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2,006 results on '"Hu, Xiaohua"'

106. Data Analysis for Gut Microbiota and Health

Author

Jiang, Xingpeng, Hu, Xiaohua, COHEN, IRUN R., Series editor, LAJTHA, ABEL, Series editor, LAMBRIS, JOHN D., Series editor, PAOLETTI, RODOLFO, Series editor, REZAEI, NIMA, Series editor, and Shen, Bairong, editor

Published
2017
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107. LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-overlapping Cross-Domain Collaborative Filtering

Author

Zang, Yizhou, Hu, Xiaohua, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ceci, Michelangelo, editor, Hollmén, Jaakko, editor, Todorovski, Ljupčo, editor, Vens, Celine, editor, and Džeroski, Sašo, editor

Published
2017
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120. Perioperative Toripalimab Plus Chemotherapy for Patients With Resectable Non–Small Cell Lung Cancer: The Neotorch Randomized Clinical Trial.

Author

Lu, Shun, Zhang, Wei, Wu, Lin, Wang, Wenxiang, Zhang, Peng, Fang, Wentao, Xing, Wenqun, Chen, Qixun, Yang, Lin, Mei, Jiandong, Tan, Lijie, Sun, Xiaohong, Xu, Shidong, Hu, Xiaohua, Yu, Guohua, Yu, Dongliang, Yang, Nong, Chen, Yuping, Shan, Jinlu, and Xing, Ligang

Subjects
NON-small-cell lung carcinoma, IMMUNOTHERAPY, TERMINATION of treatment, POISONS, DRUG side effects, CANCER chemotherapy
Abstract

Key Points: Question: Will an immune checkpoint blocker in combination with perioperative chemotherapy improve event-free survival and major pathological response in patients with resectable non–small cell lung cancer (NSCLC)? Findings: The prespecified interim analysis of event-free survival revealed that the addition of toripalimab to platinum-based chemotherapy showed statistically significant and clinically meaningful improvement in event-free survival compared with chemotherapy alone in patients with stage III NSCLC, while maintaining a manageable safety profile. Meaning: The demonstrated event-free survival benefits support the use of toripalimab in combination with platinum-based chemotherapy as a new treatment option for patients with stage III resectable NSCLC. Importance: Adjuvant and neoadjuvant immunotherapy have improved clinical outcomes for patients with early-stage non–small cell lung cancer (NSCLC). However, the optimal combination of checkpoint inhibition with chemotherapy remains unknown. Objective: To determine whether toripalimab in combination with platinum-based chemotherapy will improve event-free survival and major pathological response in patients with stage II or III resectable NSCLC compared with chemotherapy alone. Design, Setting, and Participants: This randomized clinical trial enrolled patients with stage II or III resectable NSCLC (without EGFR or ALK alterations for nonsquamous NSCLC) from March 12, 2020, to June 19, 2023, at 50 participating hospitals in China. The data cutoff date for this interim analysis was November 30, 2022. Interventions: Patients were randomized in a 1:1 ratio to receive 240 mg of toripalimab or placebo once every 3 weeks combined with platinum-based chemotherapy for 3 cycles before surgery and 1 cycle after surgery, followed by toripalimab only (240 mg) or placebo once every 3 weeks for up to 13 cycles. Main Outcomes and Measures: The primary outcomes were event-free survival (assessed by the investigators) and the major pathological response rate (assessed by blinded, independent pathological review). The secondary outcomes included the pathological complete response rate (assessed by blinded, independent pathological review) and adverse events. Results: Of the 501 patients randomized, 404 had stage III NSCLC (202 in the toripalimab + chemotherapy group and 202 in the placebo + chemotherapy group) and 97 had stage II NSCLC and were excluded from this interim analysis. The median age was 62 years (IQR, 56-65 years), 92% of patients were male, and the median follow-up was 18.3 months (IQR, 12.7-22.5 months). For the primary outcome of event-free survival, the median length was not estimable (95% CI, 24.4 months-not estimable) in the toripalimab group compared with 15.1 months (95% CI, 10.6-21.9 months) in the placebo group (hazard ratio, 0.40 [95% CI, 0.28-0.57], P <.001). The major pathological response rate (another primary outcome) was 48.5% (95% CI, 41.4%-55.6%) in the toripalimab group compared with 8.4% (95% CI, 5.0%-13.1%) in the placebo group (between-group difference, 40.2% [95% CI, 32.2%-48.1%], P <.001). The pathological complete response rate (secondary outcome) was 24.8% (95% CI, 19.0%-31.3%) in the toripalimab group compared with 1.0% (95% CI, 0.1%-3.5%) in the placebo group (between-group difference, 23.7% [95% CI, 17.6%-29.8%]). The incidence of immune-related adverse events occurred more frequently in the toripalimab group. No unexpected treatment-related toxic effects were identified. The incidence of grade 3 or higher adverse events, fatal adverse events, and adverse events leading to discontinuation of treatment were comparable between the groups. Conclusions and Relevance: The addition of toripalimab to perioperative chemotherapy led to a significant improvement in event-free survival for patients with resectable stage III NSCLC and this treatment strategy had a manageable safety profile. Trial Registration: ClinicalTrials.gov Identifier: NCT04158440 This randomized clinical trial compares the effects of toripalimab in combination with platinum-based chemotherapy vs chemotherapy alone on event-free survival and major pathological response in patients with stage III resectable non–small cell lung cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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122. Can neurological soft signs and neurocognitive deficits serve as a combined endophenotype for Han Chinese with bipolar disorder?

Author

Feng, Yingying, Song, Jia, Lin, Guorong, Qian, Hong, Feng, Li, Wang, Zongqin, Wen, Juan, Wang, Chengchen, Wang, Jiayuan, Li, Peifu, Gao, Zuohui, Wang, Xiaoli, and Hu, Xiaohua

Subjects
CHINESE people, BIPOLAR disorder, COGNITIVE processing speed, MOTOR ability, VERBAL learning
Abstract

Background: Bipolar disorder's (BD) potential endophenotypes include neurological soft signs (NSS) and neurocognitive disorders (ND). Few research, meanwhile, has coupled NSS and ND as combined endophenotypes of BD. Object: This study intends to investigate NSS and ND and compare their differences in euthymic patients with bipolar disorder (EBP), their unaffected first‐degree relatives (FDR), and healthy controls (HC). Additionally, search for potential endophenotypic subprojects of NSS and ND and construct and verify a composite endophenotypic. Methods: The subjects were all Han Chinese and consisted of 86 EBP, 81 FDR, and 81HC. Cambridge Neurological Inventory and MATRICSTM Consensus Cognitive Battery tested NSS and ND independently. Results: All three groups displayed a trapezoidal distribution of NSS levels and cognitive abnormalities, with EBP having the most severe NSS levels and cognitive deficits, followed by FDR and HC. Among them, motor coordination in NSS and Information processing speed (IPS), Verbal learning (VL), and Working memory (WM) in neurocognitive function are consistent with the traits of the endophenotype of BD. The accuracy in differentiating EBP and HC or FDRs and HC was higher when these items were combined as predictor factors than in differentiating EBP and FDR. Conclusion: These results provide more evidence that motor coordination, IPS, VL, and WM may be internal characteristics of bipolar disease. When these characteristics are combined into a complex endophenotype, it may be possible to distinguish BD patients and high‐risk groups from normal populations. [ABSTRACT FROM AUTHOR]

Published
2023
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125. ProbitUCB: A Novel Method for Review Ranking

Author

Ding, Wanying, Shang, Yue, Park, Dae Hoon, Guo, Lifan, Hu, Xiaohua, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Xiao-Li, editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, and Cheung, David, editor

Published
2015
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132. Self-configured Entity Resolution with pyJedAI

Author

Sub Data Intensive Systems, Data Intensive Systems, He, Jingrui, Palpanas, Themis, Hu, Xiaohua, Cuzzocrea, Alfredo, Dou, Dejing, Slezak, Dominik, Wang, Wei, Gruca, Aleksandra, Lin, Jerry Chun-Wei, Agrawal, Rakesh, Efthymiou, Vasilis, Ioannou, Ekaterini, Karvounis, Manos, Koubarakis, Manolis, MacIejewski, Jakub, Nikoletos, Konstantinos, Papadakis, George, Skoutas, Dimitris, Velegrakis, Yannis, Zeakis, Alexandros, Sub Data Intensive Systems, Data Intensive Systems, He, Jingrui, Palpanas, Themis, Hu, Xiaohua, Cuzzocrea, Alfredo, Dou, Dejing, Slezak, Dominik, Wang, Wei, Gruca, Aleksandra, Lin, Jerry Chun-Wei, Agrawal, Rakesh, Efthymiou, Vasilis, Ioannou, Ekaterini, Karvounis, Manos, Koubarakis, Manolis, MacIejewski, Jakub, Nikoletos, Konstantinos, Papadakis, George, Skoutas, Dimitris, Velegrakis, Yannis, and Zeakis, Alexandros

Published
2023

133. Seasonal-Trend Time Series Decomposition on Graphics Processing Units

Author

He, Jingrui, Palpanas, Themis, Hu, Xiaohua, Cuzzocrea, Alfredo, Dou, Dejing, Slezak, Dominik, Wang, Wei, Gruca, Aleksandra, Lin, Jerry Chun-Wei, Agrawal, Rakesh, Serykh, Dmitry, Oehmcke, Stefan, Oancea, Cosmin, Masiliunas, Dainius, Verbesselt, Jan, Cheng, Yan, Horion, Stephanie, Gieseke, Fabian, Hinnerskov, Nikolaj, He, Jingrui, Palpanas, Themis, Hu, Xiaohua, Cuzzocrea, Alfredo, Dou, Dejing, Slezak, Dominik, Wang, Wei, Gruca, Aleksandra, Lin, Jerry Chun-Wei, Agrawal, Rakesh, Serykh, Dmitry, Oehmcke, Stefan, Oancea, Cosmin, Masiliunas, Dainius, Verbesselt, Jan, Cheng, Yan, Horion, Stephanie, Gieseke, Fabian, and Hinnerskov, Nikolaj

Abstract

In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.

Published
2023

135. Joint Analysis of Functional and Phylogenetic Composition for Human Microbiome Data

Author

Jiang, Xingpeng, Hu, Xiaohua, Xu, Weiwei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Basu, Mitra, editor, Pan, Yi, editor, and Wang, Jianxin, editor

Published
2014
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136. Hash-Based Stream LDA: Topic Modeling in Social Streams

Author

Slutsky, Anton, Hu, Xiaohua, An, Yuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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137. Document Clustering with an Augmented Nonnegative Matrix Factorization Model

Author

Xiong, Zunyan, Zang, Yizhou, Jiang, Xingpeng, Hu, Xiaohua, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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138. A Two-level Approach for Subtitle Alignment

Author

Huang, Jia, Ding, Hao, Hu, Xiaohua, Liu, Yong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, de Rijke, Maarten, editor, Kenter, Tom, editor, de Vries, Arjen P., editor, Zhai, ChengXiang, editor, de Jong, Franciska, editor, Radinsky, Kira, editor, and Hofmann, Katja, editor

Published
2014
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139. A New Decision Tree Classification Method for Mining High-Speed Data Streams Based on Threaded Binary Search Trees

Author

Wang, Tao, Li, Zhoujun, Hu, Xiaohua, Yan, Yuejin, Chen, Huowang, Carbonell, Jaime G., editor, Siekmann, J\'org, editor, Washio, Takashi, editor, Zhou, Zhi-Hua, editor, Huang, Joshua Zhexue, editor, Hu, Xiaohua, editor, Li, Jinyan, editor, Xie, Chao, editor, He, Jieyue, editor, Zou, Deqing, editor, Li, Kuan-Ching, editor, and Freire, Mário M., editor

Published
2007
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140. Subtask-Aware Representation Learning for Predicting Antibiotic Resistance Gene Properties via Gating-Controlled Mechanism

Author

Zhao, Weizhong, Wu, Junze, Luo, Shujie, Jiang, Xingpeng, He, Tingting, and Hu, Xiaohua

Abstract

The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction.

Published
2024
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142. Relation-Based Document Retrieval for Biomedical IR

Author

Zhou, Xiaohua, Hu, Xiaohua, Li, Guangren, Lin, Xia, Zhang, Xiaodan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, Hu, Xiaohua, editor, Pan, Yi, editor, and Lin, Tsau Young, editor

Published
2006
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143. Anti-parallel Coiled Coils Structure Prediction by Support Vector Machine Classification

Author

Huang, Zhong, Li, Yun, Hu, Xiaohua, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, Hu, Xiaohua, editor, Pan, Yi, editor, and Lin, Tsau Young, editor

Published
2006
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147. AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction

Author

Chen, Jiamin, Gao, Jianliang, Lyu, Tengfei, Oloulade, Babatounde Moctard, and Hu, Xiaohua

Abstract

It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure's features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics.

Published
2023
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148. CMAB009 plus irinotecan versus irinotecan-only as second-line treatment after fluoropyrimidine and oxaliplatin failure in KRAS wild-type metastatic colorectal cancer patients: promising findings from a prospective, open-label, randomized, phase III trial

Author

Shi, Yuankai, Li, Jin, Xu, Jianming, Sun, Yan, Wang, Liwei, Cheng, Ying, Liu, Wei, Sun, Guoping, Chen, Yigui, Bai, Li, Zhang, Yiping, He, Xiaohui, Luo, Yi, Wang, Zhehai, Liu, Yunpeng, Yao, Qiang, Li, Yuhong, Qin, Shukui, Hu, Xiaohua, Bi, Feng, Zheng, Rongsheng, and Ouyang, Xuenong

Published
2019
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