H5N1 high pathogenicity avian influenza virus in migratory birds exhibiting low pathogenicity in mallards increases its risk of transmission and spread in poultry.

In 2020, an H5N1 avian influenza virus of clade 2.3.4.4b was detected in Europe for the first time and was spread throughout the world by wild migratory birds, resulting in the culling of an unprecedented number of wild birds and poultry due to the epidemic. In February 2023, we isolated and identified a strain of H5N1 high pathogenicity avian influenza virus from a swab sample from a grey crane in Ningxia, China. Phylogenetic analysis of the Hemagglutinin (HA) gene showed that the virus belonged to clade 2.3.4.4b, and several gene segments were closely related to H5N1 viruses infecting humans in China. Analysis of key amino acid sites revealed that the virus contained multiple amino acid substitutions that facilitate enhanced viral replication and mammalian pathogenicity. The results of animal challenge experiments showed that the virus is highly pathogenic to chickens, moderately pathogenic to BALB/c mice, and highly infectious but not lethal to mallards. Moreover, the virus exhibited minor antigenic drift compared with the H5-Re14 vaccine strain. To this end, we need to pay more attention to the monitoring of wild birds to prevent further spread of viruses to poultry and mammals, including humans.

Biological and genomic analysis of a symbiotic nitrogen fixation defective mutant in Medicago truncatula.

Medicago truncatula has been selected as one of the model legume species for gene functional studies. To elucidate the functions of the very large number of genes present in plant genomes, genetic mutant resources are very useful and necessary tools. Fast Neutron (FN) mutagenesis is effective in inducing deletion mutations in genomes of diverse species. Through this method, we have generated a large mutant resource in M. truncatula. This mutant resources have been used to screen for different mutant using a forward genetics methods. We have isolated and identified a large amount of symbiotic nitrogen fixation (SNF) deficiency mutants. Here, we describe the detail procedures that are being used to characterize symbiotic mutants in M. truncatula. In recent years, whole genome sequencing has been used to speed up and scale up the deletion identification in the mutant. Using this method, we have successfully isolated a SNF defective mutant FN007 and identified that it has a large segment deletion on chromosome 3. The causal deletion in the mutant was confirmed by tail PCR amplication and sequencing. Our results illustrate the utility of whole genome sequencing analysis in the characterization of FN induced deletion mutants for gene discovery and functional studies in the M. truncatula. It is expected to improve our understanding of molecular mechanisms underlying symbiotic nitrogen fixation in legume plants to a great extent.

Highly Pathogenic Avian Influenza A(H5N8) Clade 2.3.4.4b Viruses in Satellite-Tracked Wild Ducks, Ningxia, China, 2020.

During October 2020, we identified 13 highly pathogenic avian influenza A(H5N8) clade 2.3.4.4b viruses from wild ducks in Ningxia, China. These viruses were genetically related to H5N8 viruses circulating mainly in poultry in Europe during early 2020. We also determined movements of H5N8 virus‒infected wild ducks and evidence for spreading of viruses.

miR-548aq-3p is a novel target of Far infrared radiation which predicts coronary artery disease endothelial colony forming cell responsiveness.

Non-invasive far infrared radiation (FIR) has been observed to improve the health of patients with coronary artery disease (CAD). Endothelial colony forming cells (ECFCs) contribute to vascular repair and CAD. The goal of this study was to uncover the role of FIR in ECFCs function and to reveal potential biomarkers for indication of FIR therapy in CAD patients. FIR significantly enhanced in vitro migration (transwell assay) and tube formation (tube length) capacities in a subpopulation of CAD ECFCs. Clinical parameters associated with the responsiveness of ECFCs to FIR include smoking and gender. ECFCs from CAD patients that smoke did not respond to FIR in most cases. In contrast, ECFCs from females showed a higher responsiveness to FIR than ECFCs from males. To decipher the molecular mechanisms by which FIR modulates ECFCs functions, regardless of sex, RNA sequencing analysis was performed in both genders of FIR-responsive and FIR-non/unresponsive ECFCs. Gene Ontology (GO) analysis of FIR up-regulated genes indicated that the pathways enriched in FIR-responsive ECFCs were involved in cell viability, angiogenesis and transcription. Small RNA sequencing illustrated 18 and 14 miRNAs that are up- and down-regulated, respectively, in FIR-responsive CAD ECFCs in both genders. Among the top 5 up- and down-regulated miRNAs, down-regulation of miR-548aq-3p in CAD ECFCs after FIR treatment was observed in FIR-responsive CAD ECFCs by RT-qPCR. Down-regulation of miR-548aq-3p was correlated with the tube formation activity of CAD ECFCs enhanced by FIR. After establishment of the down-regulation of miR-548aq-3p by FIR in CAD ECFCs, we demonstrated through overexpression and knockdown experiments that miR-548aq-3p contributes to the inhibition of the tube formation of ECFCs. This study suggests the down-regulation of miR-548aq-3p by FIR may contribute to the improvement of ECFCs function, and represents a novel biomarker for therapeutic usage of FIR in CAD patients.

Pressure Ulcer Monitoring Platform-A Prospective, Human Subject Clinical Study to Validate Patient Repositioning Monitoring Device to Prevent Pressure Ulcers.

Objective: The objective of this prospective clinical study was to validate two prototype pressure ulcer monitoring platform (PUMP) devices, (PUMP1 and PUMP2), to promote optimal bed repositioning of hospitalized patients to prevent pressure ulcers (PUs). Approach: PUMP1 was a wearable electronic device attached to the patient gown with no skin contact. PUMP2 was a set of four identical electronic devices placed under the patient's bed wheels. A video camera recorded events in the patient room while measurements from the PUMP devices were correlated with true patient repositioning activity. The performance of these PUMP devices developed by our research team were evaluated and compared by both clinicians and engineers. Results: Ten mobility-restricted patients were enrolled into the study. Repositioning movement was recorded by both PUMP devices for 10 ± 2 h and corroborated with video capture. One hundred thirty-seven movements in total were detected by both PUMP1 and PUMP2 over 105 h of capture. Two false positives were detected by the sensors and 11 movements were missed by the sensors. PUMP1 and PUMP2 never conflicted in data collection. Innovation: The presented study evaluated two different sensors' abilities to capture accurate patient repositioning to eventually prevent PU formation. Importantly, detection of patient motion was completed without contact to patient skin. Conclusion: The clinical study demonstrated successful capture of patient repositioning movement by both PUMP1 and PUMP2 devices with 85% reliability, 2 false positives, and 11 missed movements. In future studies, the PUMP devices will be combined with a SMS-based mobile phone alert system to improve caregiver repositioning behavior.

Automatic food detection in egocentric images using artificial intelligence technology.

OBJECTIVE: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. DESIGN: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. RESULTS: A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. CONCLUSIONS: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

Reliability and validity of food portion size estimation from images using manual flexible digital virtual meshes.

OBJECTIVE: The eButton takes frontal images at 4s intervals throughout the day. A three-dimensional manually administered wire mesh procedure has been developed to quantify portion sizes from the two-dimensional images. The present paper reports a test of the inter-rater reliability and validity of use of the wire mesh procedure. DESIGN: Seventeen foods of diverse shapes and sizes served on plates, bowls and cups were selected to rigorously test the portion assessment procedure. A dietitian not involved in inter-rater reliability assessment used standard cups to independently measure the quantities of foods to generate the 'true' value for a total of seventy-five 'served' and seventy-five smaller 'left' images with diverse portion sizes. SETTING: The images appeared on the computer to which the digital wire meshes were applied. SUBJECTS: Two dietitians and three engineers independently estimated portion size of the larger ('served') and smaller ('left') images for the same foods. RESULTS: The engineers had higher reliability and validity than the dietitians. The dietitians had lower reliabilities and validities for the smaller more irregular images, but the engineers did not, suggesting training could overcome this limitation. The lower reliabilities and validities for foods served in bowls, compared with plates, suggest difficulties with the curved nature of the bowls. CONCLUSIONS: The wire mesh procedure is an important step forward in quantifying portion size, which has been subject to substantial self-report error. Improved training procedures are needed to overcome the identified problems.

Application of Intelligent Recommendation Techniques for Consumers' Food Choices in Restaurants.

Currently, there has been a new trend in applying modern robotics, information technology, and artificial intelligence to restaurants for improvements of food service, cost-effectiveness, and customer satisfaction. As robots replace humans to serve food, there is a clear need for robotic servers to help consumers select foods from a menu that satisfies their preferences such as taste and nutrition. However, currently, little is known about how eating behaviors drive food choices, and it is often difficult for consumers to make choices from a variety of foods offered by the typical restaurant, even with the assistance from a human server. In this paper, we conduct an exploratory study on an intelligent food choice method that recommends dishes by predicting individual's dietary preference, including ingredients, types of spices, price, etc. A multi-attribute relation matrix tri-factorization (MARMTF) technique is developed for a relation-driven food recommendation system. First, the user's ordering history and their rating scores of the foods in the menu are gathered and represented by a user-dish rating matrix. Next, the attribute relations of the ingredients, spicy level, and price of each food choice are extracted to construct a group of the relation matrices. Then, these matrices are integrated into a large block matrix. In the next step, a matrix tri-factorization algorithm is employed to decompose the block matrix and fuse the complex relationships into matrix factors. Further, a set of approximation block matrices are constructed and the predicted food rating matrix is generated. Finally, the foods (dishes) with sufficiently high preference scores are recommended to the consumers. Our experiments demonstrate that the MARMTF technique can provide effective dish recommendation for customers. Our system significantly simplifies the daunting task of making food choices and has a great potential in providing intelligent and professionally trained non-human waiters and waitresses for employment by future restaurants.

Saliency-aware food image segmentation for personal dietary assessment using a wearable computer.

Image-based dietary assessment has recently received much attention in the community of obesity research. In this assessment, foods in digital pictures are specified, and their portion sizes (volumes) are estimated. Although manual processing is currently the most utilized method, image processing holds much promise since it may eventually lead to automatic dietary assessment. In this paper we study the problem of segmenting food objects from images. This segmentation is difficult because of various food types, shapes and colors, different decorating patterns on food containers, and occlusions of food and non-food objects. We propose a novel method based on a saliency-aware active contour model (ACM) for automatic food segmentation from images acquired by a wearable camera. An integrated saliency estimation approach based on food location priors and visual attention features is designed to produce a salient map of possible food regions in the input image. Next, a geometric contour primitive is generated and fitted to the salient map by means of multi-resolution optimization with respect to a set of affine and elastic transformation parameters. The food regions are then extracted after contour fitting. Our experiments using 60 food images showed that the proposed method achieved significantly higher accuracy in food segmentation when compared to conventional segmentation methods.

A FPGA Implementation of JPEG Baseline Encoder for Wearable Devices.

In this paper, an efficient field-programmable gate array (FPGA) implementation of the JPEG baseline image compression encoder is presented for wearable devices in health and wellness applications. In order to gain flexibility in developing FPGA-specific software and balance between real-time performance and resources utilization, A High Level Synthesis (HLS) tool is utilized in our system design. An optimized dataflow configuration with a padding scheme simplifies the timing control for data transfer. Our experiments with a system-on-chip multi-sensor system have verified our FPGA implementation with respect to real-time performance, computational efficiency, and FPGA resource utilization.

A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.

In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.

An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.

Recently, wearable computers have become new members in the family of mobile electronic devices, adding new functions to those provided by smart-phones and tablets. As "always-on" miniature computers in the personal space, they will play increasing roles in the field of healthcare. In this work, we present our development of eButton, a wearable computer designed as a personalized, attractive, and convenient chest pin in a circular shape. It contains a powerful microprocessor, numerous electronic sensors, and wireless communication links. We describe its design concepts, electronic hardware, data processing algorithms, and its applications to the evaluation of diet, physical activity and lifestyle in the study of obesity and other chronic diseases.

eButton: A Wearable Computer for Health Monitoring and Personal Assistance.

Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.

Perceptual Contrast Enhancement with Dynamic Range Adjustment.

Recent years, although great efforts have been made to improve its performance, few Histogram equalization (HE) methods take human visual perception (HVP) into account explicitly. The human visual system (HVS) is more sensitive to edges than brightness. This paper proposes to take use of this nature intuitively and develops a perceptual contrast enhancement approach with dynamic range adjustment through histogram modification. The use of perceptual contrast connects the image enhancement problem with the HVS. To pre-condition the input image before the HE procedure is implemented, a perceptual contrast map (PCM) is constructed based on the modified Difference of Gaussian (DOG) algorithm. As a result, the contrast of the image is sharpened and high frequency noise is suppressed. A modified Clipped Histogram Equalization (CHE) is also developed which improves visual quality by automatically detecting the dynamic range of the image with improved perceptual contrast. Experimental results show that the new HE algorithm outperforms several state-of-the-art algorithms in improving perceptual contrast and enhancing details. In addition, the new algorithm is simple to implement, making it suitable for real-time applications.