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Expert consensus on the diagnosis and treatment of solid tumors with BRAF mutations

The Innovation 2024年第6期5卷 100-116页

作者： Wenxian Wang Bin Lian Chunwei Xu Qian Wang Ziming Li Nan Zheng Aijun Liu Jinpu Yu Wenzhao Zhong Zhijie Wang Yongchang Zhang Jingjing Liu Shirong Zhang Xiuyu Cai Anwen Liu Wen Li Lili Mao Ping Zhan Hongbing Liu Tangfeng Lv Liyun Miao Lingfeng Min Yu Chen Jingping Yuan Feng Wang Zhansheng Jiang Gen Lin Long Huang Xingxiang Pu Rongbo Lin Weifeng Liu Chuangzhou Rao Dongqing Lv Zongyang Yu Xiaoyan Li Chuanhao Tang Chengzhi Zhou Junping Zhang Junli Xue Hui Guo Qian Chu Rui Meng Xuewen Liu Jingxun Wu Rui Zhang Jin Zhou Zhengfei Zhu Yongheng Li Hong Qiu Fan Xia Yuanyuan Lu Xiaofeng Chen Jian Feng Rui Ge Enyong Dai Yu Han Weiwei Pan Fei Pang Xin Huang Meizhen Hu Qing Hao Kai Wang Fan Wu Binbin Song Bingwei Xu Liping Wang Youcai Zhu Li Lin Yanru Xie Xinqing Lin Jing Cai Ling Xu Jisheng Li Xiaodong Jiao Kainan Li Jia Wei Huijing Feng Lin Wang Yingying Du Wang Yao Xuefei Shi Xiaomin Niu Dongmei Yuan Yanwen Yao Jianhui Huang Yue Feng Yinbin Zhang Pingli Sun Hong Wang Mingxiang Ye Dong Wang Zhaofeng Wang Yue Hao Zhen Wang Bin Wan Donglai Lv Shengjie Yang Jin Kang Jiatao Zhang Chao Zhang Wenfeng Li Jianfei Fu Lizhi Wu Shijie Lan Juanjuan Ou Lin Shi Zhanqiang Zhai Yina Wang Bihui Li Zhang Zhang Ke Wang Xuelei Ma Zhongwu Li Zhefeng Liu Nong Yang Lin Wu Huijuan Wang Gu Jin Guansong Wang Jiandong Wang Hubing Shi Meiyu Fang Yong Fang Yuan Li Xiaojia Wang Jing Chen Yiping Zhang Xixu Zhu Yi Shen Shenglin Ma Biyun Wang Yong Song Zhengbo Song Wenfeng Fang Yuanzhi Lu Lu Si Department of Chemotherapy Chinese Academy of Sciences University Cancer Hospital(Zhejiang Cancer Hospital)HangzhouZhejiang 310022P.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Department of Melanoma and SarcomaPeking University Cancer Hospital&InstituteBeijing 100142P.R.China Institute of Cancer and Basic Medicine(ICBM) Chinese Academy of SciencesHangzhouZhejiang 310022P.R.China Department of Respiratory Medicine Affiliated Jinling HospitalMedical School of Nanjing UniversityNanjingJiangsu 210002P.R.China Department of Respiratory Medicine Affiliated Hospital of Nanjing University of Chinese MedicineJiangsu Province Hospital of Chinese MedicineNanjingJiangsu 210029P.R.China Department of Shanghai Lung Cancer Center Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200030P.R.China Beijing Key Laboratory of Mental Disorders National Clinical Research Center for Mental Disorders&National Center for Mental DisordersBeijing Anding HospitalCapital Medical UniversityBeijing 200030China Advanced Innovation Center for Human Brain Protection Capital Medical UniversityBeijing 200030China Senior Department of Pathology the 7th Medical Center of PLA General HospitalBeijing 100700P.R.China Department of Cancer Molecular Diagnostics Core Tianjin Medical University Cancer Institute and HospitalTianjin 300060P.R.China Guangdong Lung Cancer Institute Guangdong Provincial Laboratory of Translational Medicine in Lung CancerGuangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesSchool of MedicineGuangzhouGuangdong 510080P.R.China State Key Laboratory of Molecular Oncology Department of Medical OncologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021P.R.China Department of Medical Oncology Lung Cancer and Gastrointestinal UnitHunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School

The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathway,which promotes cell proliferation,inhibits cell apoptosis,and ultimately contributes to the occurrence and development of *** mutations are widely present in various cancers,including malignant melanoma,thyroid cancer,colorectal cancer,non-small cell lung cancer,and hairy cell leukemia,among *** is an important target for the treatment of various solid tumors,and targeted combination therapies,represented by BRAF inhibitors,have become one of the main treatment modalities for a variety of BRAF-mutation-positive solid tumors.

关键词： BRAF diagnosis treatment

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Self-Supervised Speech Denoising Using Only Noisy Audio Signals

arXiv

引用

arXiv 2021年

作者： Wu, Jiasong Li, Qingchun Yang, Guanyu Li, Lei Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China RennesF-35042 France The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China Univ Rennes INSERM LTSI UMR 1099 RennesF-35042 France

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://***/liqingchunnnn/Only-Noisy-Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus

引用

National Science Review 2019年第3期6卷 505-514页

作者： Honglin He Shaoqiang Wang Li Zhang Junbang Wang Xiaoli Ren Lei Zhou Shilong Piao Hao Yan Weimin Ju Fengxue Gu Shiyong Yu Yuanhe Yang Miaomiao Wang Zhongen Niu Rong Ge Huimin Yan Mei Huang Guoyi Zhou Yongfei Bai Zongqiang Xie Zhiyao Tang Bingfang Wu Leiming Zhang Nianpeng He Qiufeng Wang Guirui Yu Synthesis Research Center of China's Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences Sino-French Institute for Earth System Science College of Urban and Environment Sciences Peking University Key Laboratory of Alpine Ecology and Biodiversity Institute of Tibetan Plateau Research CAS Center for Excellence in Tibetan Plateau Earth Science Chinese Academy of Sciences National Meteorological Center China Meteorological Administration International Institute for Earth System Science and Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing University Key Laboratory of Dryland Agriculture MOA Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences School of Geography Geomatics and Planning Jiangsu Normal University State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical Garden Chinese Academy of Sciences Department of Ecology College of Urban and Environmental Science Key Laboratory for Earth Surface Processes of the Ministry of Education Peking University Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

The carbon budgets in terrestrial ecosystems in China are strongly coupled with climate changes. Over the past decade, China has experienced dramatic climate changes characterized by enhanced summer monsoon and decelerated warming. However, the changes in the trends of terrestrial net ecosystem production（NEP） in China under climate changes are not well documented. Here, we used three ecosystem models to simulate the spatiotemporal variations in China's NEP during 1982–2010 and quantify the contribution of the strengthened summer monsoon and warming hiatus to the NEP variations in four distinct climatic regions of the country. Our results revealed a decadal-scale shift in NEP from a downtrend of–5.95 TgC/yr2（reduced sink） during 1982–2000 to an uptrend of 14.22 TgC/yr2（enhanced sink） during 2000–10. This shift was essentially induced by the strengthened summer monsoon, which stimulated carbon uptake, and the warming hiatus, which lessened the decrease in the NEP trend. Compared to the contribution of 56.3% by the climate effect, atmospheric CO2concentration and nitrogen deposition had relatively small contributions（8.6 and 11.3%, respectively） to the shift. In conclusion, within the context of the global-warming hiatus, the strengthening of the summer monsoon is a critical climate factor that enhances carbon uptake in China due to the asymmetric response of photosynthesis and respiration. Our study not only revealed the shift in ecosystem carbon sequestration in China in recent decades, but also provides some insight for understanding ecosystem carbon dynamics in other monsoonal areas.

关键词： climate change Asian summer monsoon warming hiatus biogeochemical modeling ecosystem carbon dynamics

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A Summary of the BCCC Data Center network Topology 4

A Summary of the BCCC Data Center Network Topology

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4th IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2018, 4th IEEE International Conference on High Performance and Smart Computing, HPSC 2018 and 3rd IEEE International Conference on Intelligent Data and Security, IDS 2018

作者： Han, Zhijie Zhang, Weiwei Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network Nanjing Jiangsu210003 China Anyang Institute of Technology Henan Anyang China

ISBN: (纸本)9781538643990

The traditional tree network topology and pattern are not applicable with today's "data-oriented" era, and unable to provide better performance to accommodate the exponential growth of data. So Data center networks have emerged and are used in society, work and life. Nowadays, the network is developing constantly, and various network models emerge one after another. The research on data center network is of great significance to the development of the network in the future. This paper mainly introduces the data center network topology of the server-centric represented by BCCC and GBC3, and it is of great significance for researchers to study the data center network topology with server-centric. © 2018 IEEE.

关键词： network topology

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Classification and Recognition of Encrypted EEG Data Based on Neural network

arXiv

引用

arXiv 2020年

作者： Liu, Yongshuang Huang, Haiping Xiao, Fu Malekian, Reza Wang, Wenming College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province Nanjing210023 China Department of Computer Science and Media Technology Malmö University Malmö20506 Sweden School of Computer and Information Anqing Normal University AnqingAnhui246011 China

With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Electrophysiology

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Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution network

arXiv

引用

arXiv 2019年

作者： Dai, Yuxiang Zhuang, Peixian Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Technology and Engineering Center of Meteorological Sensor Network School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Magnetic resonance imaging (MRI) reconstruction is an active inverse problem which can be addressed by conventional compressed sensing (CS) MRI algorithms that exploit the sparse nature of MRI in an iterative optimization-based manner. However, two main drawbacks of iterative optimization-based CSMRI methods are time-consuming and are limited in model capacity. Meanwhile, one main challenge for recent deep learning-based CSMRI is the trade-off between model performance and network size. To address the above issues, we develop a new multi-scale dilated network for MRI reconstruction with high speed and outstanding performance. Comparing to convolutional kernels with same receptive fields, dilated convolutions reduce network parameters with smaller kernels and expand receptive fields of kernels to obtain almost same information. To maintain the abundance of features, we present global and local residual learnings to extract more image edges and details. Then we utilize concatenation layers to fuse multi-scale features and residual learnings for better reconstruction. Compared with several non-deep and deep learning CSMRI algorithms, the proposed method yields better reconstruction accuracy and noticeable visual improvements. In addition, we perform the noisy setting to verify the model stability, and then extend the proposed model on a MRI super-resolution task. keywords: MRI Reconstruction, Dilated Convolution, Residual Learning, Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Survey of the BCube Data Center network Topology 4

A Survey of the BCube Data Center Network Topology

引用

作者： Han, Zhijie Yu, Lin Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network Nanjing Jiangsu210003 China Zhengzhou Shuqing Medical College Henan Zhengzhou China

ISBN: (纸本)9781538643990

With the development of cloud computing and data center, In the present society, the data center network has become an indispensable network. And the data center network has played a very important role in transportation, economy and social work. So the study of server-centric data center networks is now the focus of researchers at home and abroad. And the BCube is one of the most classic network topology. This paper mainly introduces the data center network topology of the switch-centric represented by Fat-tree and the server-centric represented by BCube. This paper mainly introduces Fat-tree and BCube, BCube as the classic server-centric data center network topology, which is of great significance for the future development of data center network topologies. © 2018 IEEE.

关键词： network topology

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A coalitional game based user access algorithm in energy-harvesting small cell networks 18

A coalitional game based user access algorithm in energy-har...

引用

2018 18th IEEE International Conference on Communication technology, ICCT 2018

作者： Wang, Xueting Izhu, Q. Nanjing University of Posts and Telecommunications Jiangsu Province Key Laboratory of Wireless Communications Key Laboratory on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education Nanjing China

ISBN: (纸本)9781538676349

To improve the users' satisfaction of downloading files, a cache based resource allocation algorithm in energy-harvesting small cell networks is proposed, which is established as a coalitional game and users try to switch between coalitions with satisfaction as utility function. The switch is accepted once the utilities of user and coalition after switching satisfies the switch orders. This algorithm also allows multi users to be served by a same small cell sharing the same channel using Non-orthogonal Frequency Division Multiple Access (NOMA). The simulation results show that our novel algorithm performs better on user satisfaction. © 2018 IEEE.

关键词： Energy harvesting

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An Agile Automatic Frequency Calibration Technique for PLL

An Agile Automatic Frequency Calibration Technique for PLL

引用

IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Xin Ding Jianhui Wu Chao Chen Jiangsu Key Laboratory of sensor network technology

ISBN: (纸本)9781538665527;9781538665510

An agile automatic frequency calibration (AFC) technique is developed in phase locked loop (PLL) for the bluetooth low energy (BLE) applications. Instead of searching for an optimal tuning curve for 40 target frequencies in BLE, the tuning curve feature extraction (TCFE) technique is adopted to reduce necessary calibration times. Moreover, the initial value of each target frequency is adjusted dynamically according to previous result to accelerate calibration speed. The calibration is conducted foreground, then the optimal tuning curve is selected directly and the output frequency of PLL hops quickly in operation. The whole PLL was designed and fabricated in 0.18 μm CMOS technology. Measurement results show that the time for foreground calibration is about 80 μs and the frequency hops without the switch of tuning curves.

关键词： Calibration Tuning Phase locked loops Feature extraction Frequency measurement Voltage-controlled oscillators Automatic frequency control

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Building an efficient intrusion detection system based on feature selection and ensemble classifier

arXiv

引用

arXiv 2019年

作者： Zhou, Yuyang Cheng, Guang Jiang, Shanqing Dai, Mian School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Jiangsu Provincial Key Laboratory of Computer Network Technology Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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