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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Liu, Jiaxin Ji, Xinglai Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing210003 China Yunnan University State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Kunming650091 China

ISBN: (纸本)9798331507398

The LeaderRank algorithm identifies influential nodes using the idea of the random walk and performs well in the identification of hub genes in biological networks. The rapid development of biological technologies promotes the evolution of networks to be highly complex and increasing largely in size. In this study, we introduce LRCu, for computing LeaderRank using CUDA, to identify hub genes. The results show that the parallel implementation is highly effective and achieves a speedup of more than 42 times for a 13,000-gene network, thereby enhancing large-scale network computation. © 2024 IEEE.

关键词： CUDA gene co-expression network hub gene parallel computing

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Salient Patch Based NAS for Grading of Colorectal Cancer Histology Images 13

Salient Patch Based NAS for Grading of Colorectal Cancer His...

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13th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2022

作者： Meng, Hang Li, Fei Yang, Zhen Zhu, Qiang Fan, Chaogang Zhan, Shu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Province Hefei230009 China The Second Affiliated Hospital of Anhui Medical University Anhui Province Hefei230601 China Tongcheng Teachers College Anhui Province Tongcheng231400 China Department of General Surgery The Affiliated BenQ Hospital of Nanjing Medical University Jiangsu Province Nanjing210021 China

ISBN: (纸本)9781950492619

Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the limited computing resources, it is not practical to directly train CNNs on gigapixel resolution histopathological tissue images. Therefore, the entire histopathological image is commonly divided into a grid of patches and then fed each patch to CNNs, but not all patches are necessary for specific tasks. Besides, although CNNs are excellent image classifiers, many pre-trained models are based on natural images. Neural Architecture Search (NAS) can realize automatic search, construction, and training specific problemoriented networks. We present salient patch based NAS for grading colorectal cancer (CRC) histology images with these motivations. The proposed framework first applies the classic classification network to extract significant patches from the whole image, then use the network obtained by NAS to grade the patches, and finally aggregates the patch-level predictions to generate the entire CRC histological image label. Our experiments on microscopic colorectal tissue images demonstrate that the proposed method is sufficient to obtain more accurate classification results compared to its counterparts. We also conducted a comprehensive analysis of the different scenarios of the proposed method. © by the International Institute of Informatics and Systemics.

关键词： Grading

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The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims

arXiv

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arXiv 2024年

作者： Zhou, Shu Wang, Xin Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China Baidu Inc. Beijing China

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and uniqueness of patent texts, we proposes a patent text abstract generation model (MSEA) based on a master-slave encoder architecture;Firstly, the MSEA model designs a master-slave encoder, which combines the instructions in the patent text with the claims as input, and fully explores the characteristics and details between the two through the master-slave encoder;Then, the model enhances the consideration of new technical terms in the input sequence based on the pointer network, and further enhances the correlation with the input text by re weighing the "remembered" and "for-gotten" parts of the input sequence from the encoder;Finally, an enhanced repetition suppression mechanism for patent text was introduced to ensure accurate and non redundant abstracts generated. On a publicly available patent text dataset, compared to the state-of-the-art model, Improved Multi-Head Attention Mechanism (IMHAM), the MSEA model achieves an improvement of 0.006, 0.005, and 0.005 in Rouge-1, Rouge-2, and Rouge-L scores, respectively. MSEA leverages the characteristics of patent texts to effectively enhance the quality of patent text generation, demonstrating its advancement and effectiveness in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

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Amplitudes and frequencies cross modulation of epileptic EEG between 16 leads 3

Amplitudes and frequencies cross modulation of epileptic EEG...

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3rd International Conference on Biological Information and Biomedical engineering, BIBE 2019

作者： Cai, Yujie Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9783800750269

Epilepsy is caused by abnormal brain nerves. In order to analyze the similarities and differences of EEG signals between patients with epilepsy and normal people, this paper introduced a cross modulation algorithm. This paper analyzed the correlation between amplitude and frequency between EEG signals in various parts of the brain. The approach overcomes the limitation to complexity of the algorithm and found abnormalities in EEG signals of temporal lobe and occipital lobe. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Computational complexity

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Cross-Modulated Amplitudes and Frequencies of Epileptic EEG 4

Cross-Modulated Amplitudes and Frequencies of Epileptic EEG

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Cai, Yujie Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

Epilepsy is a neural disorder with the hallmark of recurrent seizures. To characterize the epileptic brain electrical activities, we employed the cross modulation of instantaneous amplitudes and frequencies to separate synchronous and anti-synchronous modulation. Amplitude-amplitude, amplitude-frequency and frequency-frequency cross modulation were adopted to analyse the difference between EEG signal of epileptic patients and that of normal people. By comparing the observed patterns with two groups of EEG signals we demonstrate that the cross-modulation exponentials at the temporal region and the occipital region of right hemisphere are significant different. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Frequency modulation

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Epileptic brain network analysis based on Kendall's improved synchronization algorithm 1

Epileptic brain network analysis based on Kendall's improved...

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1st International Conference on Physics, Mathematics and Statistics, ICPMS 2018

作者： Ding, Chuchu Sun, Ting Zou, Xin Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing210002 China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing210009 China

In this paper, we use a new algorithm, the IRC algorithm, to improve the Kendall algorithm. Research on complex networks has gradually deepened into all areas of social science. The study of brain networks has become a hot topic in the study of brain function. The method of wavelet filtering is used to filter the EEG data to obtain the required α-band (8-16 Hz). Using the improved IRC algorithm, the brain functional network is constructed based on the EEG data, and the related characteristics of the brain network constructed are analyzed. The experimental results show that the method is suitable for distinguishing the network degree indicators of epilepsy and normal brain tissue, and further deepening the study of the neurokinetic behavior of the brain. © Published under licence by IOP Publishing Ltd.

关键词： Complex networks

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SAMPLING LOVÁSZ LOCAL LEMMA FOR GENERAL CONSTRAINT SATISFACTION SOLUTIONS IN NEAR-LINEAR TIME

arXiv

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arXiv 2022年

作者： He, Kun Wang, Chunyang Yin, Yitong The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing China State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing China

We give a fast algorithm for sampling uniform solutions of general constraint satisfaction problems (CSPs) in a local lemma regime. Suppose that the CSP has n variables with domain size at most q, each constraint contains at most k variables, shares variables with at most Δ constraints, and is violated with probability at most p by a uniform random assignment. The algorithm returns an almost uniform satisfying assignment in expected poly(q,k, Δ) ·(n) time, as long as a local lemma condition is satisfied: k · p · q2 · Δ5 ≤ C0 for a suitably small absolute constant C0. Previously, under similar local lemma conditions, sampling algorithms with running time polynomial in both nand Δ were only known for the almost atomic case, where each constraint is violated by a small number of forbidden local configurations. The key term Δ5 in our local lemma condition also improves the previously best known Δ7 for general CSPs [JPV21b] and Δ5.714 for atomic CSPs, including the special case of k-CNF [JPV21a, HSW21]. Our sampling approach departs from previous fast algorithms for sampling LLL, which were based on Markov chains. A crucial step of our algorithm is a recursive marginal sampler that is of independent interests. Within a local lemma regime, this marginal sampler can draw a random value for a variable according to its marginal distribution, at a cost independent of the size of the CSP. Copyright © 2022, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms 12

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhong, Weiyang Jiang, Shanxi Wu, Wei Peng, Danhong Xu, Tian Wang, Jun Wang, Gongdao Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China China Tower Corporation Limited Nantong Branch China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing210009 China Department of Obstetrics and Gynecology Zhongda Hospital Southeast University Nanjing School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9781728148526

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network. © 2019 IEEE.

关键词： Genetic algorithms

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LRCu: A High-Performance Parallel Program for Identifying Hub Genes

LRCu: A High-Performance Parallel Program for Identifying Hu...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Jiaxin Liu Xinglai Ji Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Yunnan University Kunming China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

关键词： Evolution (biology) Signal processing algorithms Graphics processing units Signal processing Informatics Biomedical engineering

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

SSRN

引用

SSRN 2023年

作者： Kong, Lijun Fan, Lin Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China Nanjing University Nanjing210093 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel Neurotransmitter Biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. Beyond that, the multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based Neurotransmitter Biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 5.066 μM and 0.288 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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