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Your search keyword '"Hao, Ziqian"' showing total 27 results
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27 results on '"Hao, Ziqian"'

2. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Author

Hou, Yimin, Jia, Shuyue, Lun, Xiangmin, Hao, Ziqian, Shi, Yan, Li, Yang, Zeng, Rui, and Lv, Jinglei

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step towards better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.

Published
2020
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4. Plasma extrachromosomal circular DNA as a potential diagnostic biomarker for nodular thyroid disease.

Author

Zhou, Meng, Lv, Wei, Han, Peng, Sun, Kai, Hao, Ziqian, Gao, Ling, Xu, Yunyun, Xu, Zhe, Shao, Shanshan, Ma, Shizhan, Guo, Qingling, Zhang, Haiqing, Liu, Ke, Yang, Fan, Yuan, Zhongshang, Wu, Guojun, Yu, Changbin, Luo, Yonglun, Yao, Zhenyu, and Zhao, Jiajun

Subjects
NODULAR disease, CIRCULAR DNA, EXTRACHROMOSOMAL DNA, THYROID diseases, BIOMARKERS, CONVOLUTIONAL neural networks, CIRCULATING tumor DNA
Abstract

This article explores the potential of plasma extrachromosomal circular DNA (eccDNA) as a diagnostic biomarker for distinguishing between benign and malignant thyroid nodules, specifically papillary thyroid carcinoma (PTC). The study utilizes Circle-seq technology and machine learning to analyze eccDNA profiles in plasma samples from healthy controls, nodular thyroid goitre patients, and PTC patients. The results show significant differences in eccDNA counts and genomic characteristics between the groups, suggesting the potential of eccDNA as a non-invasive biomarker for diagnosing thyroid cancer. However, the study acknowledges the limitation of a small sample size and the need for further research to validate the findings. [Extracted from the article]

Published
2024
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5. Monolayer Organic Crystals for Ultrahigh Performance Molecular Diodes

Author

Li, Yating, primary, Xie, Jiacheng, additional, Sun, Li, additional, Zeng, Junpeng, additional, Zhou, Liqi, additional, Hao, Ziqian, additional, Pan, Lijia, additional, Ye, Jiandong, additional, Wang, Peng, additional, Li, Yun, additional, Xu, Jian‐Bin, additional, Shi, Yi, additional, Wang, Xinran, additional, and He, Daowei, additional

Published
2023
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7. Monolayer Organic Crystals for Ultrahigh Performance Molecular Diodes.

Author

Li, Yating, Xie, Jiacheng, Sun, Li, Zeng, Junpeng, Zhou, Liqi, Hao, Ziqian, Pan, Lijia, Ye, Jiandong, Wang, Peng, Li, Yun, Xu, Jian‐Bin, Shi, Yi, Wang, Xinran, and He, Daowei

Subjects
DIODES, MONOMOLECULAR films, MOLECULAR crystals, ELECTRIC breakdown, CLASS A metals, SEMICONDUCTOR lasers, ORGANIC semiconductors
Abstract

Molecular diodes are of considerable interest for the increasing technical demands of device miniaturization. However, the molecular diode performance remains contact‐limited, which represents a major challenge for the advancement of rectification ratio and conductance. Here, it is demonstrated that high‐quality ultrathin organic semiconductors can be grown on several classes of metal substrates via solution‐shearing epitaxy, with a well‐controlled number of layers and monolayer single crystal over 1 mm. The crystals are atomically smooth and pinhole‐free, providing a native interface for high‐performance monolayer molecular diodes. As a result, the monolayer molecular diodes show record‐high rectification ratio up to 5 × 108, ideality factor close to unity, aggressive unit conductance over 103 S cm−2, ultrahigh breakdown electric field, excellent electrical stability, and well‐defined contact interface. Large‐area monolayer molecular diode arrays with 100% yield and excellent uniformity in the diode metrics are further fabricated. These results suggest that monolayer molecular crystals have great potential to build reliable, high‐performance molecular diodes and deeply understand their intrinsic electronic behavior. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Inkjet‐Printed, Wafer‐Scale Organic Schottky‐Gate Transistors toward Single‐Battery‐Driven Integrated Logic.

Author

Hao, Ziqian, Qian, Jun, Li, Yating, Dai, Qinyong, Li, Longfei, Xie, Jiacheng, Sun, Li, Wang, Xiaomu, Wang, Xinran, Shi, Yi, and Li, Yun

Subjects
SEQUENTIAL circuits, ORGANIC field-effect transistors, CHARGE injection, ORGANIC electronics, POWER resources, TRANSISTORS
Abstract

Organic field‐effect transistors switched by insulated gates are the most essential building blocks, while usually plagued with degraded gate control arising from complicated dielectric engineering. Subsequently, the resulting large supply voltage and power consumption remain an essential issue for portable electronics driven by a single battery of only 1.5 V. Herein, wafer‐scale organic Schottky‐gate transistor arrays using inkjet‐printed few‐layer organic semiconducting crystals are reported. The transistors exhibit steep switching characteristics with an average subthreshold swing of 55 mV per dec and high signal amplification efficiency over 45 S A‐1, attributed to efficient Schottky gating and enhanced charge injection. Thereafter, high‐gain inverters are successfully demonstrated with an ultralow power consumption of only 800 pW; also, they are integrated as 1 V driven sequential logic circuits. A coplanar double‐gate geometry is also introduced for low‐voltage, single‐device AND logic. Therefore, the work opens new avenues toward the sustainable advancement in single‐battery‐driven, ultralow‐power organic electronics. [ABSTRACT FROM AUTHOR]

Published
2024
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13. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

Author

Hou, Yimin, Jia, Shuyue, Lun, Xiangmin, Hao, Ziqian, Shi, Yan, Li, Yang, Zeng, Rui, and Lv, Jinglei

Abstract

Toward the development of effective and efficient brain–computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson’s matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

Published
2024
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15. Monolayer molecular diode

Author

Li, Yating, primary, Xie, Jiacheng, additional, Sun, Li, additional, Zeng, Junpeng, additional, Zhou, Liqi, additional, Hao, Ziqian, additional, Pan, Lijia, additional, Ye, Jiandong, additional, Wang, Peng, additional, Shi, Yi, additional, Xu, Jian-Bin, additional, Li, Yun, additional, Wang, Xinran, additional, and He, Daowei, additional

Published
2022
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17. Molecular-Layer-Defined Asymmetric Schottky Contacts in Organic Planar Diodes for Self-Powered Optoelectronic Synapses

Author

Wang, Hengyuan, primary, Jiang, Sai, additional, Hao, Ziqian, additional, Xu, Xin, additional, Pei, Mengjiao, additional, Guo, Jianhang, additional, Wang, Qijing, additional, Li, Yating, additional, Chen, Jiaming, additional, Xu, Jun, additional, Wang, Xinran, additional, Wang, Junzhuan, additional, Shi, Yi, additional, and Li, Yun, additional

Published
2022
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19. Retina‐Inspired Self‐Powered Artificial Optoelectronic Synapses with Selective Detection in Organic Asymmetric Heterojunctions

Author

Hao, Ziqian, primary, Wang, Hengyuan, additional, Jiang, Sai, additional, Qian, Jun, additional, Xu, Xin, additional, Li, Yating, additional, Pei, Mengjiao, additional, Zhang, Bowen, additional, Guo, Jianhang, additional, Zhao, Huijuan, additional, Chen, Jiaming, additional, Tong, Yunfang, additional, Wang, Jianpu, additional, Wang, Xinran, additional, Shi, Yi, additional, and Li, Yun, additional

Published
2022
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24. Big data reveals fewer recombination hotspots than expected in human genome

Author

Hao, Ziqian and Li, Haipeng

Abstract

Recombination is a major force that shapes genetic diversity. The inference accuracy of recombination rate is important and can be improved by increasing sample size. However, it has never been investigated whether sample size affects the distribution of inferred recombination activity along the genome, and the inference of recombination hotspots. In this study, we applied an artificial intelligence approach to estimate recombination rates in the UK10K human genomic data set with 7,562 genomes and in the OMNI CEU data set with 170 genomes. We found that the fluctuation of local recombination rate along the UK10K genomes is much smaller than that along the CEU genomes, and recombination activity in the UK10K genomes is also much less concentrated. The same phenomena were also observed when comparing UK10K with its two subsets with 200 and 400 genomes. In all cases, analyses of a larger number of genomes result in a more precise estimation of recombination rate and a less concentrated recombination activity with fewer recombination hotpots identified. Generally, UK10K recombination hotspots are about 2.93-14.25 times fewer than that identified in previous studies. By comparing the recombination hotspots of UK10K and its subsets, we found that the false inference of population-specific recombination hotspots could be as high as 75.86% if the number of sampled genomes is not super large. The results suggest that the uncertainty of estimated recombination rate is substantial when sample size is not super large, and more attention should be paid to accurate identification of recombination hotspots, especially population-specific recombination hotspots. Author summary We applied FastEPRR, an artificial intelligence method to estimate recombination rates in the UK10K data set with 7,562 genomes and established the most accurate human genetic map. By comparing with other human genetic maps, we found that analyses of a larger number of genomes result in a more precise estimation of recombination rate and a less concentrated recombination activity with fewer recombination hotpots identified. The false inference of population-specific recombination hotspots could be substantial if the number of sampled genomes is not super large.

Published
2019
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27. eGPS 1.0: comprehensive software for multi-omic and evolutionary analyses

Author

Yu, Dalang, primary, Dong, Lili, additional, Yan, Fangqi, additional, Mu, Hailong, additional, Tang, Bixia, additional, Yang, Xiao, additional, Zeng, Tao, additional, Zhou, Qing, additional, Gao, Feng, additional, Wang, Zhonghuang, additional, Hao, Ziqian, additional, Kang, Hongen, additional, Zheng, Yi, additional, Huang, Hongwei, additional, Wei, Yuzhang, additional, Pan, Wei, additional, Xu, Yaochen, additional, Zhu, Junwei, additional, Zhao, Shilei, additional, Wang, Ciran, additional, Wang, Pengyu, additional, Dai, Long, additional, Li, Mushan, additional, Lan, Li, additional, Wang, Yiwei, additional, Chen, Hua, additional, Li, Yi-Xue, additional, Fu, Yun-Xin, additional, Shao, Zhen, additional, Bao, Yiming, additional, Zhao, Fangqing, additional, Chen, Luo-Nan, additional, Zhang, Guo-Qing, additional, Zhao, Wenming, additional, and Li, Haipeng, additional

Published
2019
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