Developmental Imaging of Radish Sprouts Using Dynamic Optical Coherence Tomography.

The germination process of radish sprouts was investigated in detail using volumetric dynamic optical coherence tomography (OCT). Dynamic OCT involves the sequential acquisition of 16 OCT images and subsequent temporal variance analysis of each pixel, enabling non-invasive visualization of the cellular and tissue activities of plants. The radish sprouts were longitudinally investigated for up to 12 days, and changes in morphology and dynamic OCT image patterns were observed as the plants developed.

A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic health problems. Wearable sensors offer an effective solution for continuous and real-time stress monitoring due to their non-intrusive nature and ability to monitor vital signs, e.

Clinical practice recommendations for management of Diabetes Mellitus in Arab region: An expert consensus statement from Arab Diabetes Forum (ADF).

Prevalence of diabetes in Arab region has significantly increased, resulting in a significant economic burden on healthcare systems. This surge can be attributed to obesity, rapid urbanization, changing dietary habits, and sedentary lifestyles. The Arab Diabetes Forum (ADF) has established localized recommendations to tackle the region's rising diabetes prevalence.

Neural-network based high-speed volumetric dynamic optical coherence tomography.

We demonstrate deep-learning neural network (NN)-based dynamic optical coherence tomography (DOCT), which generates high-quality logarithmic-intensity-variance (LIV) DOCT images from only four OCT frames. The NN model is trained for tumor spheroid samples using a customized loss function: the weighted mean absolute error. This loss function enables highly accurate LIV image generation.