Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning.

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI).

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy.

Recent advances in detectors for imaging and spectroscopy have afforded in situ, rapid acquisition of hyperspectral data. While electron energy loss spectroscopy (EELS) data acquisition speeds with electron counting are regularly reaching 400 frames per second with near-zero read noise, signal to noise ratio (SNR) remains a challenge owing to fundamental counting statistics. In order to advance understanding of transient materials phenomena during rapid acquisition EELS, trustworthy analysis of noisy spectra must be demonstrated.

Physical therapy management, surgical treatment, and patient-reported outcomes measures in a prospective observational cohort of patients with neurogenic thoracic outlet syndrome.

Objective: To assess the results of physical therapy management and surgical treatment in a prospective observational cohort of patients with neurogenic thoracic outlet syndrome (NTOS) using patient-reported outcomes measures.

Methods: Of 183 new patient referrals from July 1 to December 31, 2015, 150 (82%) met the established clinical diagnostic criteria for NTOS. All patients underwent an initial 6-week physical therapy trial.