Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data.

Background/objectives: Although eligibility criteria for clinical trials significantly impact study outcomes, these criteria are often established without scientific justification, leading to delayed recruitment, small sample sizes, and limited study generalizability. Persistent Lyme disease (PLD) presents unique challenges due to symptom variability, inconsistent treatment responses, and the lack of reliable biomarkers, underscoring the need for scientifically justified eligibility criteria.

Objective: This study examines the effects of commonly used enrollment criteria on sample yield in PLD clinical trials using real-world data (RWD) from the MyLymeData patient registry.

Matrix Completion With Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling.

While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in real-world applications. In this work, we propose a novel and easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS).

HOSVD-Based Algorithm for Weighted Tensor Completion.

Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful approaches and analyses. Tensor completion is the tensor analog that attempts to impute missing tensor entries from similar low-rank type assumptions. In this paper, we study the tensor completion problem when the sampling pattern is deterministic and possibly non-uniform.

Analysis of fast structured dictionary learning.

Sparsity-based models and techniques have been exploited in many signal processing and imaging applications. Data-driven methods based on dictionary and sparsifying transform learning enable learning rich image features from data and can outperform analytical models. In particular, alternating optimization algorithms have been popular for learning such models.

Antibiotic Treatment Response in Chronic Lyme Disease: Why Do Some Patients Improve While Others Do Not?

There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among these patients varies: some patients improve with treatment while others do not. A previous study examined treatment response variation in a sample of over 3500 patients enrolled in the MyLymeData patient registry developed by LymeDisease.

Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity.

Diffuse optical tomography (DOT) is an important functional imaging modality in clinical diagnosis and treatment. As the number of wavelengths in the acquired DOT data grows, it becomes very challenging to reconstruct diffusion and absorption coefficients of tissue, i.e.

Near-optimal compressed sensing guarantees for total variation minimization.

Consider the problem of reconstructing a multidimensional signal from an underdetermined set of measurements, as in the setting of compressed sensing. Without any additional assumptions, this problem is ill-posed. However, for signals such as natural images or movies, the minimal total variation estimate consistent with the measurements often produces a good approximation to the underlying signal, even if the number of measurements is far smaller than the ambient dimensionality.