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An Ultra-Sensitive Dual-Signal Ratio Electrochemical Aptamer Biosensor for Detection of Her2

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SSRN

SSRN 2022年

作者： Zhang, Ya Xu, Ying Li, Ning Qi, Na Yang, Mei Peng, Lan Hou, Changjun Huo, Danqun Key Laboratory for Biorheological Science and Technology Ministry of Education Bioengineering College Chongqing University Chongqing 400044 China Chongqing University China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing 400044 China National Facility for Translational Medicine Shanghai Jiao Tong University Shanghai 200240 China

Electrochemical biosensor technology has the characteristics of simple fabrication, low loss, high sensitivity, short consumption time and easy to carry, which is conducive to its rapid application. However, its accuracy and stability still need to be improved. Herein, a dual-signal ratio electrochemical aptamer biosensor, improve detection accuracy due to its self-calibration capability，was constructed to detect breast cancer biomarker - HER2. The dopamine (PDA) and 3,3',5,5' -tetramethylbenzidine (TMB) were selected as electroactive materials, with the ZIF-67 loaded PDA (ZIF-67@PDA) as the capture probe, and bimetal Cu/UiO-66 loaded TMB as the signal identification probe. In the presence of the HER2, ZIF-67@PDA probe trapping HER2 will lead to the weakening of the electrode conductivity to weaken the electrochemical signal of PDA, however, the modification of the signal probe will lead to the enhancement of the electrochemical signal of TMB. By calculating the ratio of the two electrochemical signal, the high sensitivity, specificity and ratio of the target can be achieved. It can sensitive detection of HER2 biomarkers in only 30 min with the detection range are 0.75 to 40 pg/mL and detection limits as low as 4 fg/mL. The dual-signal ratio biosensor has low detection limit, short detection time and simple method, which can accurately detect the target in complex biological samples, and has important theoretical significance. © 2022, The Authors. All rights reserved.

关键词： Probes

Learning data-adaptive non-parametric kernels

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

Hybrid Data-Free Knowledge Distillation

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

Analysis of river morphology change in Zhengzhou reach of the Yellow River based on long time series remote sensing images

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Analysis of river morphology change in Zhengzhou reach of th...

2024 International Conference on Remote Sensing, Mapping, and Geographic information Systems, RSMG 2024

作者： Li, Ziwei Liang, Bingjie Chen, Yi Ge, Wen Deng, Xingran Jianzhong, Guo The College of Geography and Environmental Science Henan University No. 379 North Mingli Road Henan Province Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent processing Ministry of Natural Resources Henan Province Zhengzhou450001 China Yellow River Institute of Hydraulic Research Yellow River Conservancy Commission Zhengzhou450003 China China Construction Eighth Engineering Division Rall Transit Construction Co Ltd Zhongjian Building No.6 Wenlan Road Xianlin University Town Qixia District Jiangsu Province Nanjing City210023 China

ISBN: (纸本)9781510685826

The Zhengzhou reach of the Yellow River is a typical meandering section with frequent changes in river morphology. In this paper, using satellite remote sensing technology and Landsat 8 satellite imagery data, the morphological changes of the Yellow River in Zhengzhou (Taohuayu - Huayuankou) in the lower reaches from 2013 to 2022 were quantitatively studied and analyzed. The overall water surface area of the study segment changes significantly (61.8 square kilometers), while the length remains stable. The variations in the area and length of the river segment exhibit a pattern of phased changes. Regarding the main meanders, the first and third meanders showed a trend of initial strengthening followed by weakening, while the second meander showed an upward trend. The intensity of the river's morphological changes followed a pattern of stability, followed by a period of drastic alteration, and then a return to stability. Overall, the tendency for the river segment in the study area to meander has diminished over the study period. © 2024 SPIE.

关键词： Remote sensing Satellites Satellite imaging Floods Earth observing sensors Feature extraction Landsat Clouds Statistical analysis Roads

Cross-Modality Perturbation Synergy Attack for Person Re-identification

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arXiv 2024年

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024, CC BY-NC-ND.

关键词： Infrared imaging

A Three-Modal Fluorescent Sensor Harnessing Diverse Luminescent Mechanisms for the Purpose of Segmented Baijiu Identification

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SSRN

SSRN 2023年

作者： Hou, Changjun Chen, Lin Mao, Zhenyu Ma, Yi Luo, Huibo Zhang, Suyi Huo, Danqun Chongqing University China Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing400044 China Liquor Making Biology Technology Application of Key Laboratory of Sichuan Province College of Bioengineering Sichuan University of Science and Engineering 188 University Town Yibin644000 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

The classification and verification of segmented Baijiu hold significant importance as they profoundly influence the blending and overall quality of the Baijiu. Our scholarly investigation yielded a fluorescent sensor with three luminescent modes by integrating Tb3+ and RHB into UiO-66. The interplay between carboxyl-containing compounds and RHB/Tb@TLU-2 orchestrates a harmonious molecular association, where the convergence of carboxyl groups with Tb3+ yields a resonating impact on the antenna effect of BDC-SO3-. Furthermore, the acidity and alkalinity of reactants induced a charge transfer interaction between BDC-NH2 and Zr4+ and led to structural changes in RHB/Tb@TLU-2, resulting in observable fluorescence signal variations across the three emission centers. The sensor array successfully identified eight organic acids, achieving an impressive 97.5% accuracy in discerning segmented Baijiu samples from four Baijiu pits. This meticulous methodology prioritizes simplicity, swiftness, and effectiveness, paving the path for comprehensive segmented Baijiu analysis in the esteemed realm of Brewing production. © 2023, The Authors. All rights reserved.

关键词： Terbium compounds

The Microsphere of Sodium Alginate-Chitosan-Pichia Kudriavzevii Enhanced Esterase Activity to Increase the Content of Esters in Baijiu Solid-State Fermentation

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SSRN

SSRN 2022年

作者： Huang, Jiaqing Qiao, Cailin Wang, Xinrou Gao, Yuwei Zhao, Jinsong Luo, Huibo Wang, Yongzhong Huo, Danqun Hou, Changjun Key Laboratory for Biorheological Science and Technology Ministry of Education College of Bioengineering Chongqing University Chongqing400044 China Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province College of Bioengineering Sichuan University of Science and Engineering 188 University Town Yi bin Sichuan 644000 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China Chongqing University China

Herein, P. kudriavzevii cells were immobilized in a gel network formed by chitosan and sodium alginate to form sodium alginate/chitosan-P. kudriavzevii microspheres (AS/CS-PMs). Their structural characteristics and formation processes were characterized by SEM and FT-IR. The effects of synthesis conditions on the performance of microspheres were determined by single-factor experiments. Under the optimal conditions, the SA/CS-PMs could increase the amylase activity of the fermentation broth by 57.18%, the esterase activity by 66.13%, the ester content by 67.04%, and could be reused at least three times. Further research results indicated that the content of esters could be increased significantly in Baijiu solid-state fermentation with the SA/CS-PMs. In conclusion, the SA/CS-PMs could improve the ester production ability of P. kudriavzevii by increasing the esterase activity, which was a valuable exploration for the targeted synthesis of esters in the fermentation of Baijiu. © 2022, The Authors. All rights reserved.

关键词： Esters

Multi-Party dual learning

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arXiv 2021年

作者： Gong, Maoguo Gao, Yuan Xie, Yu Qin, A.K. Pan, Ke Ong, Yew-Soon The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an Shaanxi Province710071 China The Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan030006 China The Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and integrated due to various data policy constraints. As a result, some parties may suffer from insufficient data available for training machine learning models. In this paper, we propose a multi-party dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party. Since the knowledge sharing processes for multiple parties always emerge in dual forms, we show that dual learning is naturally suitable to handle the challenge of missing data, and explicitly exploits the probabilistic correlation and structural relationship between dual tasks to regularize the training process. We introduce a feature-oriented differential privacy with mathematical proof, in order to avoid possible privacy leakage of raw features in the dual inference process. The approach requires minimal modifications to the existing multi-party learning structure, and each party can build flexible and powerful models separately, whose accuracy is no less than non-distributed self-learning approaches. The MPDL framework achieves significant improvement compared with state-of-the-art multi-party learning methods, as we demonstrated through simulations on real-world datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Research Center for Industries of the Future the School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at https://***/IVIPLab/FIWHN. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction