DiffMAP-GP: Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning using Gaussian Processes.

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we develop a Bayesian framework (DiffMAP-GP) by placing Gaussian Process (GP) priors on the family of candidate maps. For sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs.

A Pilot Study to Test the Feasibility for a Randomized Controlled Trial of E-cigarettes as Harm Reduction Tools Among People Who Smoke and Previously Failed to Quit with Pharmacotherapy.

Introduction: We conducted a pilot study to test the feasibility of a future randomized controlled trial comparing e-cigarettes to traditional pharmacotherapy among people who smoke daily, were motivated to quit, and failed to quit within the past 5 years using pharmacotherapy.

Methods: Eligible participants were assigned to either: 1) an e-cigarette (n=20) or 2) combination nicotine replacement therapy (patches and lozenges) (n=10). Participants received 5 weeks of product and selected a quit date 1 week later.

Implementation of an "opt-out" tobacco treatment program in six hospitals in South Carolina.

Objective: Describe the screening, referral, and treatment delivery associated with an opt-out tobacco treatment program (TTP) implemented in six hospitals varying in size, rurality and patient populations.

Methods: Between March 6, 2021 and December 17, 2021, adult patients (≥ 18 years) admitted to six hospitals affiliated with the Medical University of South Carolina were screened for smoking status. The hospitals ranged in size from 82 to 715 beds.

Development of an Electronic Health Record Self-Referral Tool for Lung Cancer Screening: One-Group Posttest Study.

Background: Approximately 14 million individuals in the United States are eligible for lung cancer screening (LCS), but only 5.8% completed screening in 2021. Given the low uptake despite the potential great health benefit of LCS, interventions aimed at increasing uptake are warranted.

Learning Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning.

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs.