Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Evidence for gastrointestinal infection of SARS-CoV-2

Abstract: The new coronavirus (SARS-CoV-2) outbreak originating from Wuhan, China, poses a threat to global health. While it’s evident that the virus invades respiratory tract and transmits from human to human through airway, other viral tropisms and transmission routes remain unknown. We tested viral RNA in stool from 73 SARS-CoV-2-infected hospitalized patients using rRT-PCR. 53.42% of the patients tested positive in stool. 23.29% of the patients remained positive in feces even after the viral RNA decreased to undetectable level in respiratory tract. The viral RNA was also detected in gastrointestinal tissues. Furthermore, gastric, duodenal and rectal epithelia showed positive immunofluorescent staining of viral host receptor ACE2 and viral nucleocapsid protein in a case of SARS-CoV-2 infection. Our results provide evidence for gastrointestinal infection of SARS-CoV-2, highlighting its potential fecal-oral transmission route.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Abstract: Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

Design and Synthesis of FeOOH/CeO2 Heterolayered Nanotube Electrocatalysts for the Oxygen Evolution Reaction.

Abstract: FeOOH/CeO2 heterolayered nanotubes supported on Ni foam as efficient oxygen evolution electrocatalysts are reported. The hybrid structure can obviously promote the catalytic performance for the oxygen evolution reaction, such as low onset potential, high electroactivity, and excellent long-term durability. This study provides a new route to the design and fabrication of electrocatalysts with high electroactivity and durability for oxygen evolution.

Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction

Abstract: An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.