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1,341 results on '"Jiang, Xiaodong"'

3. Relationship Between Peripheral Blood T Lymphocyte Subsets and Prognosis of Patients with Advanced Non-small Cell Lung Cancer Treated with Camrelizumab

Author

DONG Changhong, FENG Yan, JIANG Yanting, GAO Jie, and JIANG Xiaodong

Subjects
non-small cell lung cancer, cd4+/cd8+ ratio, cd4+t cell, cd8+t cell, anti-pd-1 immunotherapy, camrelizumab, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Objective To explore the relationship between peripheral blood T lymphocyte subsets and prognosis of patients with advanced non-small cell lung cancer (NSCLC) who received treatment with camrelizumab. Methods We retrospectively collected data from 88 patients with advanced NSCLC who underwent camrelizumab treatment. Peripheral blood lymphocyte subsets were collected from patients before and two months after treatment. Kaplan-Meier curves and Cox regression analysis were employed to investigate the relationship between peripheral blood T lymphocyte subsets and PFS and OS. Results Compared with non-responder group, the baseline peripheral blood CD4+/CD8+ ratio was higher (P=0.038), while the CD8+T lymphocyte percentage was lower (P=0.036) in the responder group. Kaplan-Meier curves showed that a high baseline CD4+/CD8+ ratio was associated with long PFS and OS (P=0.001, P=0.023). Multivariate Cox analysis revealed that the baseline CD4+/CD8+ ratio was a significant predictor for PFS and OS. Additionally, a high post-treatment CD4+/CD8+ ratio and high CD4+T lymphocyte percentage were associated with long PFS (P=0.005, P=0.015), whereas a low post-treatment CD8+T lymphocyte percentage was associated with long PFS and OS (P=0.001, P=0.016). Conclusion The peripheral blood CD4+/CD8+ ratio can serve as a predictive factor for survival of patients with NSCLC treated with camrelizumab.

Published
2024
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4. MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection

Author

Chatterjee, Sourav, Bopardikar, Rohan, Guerard, Marius, Thakore, Uttam, and Jiang, Xiaodong

Subjects
Computer Science - Machine Learning
Abstract

Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and anomaly patterns. The selection of algorithm and parameters needs to be precise for each use case: manual tuning does not scale, and automated tuning requires ground truth, which is rarely available. In this paper, we explore MOSPAT, an end-to-end automated machine learning based approach for model and parameter selection, combined with a generative model to produce labeled data. Our scalable end-to-end system allows individual users in large organizations to tailor time-series monitoring to their specific use case and data characteristics, without expert knowledge of anomaly detection algorithms or laborious manual labeling. Our extensive experiments on real and synthetic data demonstrate that this method consistently outperforms using any single algorithm., Comment: 10 pages, submitted originally to KDD'22

Published
2022

11. Relationship between Crop Yield and Soil Properties and Its Application for Optimizing Irrigation and Fertilization in Hetao Irrigation District

Author

SONG Awei, LIU Yuxing, WANG Xiangping, XIE Wenping, ZHANG Chenping, JIANG Xiaodong, and YAO Rongjiang

Subjects
hetao irrigation district, soil properties, crop yield, spatial distribution, soil salinization, water and fertilizer management, Agriculture (General), S1-972, Irrigation engineering. Reclamation of wasteland. Drainage, TC801-978
Abstract

【Objective】 Crop yield is inherently influenced by soil properties. This paper analyzes the spatial distribution of soil properties in Hetao Irrigation District (HID) and its relationship with crop yields, from which we propose how to optimize irrigation and fertilization to improve soil quality and crop yield in the district. 【Method】 The analysis was based on experimental data. The spatial distribution of soil physicochemical properties and crop yields in the district were analyzed using the classical statistics and geostatistics. Their correlations were used to help improve irrigation and fertilization. 【Result】 ①We found prominent issues such as high salinization, increased soil bulk density, and reduced crop yields, in the district. Notably, organic matter and nitrogen levels are relatively low, while available phosphorus is adequate and available potassium is abundant. Soil salinity showed considerable spatial variability, whereas organic matter, total nitrogen, available phosphorus, potassium, and crop yield coefficients showed moderate spatial fluctuations. Bulk density and soil pH, on the other hand, demonstrated only minor spatial variation. Bulk density and soil pH showed weak spatial variation. ②Spatially, soil salinity was pronounced in the central region, decreasing towards the periphery. Crop yield coefficients declined from the Southwest to the Northeast. Organic matter content increased from the West to the East. Soil bulk density, pH, total nitrogen and available phosphorus were the highest in the central region and the least in the East and the West. Low available potassium content was in the Southeast corner, with other areas featuring medium to high levels. ③Soil salinity was the main constraint to crop yields. ④Nitrogen and organic fertilization should be increased to improve soil fertility and crop yields. Specially, we recommend applying 225 kg/hm2 of urea and 3 393.7 kg/hm2 of organic fertilizer for heavily salinized and saline regions, 295.4 kg/hm2 of urea and 3 332.8 kg/hm2 of organic fertilizer for moderately salinized regions, and 365.1 kg/hm2 of urea and 1 280.9 kg/hm2 of organic fertilizer for slightly salinized and non-saline regions. In addition, drainage systems in the Northwest, South, and Southeast of the district should be improved to facilitate salt leaching. 【Conclusion】 The findings outlined in this paper establish a fundamental reference for enhancing soil salinity control and drainage management, as well as optimizing fertilization and irrigation to maintain and bolster crop yields in the Hetao Irrigation District.

Published
2023
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22. Self-supervised learning for fast and scalable time series hyper-parameter tuning

Author

Zhang, Peiyi, Jiang, Xiaodong, Holt, Ginger M, Laptev, Nikolay Pavlovich, Komurlu, Caner, Gao, Peng, and Yu, Yang

Subjects
Computer Science - Machine Learning, K.3.2
Abstract

Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a given model, and therefore, selecting good hyper-parameter values is indispensable. Most of the existing generic hyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian Optimal Search, are based on one key component - search, and thus they are computationally expensive and cannot be applied to fast and scalable time-series hyper-parameter tuning (HPT). We propose a self-supervised learning framework for HPT (SSL-HPT), which uses time series features as inputs and produces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting hyper-parameters compared to other search based algorithms while producing comparable accurate forecasting results in various applications.

Published
2021

25. Co-embedding of Nodes and Edges with Graph Neural Networks

Author

Jiang, Xiaodong, Zhu, Ronghang, Ji, Pengsheng, and Li, Sheng

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space to a low dimensional and structural space, which is easily exploited by other machine learning algorithms. We have witnessed a huge surge of such embedding methods, from statistical approaches to recent deep learning methods such as the graph convolutional networks (GCN). Deep learning approaches usually outperform the traditional methods in most graph learning benchmarks by building an end-to-end learning framework to optimize the loss function directly. However, most of the existing GCN methods can only perform convolution operations with node features, while ignoring the handy information in edge features, such as relations in knowledge graphs. To address this problem, we present CensNet, Convolution with Edge-Node Switching graph neural network, for learning tasks in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach achieves or matches the state-of-the-art performance in four graph learning tasks, including semi-supervised node classification, multi-task graph classification, graph regression, and link prediction., Comment: This manuscript has been accepted by the IEEE Transactions on Pattern Analysis and Machine Intelligence

Published
2020

34. Controlling light emission by engineering atomic geometries in silicon photonics

Author

Nandi, Arindam, Jiang, Xiaodong, Pak, Dongmin, Perry, Daniel, Han, Kyunghun, Bielejec, Edward S, Xuan, Yi, and Hosseini, Mahdi

Subjects
Physics - Applied Physics, Quantum Physics
Abstract

By engineering atomic geometries composed of nearly 1000 atomic segments embedded in micro-resonators we observe Bragg resonances induced by the atomic lattice at the telecommunication wavelength. The geometrical arrangement of erbium atoms into a lattice inside a silicon nitride microring resonator reduces the scattering loss at a wavelength commensurate with the lattice. We confirm dependency of light emission to the atomic positions and lattice spacing and also observe Fano interference between resonant modes in the system., Comment: 4 pages, 3 figures

Published
2019
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35. Camrelizumab Plus Carboplatin and Pemetrexed as First-Line Treatment for Advanced Nonsquamous NSCLC: Extended Follow-Up of CameL Phase 3 Trial

Author

Zhou, Caicun, Chen, Gongyan, Huang, Yunchao, Zhou, Jianying, Lin, LiZhu, Feng, Jifeng, Wang, Zhehai, Shu, Yongqian, Shi, Jianhua, Hu, Yi, Wang, QiMing, Cheng, Ying, Wu, Fengying, Chen, Jianhua, Lin, Xiaoyan, Wang, Yongsheng, Huang, Jianan, Cui, Jiuwei, Cao, Lejie, Liu, Yunpeng, Zhang, Yiping, Pan, Yueyin, Zhao, Jun, Wang, LiPing, Chang, Jianhua, Chen, Qun, Ren, Xiubao, Zhang, Wei, Fan, Yun, He, Zhiyong, Fang, Jian, Gu, Kangsheng, Dong, XiaoRong, Jin, Faguang, Gao, Hongjun, An, Guangyu, Ding, Cuimin, Jiang, Xiaodong, Xiong, Jianping, Zhou, Xiangdong, Hu, Sheng, Lu, Ping, Liu, Anwen, Guo, Shuliang, Huang, Jianjin, Zhu, Chengchu, Zhao, Jian, Gao, Beili, Chen, Yinglan, Hu, Chengping, Zhang, Jian, Zhang, Hongmei, Zhao, Hui, Tai, Yanfei, Ma, Xinjing, and Shi, Wei

Published
2023
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