalent adaptable networks and biobased nanomaterials has been a promising approach toward sustainable advanced materials, enabling reprogramming and recycling capabilities. Herein, a core-shell nanohybrid of functionalized cellulose nanocrystals (CNCs) is explored to provide crucial interfacial compatibility for improving the covalent adaptable networks of epoxy-thiol vitrimers in fracture resistance. The poly(ε-caprolactone) (PCL) shells grafted from CNC surfaces can be cross-linked with the covalent adaptable networks via a hot-pressing transesterification process. According to the additive concentration and annealing temperature, the stress relaxation behavior of nanohybrid vitrimer composites can be effectively regulated by the core-shell PCL-grafted CNC (CNC-PCL) nanohybrids from a dispersed to cross-linked interaction. The addition of 15 wt % of the core-shell CNC-PCLs exhibits the reinforced improvement of nanohybrid vitrimer composites in the average Young's modulus of 2.5×, fracture stress of 5.4×, and fracture strain of 2.0×. The research findings might have profound implications for developing synergistic interfacial compatibility between dynamic vitrimer networks and functional nanoparticles for advanced polymeric nanocomposites.

Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.

Intensive longitudinal data collected with ecological momentary assessment methods capture information on participants' behaviors, feelings, and environment in near real-time. While these methods can reduce recall biases typically present in survey data, they may still suffer from other biases commonly found in self-reported data (e.g., measurement error and social desirability bias). To accommodate potential biases, we develop a Bayesian hidden Markov model to simultaneously identify risk factors for subjects transitioning between discrete latent states as well as risk factors potentially associated with them misreporting their true behaviors. We use simulated data to demonstrate how ignoring potential measurement error can negatively affect variable selection performance and estimation accuracy. We apply our proposed model to smartphone-based ecological momentary assessment data collected within a randomized controlled trial that evaluated the impact of incentivizing abstinence from cigarette smoking among socioeconomically disadvantaged adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Effective connectivity between resting-state networks in depression.

RATIONALE: Although depression has been widely researched, findings characterizing how brain regions influence each other remains scarce, yet this is critical for research on antidepressant treatments and individual responses to particular treatments. OBJECTIVES: To identify pre-treatment resting state effective connectivity (rsEC) patterns in patients with major depressive disorder (MDD) and explore their relationship with treatment response. METHODS: Thirty-four drug-free MDD patients had an MRI scan and were subsequently treated for 6 weeks with an SSRI escitalopram 10 mg daily; the response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HAMD) score. RESULTS: rsEC networks in default mode, central executive, and salience networks were identified for patients with depression. Exploratory analyses indicated higher connectivity strength related to baseline depression severity and response to treatment. CONCLUSIONS: Preliminary analyses revealed widespread dysfunction of rsEC in depression. Functional rsEC may be useful as a predictive tool for antidepressant treatment response. A primary limitation of the current study was the small size; however, the group was carefully chosen, well-characterized, and included only medication-free patients. Further research in large samples of placebo-controlled studies would be required to confirm the results.

Nature-inspired robust hydrochromic film for dual anticounterfeiting.

Nature-inspired materials have been actively developed for anticounterfeiting applications. Among a variety of stimuli-responsive anticounterfeiting strategies, hydrochromic materials exhibit reversible color change in response to moisture or water and have the advantage of being easy to authenticate. However, the security level of current hydrochromic anticounterfeiting materials is not sufficient for practical applications since they only exhibit a single anticounterfeiting function, where the information switches between visible and invisible. To improve the security level and efficiency of hydrochromic anticounterfeiting materials, here we developed a robust dual hydrochromic material via the self-assembly of polyurethane (PU)-polyelectrolytes colloids with which the desired information can not only switch between visible and invisible but also transform from one pattern to another within 3 s without the need of any external instruments. The bio-inspiration, material design and demonstrated hydrochromic properties might have profound implications for using colloidal complexes to make advanced anticounterfeiting materials.