Colloidal heat engines are paradigmatic models to understand the conversion of heat into work in a noisy environment - a domain where biological and synthetic nano/micro machines function. While the operation of these engines across thermal baths is well-understood, how they function across baths with noise statistics that is non-Gaussian and also lacks memory, the simplest departure from the thermal case, remains unclear. Here we quantified the performance of a colloidal Stirling engine operating between an engineered memoryless non-Gaussian bath and a Gaussian one. In the quasistatic limit, the non-Gaussian engine functioned like a thermal one as predicted by theory. On increasing the operating speed, due to the nature of noise statistics, the onset of irreversibility for the non-Gaussian engine preceded its thermal counterpart and thus shifted the operating speed at which power is maximum. The performance of nano/micro machines can be tuned by altering only the nature of reservoir noise statistics.
Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.
Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively integrating and analyzing multi-omics data remains challenging due to their heterogeneity and high dimensionality. Existing methods often struggle with noise, redundant features, and the complex interactions between different omics layers, leading to suboptimal performance.
Objectives: Implementation of diffusion-weighted imaging (DWI) for abdominal imaging in children has challenges due to motion artifacts exacerbated by long acquisition times. We aimed to compare acquisition time and image quality between conventional DWI and multi-band (MB) DWI of the liver in children and young adults.
Methods: Clinical MRI exams from May 2023 to January 2024 were reviewed, including four DWI sequences: respiratory-triggered (RTr, clinical standard), free-breathing (FB), MB-DWI with shift factor 1 (MBsf1), and MB-DWI with shift factor 2 (MBsf2).
Background: Three-dimensional MR fingerprinting (3D-MRF) has been increasingly used to assess cartilage degeneration, particularly in the knee joint, by looking into multiple relaxation parameters. A comparable 3D-MRF approach can be adapted to assess cartilage degeneration for the hip joint, with changes to accommodate specific challenges of hip joint imaging.
Purpose: To demonstrate the feasibility and repeatability of 3D-MRF in the bilateral hip jointly we map proton density (PD), T, T, T, and ∆B in clinically feasible scan times.
