Flavonoids are widely distributed in mulberry leaves and have been recognized for their beneficial physiological effects on the human health. Here, we analyzed variations in 44 flavonoid compounds among 91 mulberry resources. Metabolic profiling revealed that O-rhamnosylated flavonols and malonylated flavonol glycosides, including rutin and quercetin 3-O-(6″-O-malonylglucoside) (Q3MG), were absent from Morus notabilis and multiple mulberry (Morus alba L.) resources. Transcriptome and phylogenetic analyses of flavonoid-related UDP-glycosyltransferases (UGTs) suggested that the flavonol 3-O-glucoside-O-rhamnosyltransferase (FGRT) KT324624 is a key enzyme involved in rutin synthesis. A recombinant FGRT protein was able to convert kaempferol/quercetin 3-O-glucoside to kaempferol 3-O-rutinoside (K3G6″Rha) and rutin. The recombinant FGRT was able to use 3-O-glucosylated flavonols but not flavonoid aglycones or 7-O-glycosylated flavonoids as substrates. The enzyme preferentially used UDP-rhamnose as the sugar donor, indicating that it was a flavonol 3-O-glucoside: 6″-O-rhamnosyltransferase. This study provided insights into the biosynthesis of rutin in mulberry.

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A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.

Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food-handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.

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