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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection 16

On Learning Frequency-Instance Correlations by Model-Agnosti...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Wang, Zining Gao, Lijian Zhang, Jialin Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

The goal of Synthetic Speech Detection (SSD) is to detect spoofing speech synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only on mining frequency-wise dependency by customizing frequency-aggregation modules in SSD models. However, the instance-wise dependency is usually under-explored, which is critical for identifying the synthetic speech from a global view. In this paper, we propose a novel model-agnostic training strategy for SSD that exploits both local (frequency-wise) and global (instance-wise) contexts, which do not rely on a customized architecture and can be flexibly integrated into previous SSD models. Specifically, we propose an inter-frequency correlation module to capture the local context by reconstructing the masked frequency information from the unmasked frequency context. Meanwhile, an inter-instance correlation module is performed to explore the global context among different instances by promoting intra-class compactness and inter-class dispersion in the latent space. These two complementary modules operate from distinct contextual perspectives, leading to improvements in SSD performance. Extensive experiments show that our method significantly improves the performance of two state-of-the-art models on the 2019 dataset and 2021 dataset of ASVspoof. © 2024 Z. Wang, L. Gao, J. Zhang & Q. Mao.

关键词： Speech recognition

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Study on IoT and big data analysis of Furnace Process Exhaust Gas Leakage 15th

Study on IoT and Big Data Analysis of Furnace Process Exhaus...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2019

作者： Zhou, Yu-Wen Chang, Kuo-Chi Pan, Jeng-Shyang Chu, Kai-Chun Horng, Der-Juinn Lin, Yuh-Chung Jing, Huang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Administration Group of Strategic Management National Central University Taoyuan Taiwan College of Information Science and Engineering Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811397134

Modern FABs use a large number of high-energy processes such as plasma, CVD, and ion implantation;the furnace is one of the important tools of semiconductor manufacturing. The FAB installed FTIR system due to the 12" furnace tools based on the aforementioned production management requirements. This study used open-type FTIR and integrated IoT mechanism to connect to the cloud, which is suitable for a variety of gaseous pollutants. This study set up two measuring points of furnace process tools in the 12" factory of Hsinchu science Park in Taiwan. This study obtained FTIR measurements, and according to the OHSA regulations, this study is set in the cloud database for big data analysis and decision-making, when the upper limits of TEOS, C2H4, and CO are 0.6 ppm, 2.0 ppm, and 1.7 ppm and the lower limits of TEOS, C2H4, and CO are 0.4 ppm, 1.5 ppm, and 1 ppm. The application architecture of this study can be extended to other semiconductor processes, so that IoT integration and big data operations can be performed for all processes;this is an important step in promoting FAB intelligent production and an important contribution of this study. © 2020, Springer Nature Singapore Pte Ltd.

关键词： big data

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

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21st International Conference on Parallel and Distributed Computing, applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In Software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous Software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous Software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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Activation Control of Multiple Piecewise Linear Neural Networks

Activation Control of Multiple Piecewise Linear Neural Netwo...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Chen Hou National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China APKU-Changsha Institute for Computing and Digital Economy Changsha China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Piecewise linear neural networks (PLNNs) are proven universal approximators for continuous functions on the compact domain. For multiple PLNNs (mPLNNs) differing from each other in suffering different approximation errors (AEs) when approximating the same continuous function, activating all the PLNNs to approximate the continuous function, and then, picking up the minimum AE (MAE) from all the AEs seems to be a practical way to arrive at such MAE. Activating PLNNs has to consume energy, and more activated PLNNs provide more AEs to consider, which can maximize the probability of guaranteeing the MAE, while also needing to harvest more energy. Therefore, how to make the optimal tradeoff between energy harvested and approximation accuracy for mPLNNs arises as an interesting issue. To address this problem, this paper first deduces the objective function, with the accumulative probability of obtaining the MAE as the objective and the accumulative energy harvested as the constraint, then proposes an algorithm for mPLNNs to enjoy the maximum probability of achieving the MAE within the acceptable level of accumulative energy harvested. Experiments verify its performance.

关键词： Energy consumption Accuracy Computer aided software engineering Automation Neurons Approximation error Approximation algorithms Linear programming Biological neural networks

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A DECOUPLING TWO-GRID METHOD FOR THE STEADY-STATE POISSON-NERNST-PLANCK EQUATIONS

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Journal of Computational Mathematics 2019年第4期37卷 556-578页

作者： Ying Yang Benzhuo Lu Yan Xie School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin 541004 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing the National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Poisson-Nernst-Planck equations are widely used to describe the electrodiffusion of ions in a solvated biomolecular system. Two kinds of two-grid finite element algorithms are proposed to decouple the steady-state Poisson-Nernst-Planck equations by coarse grid finite element approximations. Both theoretical analysis and numerical experiments show the efficiency and effectiveness of the two-grid algorithms for solving Poisson-Nernst-Planck equations.

关键词： Poisson-Nernst-Planck equations Two-grid finite element method Decoupling method Error analysis Gummel iteration

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A Robust Optimization Method for Dynamic Operating Envelope Coordination in Distribution Networks

A Robust Optimization Method for Dynamic Operating Envelope ...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zhisen Jiang Ye Guo Jianxiao Wang Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

In this paper, we propose a Dynamic Operating Envelope (DOE) coordination method for aggregators in distribution networks (DNs). While the DOE acts as a time-varying power export limit to ensure the DN’s integrity, existing aggregator-agnostic allocation overlooks the surplus of aggregators. To address this limitation, our approach allows aggregators to cooperatively adjust DOEs by solving a two-stage robust optimization and settling payments between themselves. The coordination problem is solved by the column-and-constraint generation (C&CG) algorithm, ensuring that any power injection within the adjusted DOE maintains the network integrity. Consequently, the aggregators’ total surplus increases. A 4-bus system case study demonstrates the effectiveness of the proposed method.

关键词： Economics Scalability Heuristic algorithms Optimization methods Energy Internet Distribution networks System integration Real-time systems Resource management

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Earthquake early warning using ocean bottom seismic data for railways

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Quarterly Report of RTRI (Railway Technical Research Institute) 2019年第2期60卷 134-139页

作者： Korenaga, Masahiro Yamamoto, Shunroku Noda, Shunta Aoi, Shin Seismic Data Analysis Laboratory Center for Railway Earthquake Engineering Research National Research Institute for Earth Science and Disaster Resilience

This article explains processes for improving the railway earthquake early warning system. These processes were introduced to use data transmitted in real time from recently developed ocean bottom seismic networks. Three mechanisms were designed to be able to exploit this data: 1) an algorithm for servers in OBS system base stations;2) a procedure to allow transmissions between the servers and railway company receivers;and 3) a system built into the receiver to determine whether running trains need to be stopped or not. Confirmation was obtained that the proposed processes were able to reduce the risks to railways from earthquakes, because they are capable of extending the lead time before the arrival of strong seismic motion. © 2019 Ken-yusha Inc.. All rights reserved.

关键词： Shear waves

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