作者机构：
[Isa, Mohd Hafizal Mohd; Isa, MHM; He, Danqiu; He, DQ] Univ Sains Malaysia, Sch Housing Bldg & Planning, George Town 11800, Malaysia.;[He, Danqiu] Univ South China, Solux Coll Architecture & Design, Hengyang 421001, Peoples R China.
会议名称：
6th International Conference on Architecture and Civil Engineering (ICACE)
会议时间：
AUG 18, 2022
会议地点：
Kuala Lumpur, MALAYSIA
会议主办单位：
[He, Danqiu;Isa, Mohd Hafizal Mohd] Univ Sains Malaysia, Sch Housing Bldg & Planning, George Town 11800, Malaysia.^[He, Danqiu] Univ South China, Solux Coll Architecture & Design, Hengyang 421001, Peoples R China.
会议论文集名称：
Lecture Notes in Civil Engineering
关键词：
MPCM;Building wall;Energy saving;Sustainable development
摘要：
Many researchers have confirmed that applying phase change material (PCM) thermal energy storage technology to building walls can effectively solve the problem of building energy consumption, but there are still many shortcomings. For example, leakage is easy to occur in the process of material compounding. Therefore, this problem can be solved by using microcapsule technology. With the use of microencapsulated phase change material (MPCM) and building materials composites, PCM is encapsulated in microcapsules, which can effectively solve problems such as leakage, so that PCM can be fully used in building walls. This paper reviews the basic characteristics, preparation technology, and thermal properties of MPCM and focuses on the application of MPCM in walls after compounding with several building materials. The influence of this composite material on building energysaving was explored, hoping to provide more ideas for the sustainable development of future buildings.
摘要：
The task of Knowledge Graph Completion (KGC) entails inferring missing relations and facts in a partially specified graph to discover new knowledge. However, the discrepancy in the targets between the training and inference phases might lead to in-depth bias and in-breadth bias during inference, potentially resulting in incorrect outcomes. In this work, we conduct a comprehensive analysis of these biases to determine their extent of impact. To mitigate these biases, we propose a novel debiasing framework called Causal Inference-based Debiasing Framework for KGC (CIDF) by formulating a causal graph and utilizing it for causal analysis of KGC tasks. The framework incorporates In-Depth Bias Mitigation to diminish the bias on feature representations by measuring the bias during inference, and In-Breadth Bias Mitigation to increase the distinguishability between feature representations by introducing a novel loss function. We evaluate the effectiveness of our proposed method on four benchmark datasets - WN18RR, FB15k-237, Wikidata5M-Trans, and Wikidata5M-Ind, achieving improvements of 2.5%, 0.9%, 3.2%, and 1.5% on Hit@1 respectively. Our results demonstrate that CIDF leads to significant improvements on these datasets, with more substantial gains observed in the biased settings on WN18RR achieving a 3.4% improvement in Hit@1.
作者机构：
[Li Y.; Hu X.] School of Computer Science, University of South China, Hengyang, 421001, China;[Lin W.] School of Mathematics and Physics, University of South China, Hengyang, 421001, China
会议名称：
9th International Forum on Digital Multimedia Communication, IFTC 2022
期刊：
E3S Web of Conferences,2023年393:01010-null ISSN：2267-1242
通讯作者：
Wang, J.
作者机构：
[Zheng J.; Dai Z.; Wang J.; Jiang W.] College of Civil Engineering, University of South China, Hunan, Hengyang, China;[Zhang Z.; Wang J.; Chen S.] South China Institute of Environmental Science, Mep, Guangdong, Guangzhou, China
通讯机构：
[Wang, J.] C;College of Civil Engineering, Hunan, China
会议名称：
5th International Conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2023
作者机构：
[Liu, Jing; Hu, Bin; Gong, Han; Yang, Chaoying; Liang, Long] Cent South Univ, Mol Biol Res Ctr, Hunan Prov Key Lab Basic & Appl Hematol, Dept Hematol,Xiangya Hosp 2,Sch Life Sci, Changsha, Peoples R China.;[Nie, Ling] Cent South Univ, Xiangya Hosp, Dept Hematol, Changsha, Peoples R China.;[Zhang, Ji] Univ South China, Dept Clin Lab Med, Inst Microbiol & Infect Dis, Affiliated Hosp 1,Hengyang Med Sch, Hengyang, Peoples R China.;[Narla, Mohandas] New York Blood Ctr, Res Lab Red Cell Physiol, New York, NY USA.;[Sheng, Yue] Cent South Univ, Xiangya Hosp 2, Dept Hematol, Changsha, Peoples R China.
会议名称：
65th Annual Meeting of the American-Society-of-Hematology (ASH)
会议时间：
DEC 09-12, 2023
会议地点：
San Diego, CA
摘要：
Lysine succinylation has emerged as a recently discovered protein modification that significantly impacts the chemical environment and exhibits diverse functions in various biological processes. However, the specific role of lysine succinylation in erythropoiesis has not been fully elucidated. In this study, we investigated the levels of six common acylations (acetylation, crotonylation, succinylation, propionylation, butyrylation, and malonylation) in human erythroid cells. Interestingly, we observed a prominent accumulation of lysine succinylation during human erythroid differentiation, suggesting its potential importance in this process. To explore the functional significance of succinylation, we inhibited succinylation in human erythroid progenitor cell line by disrupting the expression of the key succinyltransferases and desuccinylases. The results revealed that succinylation inhibition led to suppressed cell proliferation, increased apoptosis, and disrupted differentiation, indicating the essential role of succinylation in erythropoiesis. Furthermore, integrative proteome and succinylome analysis identifies 939 quantifiable proteins with 2,871 Ksu sites. Notably, we observed inconsistencies between alterations in protein levels and succinylation levels, suggesting that the role of succinylation in proteins' function regulation. These succinylated proteins are widely distributed in various cellular compartments and involved in multiple cell processes, indicating that succinylation is a prevalent modification in erythropoiesis. Mechanically, we identified CYCS as a key target of succinylation during erythropoiesis, emphasizing its essential role in this process. Specially, we implicated KAT2A-mediated histone succinylation in chromatin remodeling, further highlighting the regulatory significance of lysine succinylation in erythropoiesis at the epigenetic level. Collectively, our comprehensive investigation of the succinylation landscape during erythropoiesis provides valuable insights into its regulatory role and offer potential implications for erythroid-related diseases and therapeutic strategies.
作者机构：
[Liu, Yong] Univ South China, Coll Elect Engn, Hengyang 421001, Peoples R China.;[Hu, Ji-wen; Xie, Ya-qian; Liu, Yong] Univ South China, Coll Math & Phys, Hengyang 421001, Peoples R China.
会议名称：
Conference on Biophysical-Society-of-GuangDong-Province-Academic-Forum - Precise Photons and Life Health (PPLH)
会议时间：
DEC 09-11, 2022
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Liu, Yong] Univ South China, Coll Elect Engn, Hengyang 421001, Peoples R China.^[Liu, Yong;Hu, Ji-wen;Xie, Ya-qian] Univ South China, Coll Math & Phys, Hengyang 421001, Peoples R China.
会议论文集名称：
Proceedings of SPIE
关键词：
Atherosclerosis;Electromagnetic wave;Ablation of heat;Finite element method
摘要：
The purpose of this study is to explore the thermal damage of microwave to atherosclerotic plaques in order to achieve the purpose of treating atherosclerosis. In this paper, a fluid-solid-heat coupling model of thermal ablation of atherosclerotic plaque is established (The coupling model of blood-plaque-electromagnetic wave is studied in this paper, in which the thermal ablation of atherosclerotic plaque means that the electromagnetic wave is used to generate heat, and the temperature of atherosclerotic plaque tissue rises. If the cells in it reach the threshold of death temperature, they will be killed, so as to achieve the purpose of thermal ablation.). The electromagnetic field and bio-thermal equation are solved and analyzed by finite element method. By calculating the temperature and thermal damage distribution of microwave on atherosclerotic plaque, the effect of microwave on thermal ablation of atherosclerotic plaque was evaluated. The results show that the thermal damage degree of atherosclerotic plaque is positively correlated with electromagnetic wave frequency, electromagnetic wave power and heating time. The model shows that electromagnetic wave hyperthermia may provide a new therapeutic mode for thermal ablation of atherosclerotic plaques.
作者机构：
[Xu, X.; Skinner, S.J.] Department of Materials, Imperial College London, Exhibition Road, London;SW7 2AZ, United Kingdom;[Bi, L.] School of Resource Environment and Safety Engineering, University of South China, Hengyang;421001, China;[Xu, X.; Skinner, S.J.] SW7 2AZ, United Kingdom
期刊：
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS,2023年117(2, Supplement):e479-e480 ISSN：0360-3016
通讯作者：
Q. Ni
作者机构：
[Ni, Q.] Cent South Univ, Hunan Canc Hosp, Affiliated Canc Hosp, Dept Radiat Oncol,Xiangya Sch Med, Changsha, Peoples R China.;[Ni, Q.] Univ South China, Sch Nucl Sci & Technol, Hengyang, Peoples R China.
通讯机构：
[Q. Ni] D;Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China<&wdkj&>School of Nuclear Science and Technology, University of South China, Hengyang, China
会议名称：
65th ANNUAL MEETING OF THE AMERICAN-SOCIETY-FOR-RADIATION-ONCOLOGY (ASTRO)
会议时间：
OCT 01-04, 2023
会议地点：
San Diego, CA
摘要：
To establish the different machine learning classification predict models of gamma pass rates for specific dosimetric verification of pelvic intensity modulated radiotherapy plan which based on the radiomic features and to explore the best prediction model.
Retrospective analysis of the 3D dosimetric verification results based on measurements with gamma pass rate criteria of 3%/2 mm and 10% dose threshold of 196 pelvic intensity-modulated radiotherapy plans was carried. Prediction models were established by extracting radiomic features data. Four machine learning algorithms, random forest, support vector machine, adaptive boosting and gradient boosting decision trees, were used to calculate the AUC value, sensitivity and specificity respectively. The classification performance of the four prediction models were evaluated.
The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision trees models were 0.93,0.85,0.93,0.96, and 0.38,0.69,0.46, and 0.46, respectively. The AUC values for the random forest model and the adaptive boosting model were 0.81 and 0.82, respectively, and the AUC values for the support vector machine and gradient boosting decision tree models were 0.87.
Machine learning methods based on radiomics can be used to establish a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity modulated radiotherapy. The classification performance of support vector machine model and gradient boosting decision trees model is better than that of random forest model and adaptive boosting model. The prediction model for a specific site is helpful to improve the performance of the model.
作者机构：
School of Computer Science, University of South China, Hengyang, 421200, China;[Jiang W.; Zhong K.; Wu Z.; Chen Z.; Han W.] Nuclear Power Institute of China, Chengdu, 610000, China;Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan, Hengyang, China;CNNC Key Laboratory on High Trusted Computing, Hunan, Hengyang, China;[Bai Y.; Liu J.] School of Computer Science, University of South China, Hengyang, 421200, China, Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hunan, Hengyang, China
会议名称：
9th International Conference on Energy Engineering and Environmental Engineering, ICEEEE 2022
会议时间：
9 December 2022 through 10 December 2022
关键词：
March X;Memory;Nuclear power;PBIST;Safety level DCS
作者机构：
School of Computer Science, University of South China, Hengyang, 421200, China;[Chen Z.; Jiang W.; Wu Z.; Zhong K.; Zhang Y.] Nuclear Power Institute of China, Chengdu, 610000, China;Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan, Hengyang, China;CNNC Key Laboratory on High Trusted Computing, Hunan, Hengyang, China;[Liu J.; Zhou P.] School of Computer Science, University of South China, Hengyang, 421200, China, Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hunan, Hengyang, China
会议名称：
9th International Conference on Energy Engineering and Environmental Engineering, ICEEEE 2022
会议时间：
9 December 2022 through 10 December 2022
关键词：
Data security;Encryption algorithm;Gateway;Safety grade DCS;Transmission efficiency
会议名称：
11th International Conference on Bioinformatics and Computational Biology (ICBCB)
会议时间：
APR 21-23, 2023
会议地点：
Hangzhou, PEOPLES R CHINA
会议主办单位：
[Xu, Rong;Luo, Lingyun;Liu, Zhiming;Ouyang, Chunping;Wan, Yaping] Univ South China, Sch Comp, Hengyang, Peoples R China.
关键词：
drug-drug interaction;prediction;deep learning;graph contrastive learning;adverse drug events
摘要：
Adverse drug-drug interactions (DDIs) may occur when drugs are combined to treat complex or comorbid diseases, which can result in adverse drug events, injury, and even death. Therefore, accurate prediction of potential DDI events is critical. Recently, automated computational methods such as deep learning are widely used for DDI events prediction. However, most of these methods only consider single information about the drug or rely on a large amount of label data, which easily leads to insufficient robustness and generalization ability. Accordingly, we proposed a novel end-to-end graph contrastive learning model for predicting multi-relational DDI events (CLDDI). It comprehensively considers the rich biomedical information of the Knowledge Graph (KG) and the structural information of the drug network. Specifically, we first generate two graph views by randomly corrupting the original KG, and compute a contrastive loss to maximize the agreement of node representation in these two views. Then we extract the drug embeddings obtained by contrastive learning and aggregate their neighbor information in the multi-relational DDI network. Finally, we combine the contrastive and supervised loss to learn the feature representation of nodes in an end-to-end fashion. Extensive experiments on real datasets show that the performance of CLDDI is competitive with the best baselines. Experimental results on sparse datasets further demonstrate that CLDDI has strong generalization performance and robustness.
