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Jian Wan
Department of Computer Science, Zhejiang University of Science and Technology, 91616 Hangzhou, Zhejiang, China

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Original research article
Published: 17 August 2021 in Frontiers in Bioengineering and Biotechnology
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Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).

ACS Style

Ke Xue; Huilin Zheng; Xiaowen Qian; Zheng Chen; Yangjun Gu; Zhenhua Hu; Lei Zhang; Jian Wan. Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO. Frontiers in Bioengineering and Biotechnology 2021, 9, 1 .

AMA Style

Ke Xue, Huilin Zheng, Xiaowen Qian, Zheng Chen, Yangjun Gu, Zhenhua Hu, Lei Zhang, Jian Wan. Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO. Frontiers in Bioengineering and Biotechnology. 2021; 9 ():1.

Chicago/Turabian Style

Ke Xue; Huilin Zheng; Xiaowen Qian; Zheng Chen; Yangjun Gu; Zhenhua Hu; Lei Zhang; Jian Wan. 2021. "Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO." Frontiers in Bioengineering and Biotechnology 9, no. : 1.

Journal article
Published: 05 August 2021 in IEEE Transactions on Network Science and Engineering
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People connected by huge-size social networks are highly dependent on recommendation systems to discover interesting persons, contents, and commodities. Extracting user interests and modeling their evolutions from user historical behavior are vital for algorithms to judge whether users are interested in given persons and items. The existing methods have two shortcomings: firstly, they do not have the high-order ability to process temporal sequences, which leads to their inability to mine the evolution of user interests; secondly, the models take full large-scale information of items as input, which causes a severe problem of overfitting. Thus, we propose a new hybrid neural network, DGRU, which integrates Factorization-Machine Based Neural Network (DeepFM) and Gated Recurrent Unit Neural Network (GRU). The DeepFM component is responsible for performing the autonomic feature combination, and the GRU component is designed to model user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors. It contains information on what users like and dislike. Moreover, the conciseness of the format is helpful to avoid the problem of overfitting. Experiments on three real datasets demonstrated that the proposed model has a better CTR prediction performance and robustness than the state-of-the-art models.

ACS Style

Renjie Zhou; Chen Liu; Jian Wan; Qing Fan; Yongjian Ren; Jilin Zhang; Naixue Xiong. A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.

AMA Style

Renjie Zhou, Chen Liu, Jian Wan, Qing Fan, Yongjian Ren, Jilin Zhang, Naixue Xiong. A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Renjie Zhou; Chen Liu; Jian Wan; Qing Fan; Yongjian Ren; Jilin Zhang; Naixue Xiong. 2021. "A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 02 July 2021 in IEEE Transactions on Network Science and Engineering
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Cyber-Physical-Social Systems (CPSS) provide great value to our lives, but they also cause data overload problems. Data-driven personalized recommendation service is one of the most efficient means to solve such problems, which is currently receiving wide attention from research and industrial communities. The most important task of personalized recommender systems is to predict the click-through rate of given items, which is especially true for personalized advertisement recommendation systems. Recently, a number of deep click-through models have been proposed, which obtain low-dimensional dense embedding vectors of features, and then concatenate them together and input into multi-layer perceptron to learn the nonlinear relationship between the features. However, the existing models don't dig deep enough into the user preferences and habits in users behavior history. In this paper, we propose a new model: Self-attention based Deep Neural Network (DeepSA), which addresses this issue by constructing Ad-related graph and training graph embedding vectors to enhance the representation of the advertisement for capturing user interests, and learns the internal correlation between user behaviors via the self-attention mechanism, which better explores interests and preferences hidden in users' historical behaviors. Experiments on two public datasets and an industrial dataset demonstrate the proposed method outperforms the state-of-the-art models

ACS Style

Renjie Zhou; Hao Qian; Chang Liu; Jian Wan; Yongjian Ren; Jilin Zhang; Naixue Xiong; Nailiang Zhao; SanYuan Zhang. Self-attention Mechanism Enhanced User Interests Modeling for Personalized Recommendation Services in Cyber-Physical-Social Systems. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.

AMA Style

Renjie Zhou, Hao Qian, Chang Liu, Jian Wan, Yongjian Ren, Jilin Zhang, Naixue Xiong, Nailiang Zhao, SanYuan Zhang. Self-attention Mechanism Enhanced User Interests Modeling for Personalized Recommendation Services in Cyber-Physical-Social Systems. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.

Chicago/Turabian Style

Renjie Zhou; Hao Qian; Chang Liu; Jian Wan; Yongjian Ren; Jilin Zhang; Naixue Xiong; Nailiang Zhao; SanYuan Zhang. 2021. "Self-attention Mechanism Enhanced User Interests Modeling for Personalized Recommendation Services in Cyber-Physical-Social Systems." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 17 February 2019 in Energies
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Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

ACS Style

Yeliang Qiu; Congfeng Jiang; Yumei Wang; Dongyang Ou; Youhuizi Li; Jian Wan. Energy Aware Virtual Machine Scheduling in Data Centers. Energies 2019, 12, 646 .

AMA Style

Yeliang Qiu, Congfeng Jiang, Yumei Wang, Dongyang Ou, Youhuizi Li, Jian Wan. Energy Aware Virtual Machine Scheduling in Data Centers. Energies. 2019; 12 (4):646.

Chicago/Turabian Style

Yeliang Qiu; Congfeng Jiang; Yumei Wang; Dongyang Ou; Youhuizi Li; Jian Wan. 2019. "Energy Aware Virtual Machine Scheduling in Data Centers." Energies 12, no. 4: 646.

Journal article
Published: 12 December 2018 in Sensors
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In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface.

ACS Style

Congfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors 2018, 18, 4395 .

AMA Style

Congfeng Jiang, Tiantian Fan, Yeliang Qiu, Hongyuan Wu, Jilin Zhang, Neal N. Xiong, Jian Wan. Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors. 2018; 18 (12):4395.

Chicago/Turabian Style

Congfeng Jiang; Tiantian Fan; Yeliang Qiu; Hongyuan Wu; Jilin Zhang; Neal N. Xiong; Jian Wan. 2018. "Interdomain I/O Optimization in Virtualized Sensor Networks." Sensors 18, no. 12: 4395.

Journal article
Published: 01 December 2017 in Sustainable Computing: Informatics and Systems
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ACS Style

Youhuizi Li; Mohit Kumar; Weisong Shi; Jian Wan. Falcon: An ambient temperature aware thermal control policy for IoT gateways. Sustainable Computing: Informatics and Systems 2017, 16, 48 -55.

AMA Style

Youhuizi Li, Mohit Kumar, Weisong Shi, Jian Wan. Falcon: An ambient temperature aware thermal control policy for IoT gateways. Sustainable Computing: Informatics and Systems. 2017; 16 ():48-55.

Chicago/Turabian Style

Youhuizi Li; Mohit Kumar; Weisong Shi; Jian Wan. 2017. "Falcon: An ambient temperature aware thermal control policy for IoT gateways." Sustainable Computing: Informatics and Systems 16, no. : 48-55.

Conference paper
Published: 19 September 2017 in Computer Vision
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In recent years, container technologies have attracted intensive attention due to the features of light-weight and easy-portability. The performance isolation between containers is becoming a significant challenge, especially in terms of network throughput and disk I/O. In traditional VM environments, the performance isolation is often calculated based on performance loss ratio. In container environments, the performance loss of well-behaved containers may be incurred not only by misbehaving containers but also by container orchestration and management. Therefore, the measurement models that only take performance loss into consideration will be not accurate enough. In this paper, we proposed a novel performance isolation measurement model that combines the performance loss and resource shrinkage of containers. Experimental results validate the effectiveness of our proposed model. Our results highlight the performance isolation between containers is different with the issue in VM environments.

ACS Style

Chang Zhao; Yusen Wu; Zujie Ren; Weisong Shi; Yongjian Ren; Jian Wan. Quantifying the Isolation Characteristics in Container Environments. Computer Vision 2017, 145 -149.

AMA Style

Chang Zhao, Yusen Wu, Zujie Ren, Weisong Shi, Yongjian Ren, Jian Wan. Quantifying the Isolation Characteristics in Container Environments. Computer Vision. 2017; ():145-149.

Chicago/Turabian Style

Chang Zhao; Yusen Wu; Zujie Ren; Weisong Shi; Yongjian Ren; Jian Wan. 2017. "Quantifying the Isolation Characteristics in Container Environments." Computer Vision , no. : 145-149.