This study aimed to identify the regulatory mechanisms of white, blue, red lights on carotenoid and tocochromanol biosynthesis in mung bean sprouts. Results showed that three lights stimulated the increase of the predominated lutein (3.2-8.1 folds) and violaxanthin (2.1-6.1 folds) in sprouts as compared with dark control, as well as β-carotene (20-36 folds), with the best yield observed under white light. Light signals also promoted α- and γ-tocopherol accumulation (up to 1.8 folds) as compared with dark control. The CRTISO, LUT5 and DXS (1.24-6.34 folds) exhibited high expression levels under light quality conditions, resulting in an overaccumulation of carotenoids. The MPBQ-MT, TC and TMT were decisive genes in tocochromanol biosynthesis, and were expressed up to 4.19 folds as compared with control. Overall, the results could provide novel insights into light-mediated regulation and fortification of carotenoids and tocopherols, as well as guide future agricultural cultivation of mung bean sprouts.

UNASSIGNED: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.
UNASSIGNED: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.
UNASSIGNED: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett\'s esophagus, and detection of various abnormalities in wireless capsule endoscopy images.
UNASSIGNED: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.