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Daily exposure to low concentrations Tetrabromobisphenol A interferes with the thyroid hormone pathway in HepG2 cells

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Fundamental Research 2023年第3期3卷 384-391页

作者： Taicheng An lirong lu Guiying Li Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health Guangdong Key Laboratory of Environmental Catalysis and Health Risk ControlInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control Key Laboratory of City Cluster Environmental Safety and Green Development of the Ministry of EducationSchool of Environmental Science and EngineeringGuangdong University of TechnologyGuangzhou 510006China

Tetrabromobisphenol A(TBBPA)is a flame retardant that adversely affects the environment and human *** present study exposed HepG2 cells to low concentrations of TBBPA daily to investigate the changes in gene regulation,mainly related to pathways associated with the endocrine *** quantitative polymerase chain reaction(qPCR)confirmed that prolonged exposure gradually activated the thyroid hormone and parathyroid hormone signaling *** expression levels of genes related to the thyroid hormone signaling pathway were upregulated(1.15-8.54 times)after five generations of exposure to 1 and 81 nM ***,co-exposure to 81 nM TBBPA and 0.5 nM thyroid hormone receptor antagonist for five generations significantly reduced the expression of thyroid hormone and parathyroid hormone ***,81 nM TBBPA inhibited the activation of the Ras pathway and downregulated Ras gene expression level(3.7 times),indicating the association between the toxic effect and thyroid hormone ***,our experiments revealed that the thyroid hormone pathway regulated the induction of the Ras signaling pathway by *** study thus proves that daily exposure to TBBPA interferes with the thyroid hormone signaling pathway and subsequently the endocrine system.

关键词： TBBPA Low-concentration exposure Endocrine system Thyroid hormone receptor Ras signaling pathway

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基于心率变异性的阵发性心房颤动预测方法

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中国医学物理学杂志 2024年第5期41卷 579-587页

作者：牛晓东柴国强王大为卢莉蓉韩玲娜连亚军长治医学院生物医学工程系山西长治046000 长治医学院山西省智能数据辅助诊疗工程研究中心山西长治046000 山西师范大学物理与信息工程学院山西太原030000 长治医学院生理学教研室山西长治046000 长治医学院附属和平医院全科医疗科山西长治046000

基于心率变异性(HRV)的特征分析,提出一种患者阵发性房颤(PAF)发作的预测系统方法。首先,基于一种新的自适应滤波技术逐次平滑滤波并粗粒化HRV后,采用熵量化HRV在多个自适应尺度的复杂性特征;其次,特征经MinMax归一化和序列前向选择特... 详细信息

基于心率变异性(HRV)的特征分析,提出一种患者阵发性房颤(PAF)发作的预测系统方法。首先,基于一种新的自适应滤波技术逐次平滑滤波并粗粒化HRV后,采用熵量化HRV在多个自适应尺度的复杂性特征;其次,特征经MinMax归一化和序列前向选择特征子集,输入支持向量机识别HRV类型,预测PAF发作。经50例时长5 min HRV序列集的五折交叉验证,得到最优预测结果为:准确率98%,敏感性100%,特异性96%,性能表现优越。另外,实验表明远离和紧随PAF时的HRV复杂性特征值在不同频率段内,分别具有不同的显著变化(P<0.05),反映受试者神经系统调节心脏节律改变,以及调控机体、应激等适应外界环境变化能力的下降。

关键词：阵发性房颤心率变异性熵尺度积分均值模式分解

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PAL-BERT:An Improved Question Answering Model

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2729-2745页

作者： Wenfeng Zheng Siyu lu Zhuohang Cai Ruiyang Wang Lei Wang lirong Yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant ***,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has ***,model training has become more costly and less *** enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language ***,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is ***,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of Relu to improve the ***,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training *** with traditional models,PAL-BERT significantly improves the NLP task’s performance.

关键词： PAL-BERT question answering model pretraining language models ALBERT pruning model network pruning TextCNN BiLSTM

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Self-assembled aerogel sheet electrodes of thermocells for low-grade heat harvest

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Science China Chemistry 2024年第5期67卷 1672-1679页

作者： Ziwei Mo Shouhao Wei Daibin Xie Kaihua Zhu Hongxiong Li Xin lu lirong Liang Chunyu Du Zhuoxin Liu Guangming Chen College of Materials Science and Engineering Shenzhen UniversityShenzhen 518055China State Key Laboratory of Polymer Materials Engineering Sichuan UniversityChengdu 610065China

Efficiently harvesting low-grade heat is crucial for sustainable energy management. Thermocells(TECs), inducing heat-toelectricity conversion via the thermogalvanic effect, have thus drawn tremendous attention in recent years. This study introduces a self-assembly approach for fabricating aerogel sheet electrodes(ASEs) tailored for TECs. The crafted ASEs retain a remarkable porous architecture with approximately 95% porosity, even with their slimmed-down thickness. Results reveal that the electrode composition has minimal influence on the thermopower of TECs. Notably, the porous ASE with tunned composition demonstrates an optimal effective surface area for the thermogalvanic effect, resulting in enhanced output current density. This balances the desirable traits of electrode compactness with abundant redox active sites, positioning it favorably against conventional bulky electrode designs. The TECs utilizing the optimized ASE achieve a peak output power of 22.10 μW cm^(-2)under a temperature difference of 30 K. Furthermore, a tubular TEC device is readily assembled and specially designed for harvesting heat energy from hot fluids. These findings underscore the potential of composite electrodes in the realm of low-grade heat harvest, paving the way for broader applications in sustainable energy solutions.

关键词： thermocells low-grade heat harvest energy conversion composite electrodes

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Causal relationship between circulating vitamin C and 25-hydroxyvitamin D concentrations and common mental disorders-a Mendelian randomization study

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Food Science and Human Wellness 2024年第4期13卷 1877-1885页

作者： Yuanyuan Wang lirong Dong Meng Zhao Shiyu Yin Pan Da Dengfeng Xu Yifei lu Jiayue Xia Niannian Wang Shaokang Wang Guiju Sun Key Laboratory of Environmental Medicine and Engineering of Ministry of Education and Department of Nutrition and Food HygieneSchool of Public HealthSoutheast UniversityNanjing 210009China Department of Integrated Service and Management Jiangsu Provincial Center for Disease Control and PreventionNanjing 210009China

Mental disorders seriously affect people’s health and social *** Mendelian randomization(MR)study was designed to investigate the causal relationship between circulating vitamin C(VC)or 25-hydroxyvitamin D(25(OH)D)levels and mental *** data used for the MR analysis were derived from the summary genome-wide association studies(GWAS)database for VC and 25(OH)D and from the Finn Gen consortium for fourteen mental *** on the inverse variance weighted(IVW)method,we found a potential causal association between circulating VC and anxiety disorders(IVW:OR=1.139,95%CI:1.023-1.269,P=0.018).However,no causal association was found between VC or 25(OH)D and other mental disorders(P>0.05).In the reverse MR analysis,individuals with Alzheimer’s disease was causally associated with higher concentrations of circulating VC(P=0.012),while individuals with anxiety disorders had a negative association between the concentrations of 25(OH)D(P=0.012).However,the current evidence does not support a causal relationship between VC or 25(OH)D and other mental *** addition,there was no causal association between circulating VC and 25(OH)D(P>0.05).Future studies are needed to confirm these findings and to elucidate the mechanisms of potential causality.

关键词： Circulating vitamin C 25-Hydroxyvitamin D Mental disorder Mendelian randomization

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AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2315-2347页

作者： lirong Yin Lei Wang Siyu lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： Super-resolution feature extraction dynamic convolution attention mechanism gate control

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DPAL-BERT: A Faster and Lighter Question Answering Model

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2024年第10期141卷 771-786页

作者： Yin, lirong Wang, Lei Cai, Zhuohang lu, Siyu Wang, Ruiyang Alsanad, Ahmed Alqahtani, Salman A. Chen, Xiaobing Yin, Zhengtong Li, Xiaolu Zheng, Wenfeng Louisiana State Univ Dept Geog & Anthropol Baton Rouge LA 70803 USA Univ Elect Sci & Technol China Sch Automat Chengdu 610054 Peoples R China King Saud Univ Coll Comp & Informat Sci Riyadh 11574 Saudi Arabia Louisiana State Univ Sch Elect & Comp Engn Baton Rouge LA 70803 USA Guizhou Univ Coll Resources & Environm Engn Guiyang 550025 Peoples R China Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERTC. This research enhances the dataset through techniques such as masking, replacement, and n-gram sampling to optimize knowledge transfer. The experimental results showed that the distilled models greatly outperform models trained from scratch. In addition, although the distilled models exhibit a slight decrease in performance compared to PAL-BERT, they significantly reduce inference time to just 0.25% of the original. This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory (BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing NETWORK

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Convolution-Transformer for Image Feature Extraction

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2024年第1期141卷 87-106页

作者： Yin, lirong Wang, Lei lu, Siyu Wang, Ruiyang Yang, Youshuai Yang, Bo Liu, Shan Alsanad, Ahmed Alqahtani, Salman A. Yin, Zhengtong Li, Xiaolu Chen, Xiaobing Zheng, Wenfeng Louisiana State Univ Dept Geog & Anthropol Baton Rouge LA 70803 USA Univ Elect Sci & Technol China Sch Automat Chengdu 610054 Peoples R China King Saud Univ Coll Comp & Informat Sci Riyadh 11574 Saudi Arabia Guizhou Univ Coll Resources & Environm Engn Key Lab Karst Georesources & Environm Minist Educ Guiyang 550025 Peoples R China Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer framework. Additionally, we implement a lightweight convolution module to process the input images, resulting in faster convergence and improved stability. This results in an efficient convolution combined Transformer image feature extraction network. Experimental results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational speed. It achieves up to 85.0% accuracy across various model sizes on image classification, outperforming various baseline models. When integrated into the Mask RegionConvolutional Neural Network (R-CNN) framework as a backbone network, CEFormer outperforms other models and achieves the highest mean Average Precision (mAP) scores. This research presents a significant advancement in Transformer-based image feature extraction, balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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Facile electrochemical surface-alloying and etching of Au wires to enable high-performance substrates for surface enhanced Raman scattering

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Nano Materials Science 2024年第3期6卷 305-311页

作者： Yawen Zhan Guobin Zhang Junda Shen Binbin Zhou Chenghao Zhao Junmei Guo Ming Wen Zhilong Tan lirong Zheng Jian lu Yang Yang Li Center of Super-Diamond and Advanced Films(COSDAF) City University of Hong Kong83 Tat Chee AvenueKowloonHong KongChina Department of Materials Science and Engineering City University of Hong Kong83 Tat Chee AvenueKowloonHong KongChina Kunming Institute of Precious Metals Sino-platinum Metals Co.Ltd.KunmingYunan ProvinceChina Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research Institute8 Yuexing 1st RoadShenzhen Hi-Tech Industrial ParkNanshan DistrictShenzhenChina Shenzhen Key Laboratory on Power Battery Safety Research and Shenzhen Geim Graphene Center Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Institute of High Energy Physics Beijing100049China

Surface-enhanced Raman Spectroscopy(SERS)is a nondestructive technique for rapid detection of analytes even at the single-molecule ***,highly sensitive and reliable SERS substrates are mostly fabricated with complex nanofabrication techniques,greatly restricting their practical applications.A convenient electrochemical method for transforming the surface of commercial gold wires/foils into silver-alloyed nanostructures is demonstrated in this *** substrates are treated with repetitive anodic and cathodic bias in an electrolyte of thiourea,in a one-pot one-step manner.X-rays absorption fine structure(XAFS)spectroscopy confirms that the AuAg alloy is induced at the *** unique AuAg alloyed surface nanostructures are particularly advantageous when served as SERS substrates,enabling a remarkably sensitive detection of Rhodamine B(a detection limit of 10^(-14)M,and uniform strong response throughout the substrates at 10^(-12)M).

关键词： Electrodeposition Dealloying Surface-alloyed Noble metals Surface enhanced Raman spectroscopy substrates

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The Origin of Bedded Cherts of the Early Permian Gufeng Formation in the Lower Yangtze Area, Eastern China

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Acta Geologica Sinica(English Edition) 1995年第4期69卷 372-386,447页

作者： Xia Bangdong, Zhong lirong, Fang Zhong and lu HongboDepartment of Earth Sciences, Nanjing University, Nanjing, Jiangsu Fei Zhenbi Department of Earth Sciences Nanjing University Nanjing Jiangsu

Bedded cherts occurring in the Early Permian Gufeng Formation in the Lower Yangtze region, eastern China, are nearly 20-80 m in thickness and contain varying amounts of radiolarians and sponge spicules. There are three types of section for the Gufeng Formation: chert, carbonate and chert-carbonate mixing types. Bedded cherts mainly occur in the first and third types of section. The depocentres of silica are marked by some small (not larger than a few thousand square kilometres in area) rhombic or elliptic hollows and their formation was controlled by faults. Argillaceous volcanic rocks in cherts occur as numerous thin layers. Bedded cherts are characterized by higher Fe and lower Al contents, enrichment in such trace elements as As, Sb, Bi, Ga, Au, Ag and Cr, lower total REE abundance, negative Ce anomaly and varying degrees of HREE enrichment. These characteristics are important evidence for hydrothermal cherts. Minor amounts of substances of non-hydrothermal origin are mixed in the cherts. The temperatures of sea water range approximately from a few tens of degrees to over 160℃. during the deposition of siliceous sediments.

关键词： hydrothermal cherts argillaceous volcanic rocks radiolarian

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