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10,626 results on '"Shen, Ming"'

116. International Consensus on Natural Orifice Specimen Extraction Surgery (NOSES) for Gastric Cancer (2019)

Author

The International Alliance of NOSES, Guan, Xu, Liu, Zheng, Parvaiz, Amjad, Longo, Antonio, Saklani, Avanish, Shafik, Ali A., Cai, Jian-Chun, Ternent, Charles A., Chen, Lin, Kayaalp, Cuneyt, Sumer, Fatih, Nogueira, Fernanda, Gao, Feng, Han, Fang-Hai, He, Qing-Si, Chun, Ho-Kyung, Huang, Chang-Ming, Huang, Hai-Yang, Huang, Rui, Jiang, Zhi-Wei, Khan, Jim S., da Costa Pereira, Joaquim Manuel, Nunoo-Mensah, Joseph W., Son, Jung Tack, Kang, Liang, Uehara, Keisuke, Lan, Ping, Li, Le-Ping, Liang, Han, Liu, Bing-Rong, Liu, Juan, Ma, Dan, Shen, Ming-Yin, Islam, Mohammad Rashidul, Samalavicius, Narimantas Evaldas, Pan, Kai, Tsarkov, Petr V., Qin, Xin-Yu, Escalante, Ricardo, Efetov, Sergey, Jeong, Seung Kyu, Lee, Suk-Hwan, Sun, Dong-Hui, Sun, Li, Garmanova, Tatiana, Tian, Yan-Tao, Wang, Gui-Yu, Wang, Guo-Jun, Wang, Guo-Rong, Wang, Xiao-Qiang, Chen, William Tzu-Liang, Lee, Woo-Yong, Yan, Su, Yang, Zu-Li, Yu, Gang, Yu, Pei-Wu, Zhao, Dan, Zhong, Yun-Shi, Wang, Jian-Ping, Wang, Xishan, and Wang, Xishan, editor

Published
2023
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117. International Guideline on Natural Orifice Specimen Extraction Surgery (NOSES) for Colorectal Cancer (2023 Version)

Author

Committee of Colorectal Cancer Society Chinese Anti-Cancer Association, International NOSES Alliance, Liu, Zheng, Guan, Xu, Zhang, Mingguang, Hu, Xiyue, Yang, Ming, Bai, Junge, Li, Jun, Yu, Shaojun, Ding, Kefeng, He, Qing-Si, Kang, Liang, Ma, Dan, Fu, Chuangang, Hu, Junhong, Wei, Ye, Sun, Dong-Hui, Yu, Gang, He, Songbing, Wang, Changyou, Gao, Yanwei, Wang, Gui-Yu, Yao, Hongliang, Peng, Jian, Zheng, Yangchun, Jiang, Bo, Li, Taiyuan, Xiong, Zhiguo, Sun, Xuejun, Wang, Zhenning, Meng, William, Chen, William Tzu-Liang, Shen, Ming-Yin, Marks, John H., Ternent, Charles A., Shaw, Darcy D., Khan, Jim S., Tsarkov, Petr V., Tulina, Inna, Efetov, Sergey, da Costa Pereira, Joaquim Manuel, Nogueira, Fernanda, Escalante, Ricardo, Leroy, Joel, Saklani, Avanish, Dulskas, Audrius, Kayaalp, Cuneyt, Nishimura, Atsushi, Uehara, Kay, Lee, Woo-Yong, Kim, Sang-Hoo, Lee, Suk-Hwan, Wang, Xishan, and Wang, Xishan, editor

Published
2023
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118. Design and Evaluation of a Novel Passive Shoulder Exoskeleton Based on VSM Torque Generator

Author

Zhu, Yu, Balser, Felix, Shen, Ming, Bai, Shaoping, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, and Okada, Masafumi, editor

Published
2023
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119. Laparoscopic Hartmann’s Procedure

Author

Shao, Yen-Chen, Shen, Ming-Yin, Chen, William Tzu-Liang, Lomanto, Davide, editor, Chen, William Tzu-Liang, editor, Fuentes, Marilou B., editor, Lee-Ong, Alembert, Section Editor, Rose Nery, Emily, Section Editor, Lawenko, Michael, Section Editor, Sta. Clara, Eva Lourdes, Section Editor, Fuentes, Marilou Blancos, Section Editor, Tang, Siau Wei, Section Editor, Agarwal, Narendra, Section Editor, Shabbir, Asim, Section Editor, Rao, Jaideep, Section Editor, Goel, Rajat, Section Editor, Anh Tuan, Le Quan, Section Editor, Chua, Henry, Section Editor, Gupta, Rakesh, Section Editor, Imamura, Kiyotaka, Section Editor, Lauro, Enrico, Section Editor, Salgaonkar, Hrisikesh, Section Editor, and Malik, Sajid, Section Editor

Published
2023
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120. Laparoscopic Left Hemicolectomy

Author

Shen, Ming-Yin, Leow, Yeen Chin, Chen, William Tzu-Liang, Lomanto, Davide, editor, Chen, William Tzu-Liang, editor, Fuentes, Marilou B., editor, Lee-Ong, Alembert, Section Editor, Rose Nery, Emily, Section Editor, Lawenko, Michael, Section Editor, Sta. Clara, Eva Lourdes, Section Editor, Fuentes, Marilou Blancos, Section Editor, Tang, Siau Wei, Section Editor, Agarwal, Narendra, Section Editor, Shabbir, Asim, Section Editor, Rao, Jaideep, Section Editor, Goel, Rajat, Section Editor, Anh Tuan, Le Quan, Section Editor, Chua, Henry, Section Editor, Gupta, Rakesh, Section Editor, Imamura, Kiyotaka, Section Editor, Lauro, Enrico, Section Editor, Salgaonkar, Hrisikesh, Section Editor, and Malik, Sajid, Section Editor

Published
2023
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129. Safety, immunogenicity, and efficacy of a modified COVID-19 mRNA vaccine, SW-BIC-213, in healthy people aged 18 years and above: a phase 3 double-blinded, randomized, parallel controlled clinical trial in Lao PDR (Laos)

Author

Fang, Yi, Li, Jing-Xin, Duangdany, Davone, Li, Yang, Guo, Xi-Lin, Phamisith, Chanthala, Yu, Bo, Shen, Ming-Yun, Luo, Bin, Wang, Yu-Zhu, Liu, Si-Jun, Zhao, Fan-Fan, Xu, Cong-Cong, Qiu, Xu-Hui, Yan, Rong, Gui, Yu-Zhou, Pei, Rong-Juan, Wang, Jie, Shen, Haifa, Guan, Wu-Xiang, Li, Hang-Wen, and Mayxay, Mayfong

Published
2024
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134. Electrochemical Monitoring of Sulfadiazine via La@CeO Incorporated with Reduced Graphene Oxide

Author

Francis Packiaraj Don Disouza, Ruspika Sundaresan, Shen-Ming Chen, Balaji Ramachandran, and Narendhar Chandrasekar

Subjects
sulfonamides, reduced graphene oxide, hydrothermal, differential pulse voltammetry, Analytical chemistry, QD71-142
Abstract

In recent years, indiscriminate consumption and dumping of antibiotics have become destructive to human health and causes ecotoxicological pollution. Here, the irregular particle nanosized dendrite structure of lanthanum-doped cerium oxide (LCO) decorated with sheet-like reduced graphene oxide (RGO) composite was utilized to detect the sulfonamide-based drug sulfadiazine (SZ). LCO@RGO nanocomposite was prepared using the hydrothermal method, the synergistic effect between LCO and RGO facilitates electron transferability and conductivity which enhances the electrochemical properties toward the detection of SZ. The detection of SZ expressed a lower detection limit (0.005 µM) and linear range (0.01–265 µM) of the fabricated LCO@RGO/GCE electrode toward SZ, analyzed using the highly sensitive DPV technique. Also, DPV was utilized to determined shows good repeatability, reproducibility, and storage stability of fabricated LCO@RGO/GCE. Moreover, effective practicability was proven in human blood serum and river water samples with great recovery results. All the above probes the synthesized LCO@RGO’s thriving and outstanding electrocatalytic performance of this nanocomposite’s highly sensitive detection of SZ in real biological and environmental samples.

Published
2023
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135. Protective effect of Lyophyllum ulmarium fibrolytic enzyme on liver injury induced in hyperlipidemia rats

Author

LI Yu, SU Xin, ZHANG Jun-hui, ZHANG Rui-meng, and SHEN Ming-hua

Subjects
lyophyllum ulmarium, fibrinolytic enzyme, hyperlipidemia, liver injury, inflammation, Food processing and manufacture, TP368-456
Abstract

Objective: To investigate the protective effect and mechanism of Lyophyllum ulmarium fibrinolytic enzyme （LUFE） on rats with hyperlipidemia. Methods: Hyperlipidemia models were established by feeding with a high-fat diet, and the model rats were gavaged with different doses of LUFE. HE stain was used to analyze the morphological changes of liver.The biochemical indexes of rat plasma were detected by colorimetric method.WB analyses the expression level of related proteins. Results: LUFE could alleviate the liver tissue injury of hyperlipidemia rats and reduce the plasma lipid level and IL-6, TNF-α，MCP-1，ALT，AST. LUFE can down regulate TLR4，MyD88，phosphorylated PI3K，phosphorylated Akt and phosphorylated NF-кB protein level. Conclusion: LUFE protects against hyperlipidemia-induced liver injury in rats by regulating PI3K/Akt/NF-кB and TLR4/MyD88/NF-кB signaling pathways.

Published
2023
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136. Analysis of value shaping education for college students in the view of ideological education in colleges and universities based on deep learning

Author

Zhuang Huaijun and Shen Ming

Subjects
roberta-bilstm-attention, values emotion, correlation, standard regression coefficient, values guidance, 22a10, Mathematics, QA1-939
Abstract

This paper proposes a values sentiment analysis model based on RoBERTa-BiLSTM-Attention based on the mechanism and path of values process evaluation under sentiment analysis. After extracting and presenting the values and sentiment features data, we build the values dataset of college students, finish the training of the model, and obtain the classification results of values and sentiment. The college students’ value identity model is established to analyze the value shaping of education at the national, social, and individual levels. The correlation coefficients of the influencing factors of college students’ values were analyzed, and the correlation coefficients of the influencing factors were less than 0.75, and the p-value was less than 0.01. The hypotheses were put forward, and the standardized regression coefficient of the “social role model” was 0.2658, which was significantly higher than that of the other indicators, and the standardized regression coefficient of “emotional satisfaction” was 0.2658. The regression coefficient of “emotional satisfaction” is 0.1987, second only to cognitive identity. In view of the existence of the problem of values, the proposed political education intervention guidance strategy, after the political education intervention, 71.3625% of the students think that the values of guidance have a great help, but there are still some shortcomings, compared with this paper puts forward the optimization of the values of guidance recommendations.

Published
2024
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137. TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

Author

Lin, Bill Yuchen, Lee, Dong-Ho, Shen, Ming, Moreno, Ryan, Huang, Xiao, Shiralkar, Prashant, and Ren, Xiang

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20% of the trigger-annotated sentences results in a comparable performance as using 70% of conventional annotated sentences., Comment: Accepted to the ACL 2020. Project page: https://inklab.usc.edu/TriggerNER/ (Fixed a few typos and added a new figure.)

Published
2020

139. An efficient electrochemical sensor based on transition metal sulfides for the detection of flufenamic acid

Author

Rex Shanlee Santhiyagu Sahayaraj, Ruspika Sundaresan, Shen-Ming Chen, Balaji Ramachandran, and Narendhar Chandrasekar

Subjects
Transition metal sulfides, Flufenamic acid, DPV, Electrochemical sensor, Inorganic chemistry, QD146-197
Abstract

Transition metal sulfides (TMS) have been receiving specific attention due to their excellent physiochemical properties and electrochemical activity. In this article, we demonstrate the synthesis of sea-urchin-like Bi2S3 combined with MoS2 hierarchical nanoflowers via a hydrothermal route for electrochemical sensing of Flufenamic acid (FMA). FMA is one of the non-steroidal anti-inflammatory drugs (NSAIDs) which endangers human health. The Bi2S3/MoS2 composite's formation was confirmed by various characterizations via microscopic and spectroscopic techniques. The synergistic effect between the two metal sulfides improves electron transport and enables more active sites. Under optimal conditions, the resultant nanocomposite exhibits a high sensitivity value (0.44 ​μA ​μM−1 ​cm−2), a reasonable limit of detection (0.009 ​μM), and a broad linear range (0.01 – 453 ​μM). Furthermore, the electrochemical detection of FMA in biological samples was achieved with good reliability and decent recovery.

Published
2023
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140. CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

Author

Lin, Bill Yuchen, Zhou, Wangchunshu, Shen, Ming, Zhou, Pei, Bhagavatula, Chandra, Choi, Yejin, and Ren, Xiang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to generate a coherent sentence describing an everyday scenario using these concepts (e.g., "a man throws a frisbee and his dog catches it"). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 79k commonsense descriptions over 35k unique concept-sets. Experiments show that there is a large gap between state-of-the-art text generation models (e.g., T5) and human performance. Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context., Comment: Accepted to EMNLP 2020 Findings. Add one more human reference for each test example: Table 1,3 & Figure 4 & Section 3.3, 3.4 are updated. Project page: https://inklab.usc.edu/CommonGen/

Published
2019

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