Hui Yang

University of Electronic Science and Technology of China

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Hao Lin

University of Electronic Science and Technology of China

Hui Ding

University of Electronic Science and Technology of China

Wei Chen

North China University of Science and Technology

Hao Lv

Nanchang University

Fanny Dao

Nanyang Technological University

Wei Su

Chinese People's Armed Police General Hospital

Ming Tang

Southwest Petroleum University

Pengmian Feng

Chengdu University of Traditional Chinese Medicine

Kejun Deng

University of Electronic Science and Technology of China

Dan Yan

Beijing Friendship Hospital

Cooperative Institutions

University of Electronic Science and Technology of China

Capital Medical University

Chinese Academy of Sciences

Sichuan University

Sun Yat-sen University

Shanghai Jiao Tong University

University of Chinese Academy of Sciences

San Francisco State University

Fudan University

Chinese Academy of Medical Sciences & Peking Union Medical College

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Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Information Fusion (2021)

Hui Yang Yamei Luo Xiaolei Ren Ming Wu Xiaolin He

Medical record

Diabetes management

10.1016/j.inffus.2021.02.015

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Citations (150)

Analyzing Computer Science Students' Performance Data to Identify ImpactfulCurricular Changes

2021 IEEE Frontiers in Education Conference (FIE) (2021)

Hui Yang Thomas Olson Arno Puder

This full research paper presents a systematic analysis of 10 years' student performance data of Computer Science (CS) majors at San Francisco State University, a public 4-year degree-granting university, aiming to address the ongoing challenges of early dropouts and low graduation rate. The main objective is two-fold: (1) gain a comprehensive understanding of how the existing curriculum has been supporting (or hindering) students' progress towards graduation; and (2) suggest data-informed curricular changes. To this end, we utilize both explorative statistical analysis and data mining/machine learning approaches to first learn how individual courses and the prescribed course sequences influence a student's dropout/graduation status, and then build machine learning models to interpret/validate the observed interdependency among key courses in the current curriculum. Such patterns/models are consequently utilized to suggest impactful curricular changes towards reducing early dropouts and improving the overall student success as measured by graduation with a CS degree. One main finding of this research is that a successful CS student needs to excel in both critical thinking and core CS skills. To help students gain critical thinking skills, it is essential to strengthen the presence of mathematics and physics courses in the CS curriculum. Furthermore, our results suggest that CS students without a solid math foundation before starting their college career should complete a remedial math course earlier than putting it off for later. Moreover, before students advance to the second half of their CS study to gain core CS knowledge/skills (e.g., operating systems), they should complete the required physics class. Finally, we observe that it is necessary to introduce new prerequisite requirements among upper-level CS courses, for example, Operating Systems as a prerequisite to an upper-level CS core course on programming theories.

Graduation (instrument)

Remedial education

Dropout (neural networks)

Common core

10.1109/fie49875.2021.9637474

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Citations (4)

Investigation of mosquito populations and evaluation of control effects in Yuebing village, Shahe, Beijing.

Zhongguo meijie shengwuxue ji kongzhi zazhi (2010)

Deng Bing Hui Yang Jing Ma Ji YuChuan Li SenLin

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Citations (0)

Predicting coronary heart disease in Chinese diabetics using machine learning

Computers in Biology and Medicine (2024)

Cai-Yi Ma Yamei Luo Tianyu Zhang Yu-Duo Hao Xue-Qin Xie

Stroke

10.1016/j.compbiomed.2024.107952

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Citations (12)

Effect of Fast-Track Surgery Nursing Intervention Combined with Probiotic Nutrition Therapy on Children During the Peri-Operative Period of Thoracoscopic Lobectomy

Journal of Biobased Materials and Bioenergy (2023)

Wen Tang Liqing Song Hui Yang

This study aims to investigate the feasibility and efficacy of fast-track surgery (FTS) nursing intervention and probiotic nutrition therapy in children during the peri-operative period of precise thoracoscopic lobectomy. A retrospective analysis was conducted on a total of 479 children who underwent the surgery at the pediatric department of the West China Hospital, Sichuan University between January 2011 and January 2018. The patients were divided into two groups: the traditional rehabilitation group (A, n = 78) and the rapid rehabilitation group with FTS and probiotic nutrition therapy (B, n = 401). No significant differences were observed in general information and the incidence of postoperative complications ( P >0.05). However, the B group had significantly reduced early out-of-bed activity time, early resumption of oral feeding time, intubation duration, and length of hospital stay ( P <0.05). Furthermore, patients in the B group had higher nutritional indexes compared to those in the A group. The levels of Bifidobacterium and Lactobacillus in the B group were higher, while the levels of Enterobacterium and Enterococcus were lower than those in the A group. Additionally, the patients and their families in the B group exhibited higher levels of nursing satisfaction ( P <0.05). The combination of FTS nursing intervention and probiotic nutrition therapy improved peri-operative outcomes without increasing the incidence of complications. Therefore, our findings support the feasibility, safety, and efficacy of thoracoscopic lobectomy with FTS care in pediatric patients, highlighting its potential as a promising surgical intervention.

Group B

Fast track

10.1166/jbmb.2023.2322

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Citations (0)

[Rapid detection of rifampin- and isoniazid-resistant Mycobacterium tuberculosis using real-time PCR and melting curve analysis].

PubMed (2011)

Feng Wang Yun-yong Cui Si-yu Hu Jing Gui Hui Yang

To evaluate the application of a real-time PCR and melting curve analysis assay for rapid detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis (M. tuberculosis).A total of 311 clinical isolates of M. tuberculosis obtained from Shenzhen Center for Chronic Disease Control (SZCCC) were included in the study. These isolates were collected originally from national baseline survey on drug-resistant tuberculosis, project for drug resistance surveillance in Shenzhen and clinical patients in SZCCC between 2007 and 2009. rpoB gene resistance-determining region, ahpC promoter (-44 to -30 and -15 to -3), inhA promoter (-17 to -8), inhA 94 and katG 315 were detected by melting curve analysis after real-time PCR, and the results were compared with that of proportion method and DNA sequencing. The performances of the assay in detecting the resistance of rifampin and isoniazid were compared to that of reference proportion method drug susceptibility test.Real-time PCR and melting analysis was a closed-tube assay that could be completed within 2 - 3 h. Compared to the results of the proportion method, the sensitivity, specificity and accuracy of the assay for rifampin resistance were 97.8%, 97.1% and 97.4% respectively, and for isoniazid resistance were 86.6%, 98.7% and 92.6% respectively.Real-time PCR and melting analysis is a rapid, accurate and closed-tube method that can be used as a screening test for rapid identification of Multidrug-resistant tuberculosis.

INHA

rpoB

Melting curve analysis

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Citations (2)

Genome-Wide Identification and Characterization of the Aux/IAA Gene Family in Strawberry Species

Plants (2024)

Xiaotong Jing Quan Zou Hui Yang

Auxin is the first plant hormone found to play a dominant role in fruit growth, from fruit set to fruit ripening. Strawberry plants represent a suitable model for studying auxin's biosynthesis, sensing, and signaling machinery.

Plant hormone

Identification

Fragaria

10.3390/plants13202940

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Citations (0)

Exploring provincial sustainable intensification of cultivated land use in China: An empirical study based on emergy analysis

Physics and Chemistry of the Earth Parts A/B/C (2022)

Hui Yang Ligen Chen Bin Yang Zhikuan Shi

Emergy

Cultivated land

10.1016/j.pce.2022.103287

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Citations (6)

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features

Genomics (2020)

Yuhe R. Yang Chi Ma Jia-Shu Wang Hui Yang Hui Ding

Sequence (biology)

10.1016/j.ygeno.2020.07.035

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Citations (27)

iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens

Journal of Computational Biology (2018)

Hui Yang Hao Lv Hui Ding Wei Chen Hao Lin

2′-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2′-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2′-O-methylation sites. In this study, based on the experimental 2′-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2′-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.

Homo sapiens

Sequence (biology)

10.1089/cmb.2018.0004

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Citations (139)