Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.

Reducing electrophysiological (EP) signal noise is essential for diagnosis, mapping, and ablation, yet traditional approaches are suboptimal. This study tests the hypothesis that generative artificial intelligence (AI), specifically Variational Autoencoders (VAEs), can effectively denoise these signals by forming robust internal representations of 'clean' signals. Utilizing a dataset of 5706 time series from 42 patients with ischemic cardiomyopathy at risk of cardiac sudden death, we set out to apply a β-VAE model to denoise and reconstruct intra-ventricular monophasic action potential (MAP) signals, which have verifiable morphology.

Vector-based feedback of continuous wave radiofrequency compression cavity for ultrafast electron diffraction.

The temporal resolution of ultrafast electron diffraction at weakly relativistic beam energies (100 keV) suffers from space-charge induced electron pulse broadening. We describe the implementation of a radio frequency (RF) cavity operating in the continuous wave regime to compress high repetition rate electron bunches from a 40.4 kV DC photoinjector for ultrafast electron diffraction applications.

Hierarchical Organization of Single Crystal Polymorphs of Azobenzene Chromophore in Anisotropic Media Subjected to Local Thermal Gradients upon Cold Spraying.

The present article entails the emergence of diverse crystal polymorphs following thermal quenching into various coexistence regions of binary azobenzene chromophore (ACh)/diacrylate (DA) solution and of azobenzene/nematic liquid crystal (E7) mixture. Development of various crystal topologies encompassing rhomboidal and hexagonal shapes can be witnessed in a manner dependent on thermal quenched depths into the crystal + liquid coexistence region of ACh/DA system. Upon spraying with compressed carbon dioxide (CO ) fluid, the local temperature gradient is generated resulting in spherulitic morphology showing discrete lamellae undergoing twisting locally in some regions and branched dendrites or seaweeds in another.

Machine Learning Algorithms Evaluate Immune Response to Novel Antigens for Diagnosis of Tuberculosis.

Rationale: Tuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon- release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel antigens has the potential to improve standard immunodiagnostic tests for tuberculosis.