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3. Genomic and immune heterogeneity of multiple synchronous lung adenocarcinoma at different developmental stages

Author

Zhao, Yue, Gao, Jian, Wang, Jun, Fan, Fanfan, Cheng, Chao, Qian, Danwen, Guo, Ran, Zhang, Yang, Ye, Ting, Augustine, Marcellus, Lin, Yicong, Shang, Jun, Li, Hang, Pan, Yunjian, Huang, Qingyuan, Chen, Haiqing, Han, Han, Gao, Zhendong, Wang, Qiming, Zhang, Shiyue, Zhang, Mou, Fu, Fangqiu, Yan, Yueren, Fernandez Patel, Shanila, Vendramin, Roberto, Yuan, Hui, Zhang, Yawei, Xiang, Jiaqing, Hu, Hong, Sun, Yihua, Li, Yuan, Litchfield, Kevin, Cao, Zhiwei, and Chen, Haiquan

Published
2024
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5. Dynamic Graph Representation Learning for Depression Screening with Transformer

Author

Kuo, Ai-Te, Chen, Haiquan, Kuo, Yu-Hsuan, and Ku, Wei-Shinn

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval, Computer Science - Social and Information Networks
Abstract

Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental affliction. The recent proliferation of mental health discussions on social media platforms presents research opportunities to investigate mental health and potentially detect instances of mental illness. However, existing depression detection methods are constrained due to two major limitations: (1) the reliance on feature engineering and (2) the lack of consideration for time-varying factors. Specifically, these methods require extensive feature engineering and domain knowledge, which heavily rely on the amount, quality, and type of user-generated content. Moreover, these methods ignore the important impact of time-varying factors on depression detection, such as the dynamics of linguistic patterns and interpersonal interactive behaviors over time on social media (e.g., replies, mentions, and quote-tweets). To tackle these limitations, we propose an early depression detection framework, ContrastEgo treats each user as a dynamic time-evolving attributed graph (ego-network) and leverages supervised contrastive learning to maximize the agreement of users' representations at different scales while minimizing the agreement of users' representations to differentiate between depressed and control groups. ContrastEgo embraces four modules, (1) constructing users' heterogeneous interactive graphs, (2) extracting the representations of users' interaction snapshots using graph neural networks, (3) modeling the sequences of snapshots using attention mechanism, and (4) depression detection using contrastive learning. Extensive experiments on Twitter data demonstrate that ContrastEgo significantly outperforms the state-of-the-art methods in terms of all the effectiveness metrics in various experimental settings., Comment: 10 pages, 4 figures, 8 tables

Published
2023

6. Dynamic Modelling and Experimental Analysis of an Offshore Crane Payload Positioning System with a Parallel Cable-Driven Method

Author

Ren Zhaopeng, Huang Zhe, Zhao Tingqi, Wang Shenghai, Sun Yuqing, Chen Haiquan, and Fang Nan

Subjects
offshore cranes, payload positioning system, cable drive, dynamic analysis, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

The violent swinging motion of a payload imposes significant safety problems for the operation of offshore ship-mounted cranes (offshore cranes). We therefore propose a stable experimental hoisting platform for an offshore crane payload positioning system with a parallel cable-driven method (PP-PCDM), and an adaptive cable-drive anti-swing tension (ACAT) control method based on the PP-PCDM is developed to resolve the problem of swinging of the payload by limiting its spatial position. The PP-PCDM enables synchronous tracking of the movement of the payload when the crane is working. When the payload deviates from a stable state due to an external disturbance, the anti-swing cables are immediately retracted or released based on the feedback signal. The spatial position of the payload is then limited by adjusting the length and tension of the parallel cables until the payload appears stationary from the perspective of the ship’s deck. Operational safety and efficiency is substantially improved, and the proposed PP-PCDM structure and ACAT control method can be applied to a variety of different types of cranes. The results of simulations and physical experiments show that the anti-swing effect exceeds 89.86%. The PP-PCDM enables excellent performance of synchronous tracking and hoisting assistance, and ensures that the rated lifting weight of the offshore crane is not affected.

Published
2024
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9. Important progress in surgical treatment of lung cancer in 2023

Author

QIAN Bin, CHEN Haiquan

Subjects
lung cancer, research progress, sublobar resection, cure time window, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Lung cancer is the malignant tumor with the highest incidence and mortality rate in China, and surgical resection is a crucial approach for its treatment. In 2023, significant advancements were made in the field of lung cancer surgery. Firstly, CALGB140503 showed that for peripheral ≤2 cm nodular lung cancer, sublobar resection demonstrated no significant difference in survival rates compared to lobectomy, establishing the foundation for its role in early lung cancer treatment. Secondly, JCOG1211 found that segmentectomy could be used to treat 2-3 cm ground-glass opacity-predominant adenocarcinoma, however, patient in this study may undergo overtreatment, which could potentially be mitigated through intraoperative rapid frozen section pathological examination. Furthermore, the ECTOP-1003 study proposed a selective lymph node dissection strategy based on tumor location and intraoperative rapid frozen section pathological examination, achieving a 100% accuracy rate and providing a more precise approach to lymph node dissection. Additionally, the exploration of the cure time window for lung adenocarcinoma was remarkable, emphasizing the importance of timing in surgical intervention to avoid overtreatment in early lung cancer treatment. Finally, the ECTOP-1008 study demonstrated that high-resolution chest computed tomography (CT) scans assisted in determining the pathological invasion degree of lung cancer, aiding in the precise judgment of curative time window. This review summarized the important research progress in the field of lung cancer surgery in 2023, aiming to provide insights for clinical practice and future trials in lung cancer surgery.

Published
2024
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11. Neoadjuvant pembrolizumab plus chemotherapy followed by adjuvant pembrolizumab compared with neoadjuvant chemotherapy alone in patients with early-stage non-small-cell lung cancer (KEYNOTE-671): a randomised, double-blind, placebo-controlled, phase 3 trial

Author

Afanasyev, Sergey, Ahmed, Samreen, Alekshun, Todd, Alves, Gustavo, Anderson, Ian, Araujo, Luiz Henrique, Arkhipov, Alexander, Arora, Arvind, Bai, Jie, Begin, Paul, Belonogov, Aleksandr, Berard, Henri, Berceanu-Ion, Radu, Bernabe Caro, Reyes, Bondarenko, Igor, Bonnet, Reiner, Bosch Barrera, Joaquim, Brocca, Carlos, Bryl, Maciej, Bulotta, Alessandra, Bylicki, Olivier, Calles Blanco, Antonio, Carcereny, Enric, Carvalho, Leticia, Cebotaru, Cristina, Chaft, Jamie, Charu, Veena, Chaves, Fabio, Chen, Jun, Chen, Ke-Neng, Chen, Haiquan, Chen, Qixun, Chen, Kevin, Chiang, Chi-Lu, Chiu, Chao-Hua, Cicenas, Saulius, Ciubotaru, Elena, Ciuleanu, Tudor, Ciurescu, Ioana, Cobb, Patrick, Coetzee, Corlia, Collins, Dearbhaile, Cortinovis, Diego, Costas, Kimberly, Costin, Dan, Cronemberger, Eduardo Henrique, Cuevo, Raymund, Cuffe, Sinead, De Marchi, Pedro Rafael Martins, de Paiva Junior, Tadeu, Delmonte, Angelo, Demedts, Ingel, Deschepper, Koenraad, Dias, Josiane, Dooms, Christophe, Duchemann, Boris, Dutra, Carolina, Duvivier, Herbert, Eigendorff, Ekkehard, Ernani, Vinicius, Faehling, Martin, Faria, Luiza, Fedenko, Alexander, Fernando, Hiran, Ferrara, Roberto, Ferrari, Vittorio, Finley, Gene, Fix, Peter, Flores, Marcos, Fourie, Samuel, Franke, Fabio, Frohling, Klaus-Peter, Furqan, Muhammad, Gal, Cristian, Galamaga, Robert, Ganea, Doina, Ganti, Apar Kishor, Gao, Shugeng, Garassino, Marina, Gentzler, Ryan, Gianni, Luca, Gilli, Marina, Girard, Nicolas, Goranov, Bojidar, Gregorc, Vanesa, Greystoke, Alastair, Grisanti, Salvatore, Grohe, Christian, Guarino, Michael, Guimaraes, Jose Luiz, Guisier, Florian, Halmos, Balazs, Hammoud, Zane Taysir, Han, Ji-Youn, Hegmane, Alinta, Heng, Fook Yew, Horinouchi, Hidehito, Horio, Yoshitsugu, Hu, Jian, Huang, Hsu-Ching, Hui, Rina, Ikeda, Norihiko, Intagliata, Salvatore, Iordan, Ingrid, Jacobs, Conrad, Jain, Kirti, Jain, Sushil, Jiang, Tao, Karaseva, Nina, Kato, Terufumi, Kaywin, Paul, Kazmi, Shayma, Keresztes, Roger, Khan, Sarah, Kim, Jhingook, Kolesnik, Olena, Kolesnik, Oleksii, Kollmeier, Jens, Komiya, Takefumi, Koontz, Michael, Krasnohrud, Yuliia, Kristedja, Timothy, Kryzhanivska, Anna, Kuroda, Hiroaki, Laktionov, Konstantin, Lambrechts, Marc, Lang, Susanne, Langleben, Adrian, Lee, Se-Hoon, Lehmann, Markus, Levchenko, Evgeny, Levenko, Oleh, Li, Shanqing, Liao, Bin-Chi, Liberman, Moishe, Lima, Iane, Liu, Geoffrey, Liu-Dumlao, Theresa, Lo Russo, Giuseppe, Lou, Yan Yan, Lowczak, Anna, Luft, Alexander, Ma, Shaohua, Majem Tarruella, Margarita, Makles, Krytsyna, Martinengo, Gaston, Martinez Marti, Alex, Matias, Danielli, Mazieres, Julien, Mazilu, Laura, Mennecier, Bertrand, Migliorino, Maria Rita, Misleh, Jamal, Molina, Julian, Morbeck, Igor, Mueller, Annette, Muto, Satoshi, Nadal Alforja, Ernest, Nesterova, Alfiya, Nishio, Wataru, Niu, Jiaxin, Novello, Silvia, O'Brien, Mary, O'Day, Steven, Ogliari, Francesca, Okada, Morihito, Pang, Yong Kek, Paramonov, Viktor, Pastor, Andrea, Pawlak, Ireneusz, Piantedosi, Francovito, Pollock, Theodore, Pope, Tony, Puig, Juan, Radhi, Saba, Rao, Suman, Rathnasabapathy, Chenthilmurugan, Reck, Martin, Reinacher-Schick, Anke, Rich, Patricia, Ritgen, Mathias, Rittmeyer, Achim, Roca, Elisa, Rodriguez-Abreu, Delvys, Ruff, Paul, Rybkin, Igor, Saji, Hisashi, Sakao, Yukinori, Sangal, Ashish, Santoro, Armando, Sardenberg, Rodrigo, Savvides, Panayiotis, Scheusan, Roxana, Schiller, Joan, Schumacher, Lana, Serke, Monika, Shim, Byoung Yong, Shimizu, Junichi, Shio, Yutaka, Sibille, Anne, Siegel, Robert, Signorelli, Diego, Smagina, Maria, Sokur, Iryna, Spicer, Jonathan, Srkalovic, Gordan, Stampleman, Laura, Starodub, Alexander, Stencel, Katarzyna, Sugio, Kenji, Surmont, Veerle, Suzuki, Hiroyuki, Tabacof, Jacques, Takamochi, Kazuya, Tan, Lijie, Tanaka, Fumihiro, Tatangelo, Marcelo, Täuscher, Dagmar, Teixeira, Carlos, Thiberville, Luc, Trukhin, Dmytro, Tsai, Chen-Liang, Tsuboi, Masahiro, Ungureanu, Andrei, Ursol, Grygorii, Vanakesa, Tonu, Vansteenkiste, Johan, Varela, Mirta, Villalona-Calero, Miguel, Villaruz, Liza, Vogel, Gunther, Voitko, Nataliia, Wakelee, Heather, Wang, Qun, Wang, Wenxiang, Wang, Chin-Chou, Wang, Sarah, Wehler, Thomas, Weksler, Benny, Wermke, Martin, Wesseler, Claas, Wirtz, Hubert, Wong, Mark, Yan, Xiaolong, Yang, Yue, Yu, Kong Leong, Zasadny, Xavier, Zemaitis, Marius, Zhang, Lanjun, Zhao, Guofang, Zhao, Qing, Zhu, Yuming, Zurawski, Bogdan, Spicer, Jonathan D, Garassino, Marina C, Majem, Margarita, Martinengo, Gastón L, Rodríguez-Abreu, Delvys, Chaft, Jamie E, Yang, Jing, Arunachalam, Ashwini, Keller, Steven M, and Samkari, Ayman

Published
2024
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18. CACA guidelines for holistic integrative management of esophageal carcinoma

Author

Gong, Lei, Bai, Ming, Dai, Liang, Guo, Xufeng, Leng, Xuefeng, Li, Zhigang, Lu, Zhihao, Mao, Teng, Pang, Qingsong, Shen, Lin, Sun, Yifeng, Yang, Hong, Yin, Jun, Zhang, Hongdian, Zhang, Wencheng, Zhao, Gang, Zheng, Bin, Yang, Yueyang, Ba, Yi, Chen, Chun, Chen, Haiquan, Chen, Longqi, Deng, Ting, Fan, Qingxia, Fang, Wentao, Fu, Xiangning, Huang, Jing, Li, Xiaofei, Li, Yin, Liu, Shuoyan, Liu, Yongyu, Ma, Jianqun, Mao, Weimin, Ren, Jingli, Tan, Lijie, Tang, Peng, Wang, Lvhua, Wang, Qun, Wei, Wenqiang, Xue, Qi, Zhang, Renquan, Chen, Keneng, Fu, Jianhua, Liu, Junfeng, Han, Yongtao, He, Jie, Yu, Zhentao, and Mao, Yousheng

Published
2023
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19. Comparing a PD-L1 inhibitor plus chemotherapy to chemotherapy alone in neoadjuvant therapy for locally advanced ESCC: a randomized Phase II clinical trial: A randomized clinical trial of neoadjuvant therapy for ESCC

Author

Li, Yong, Zhou, Aiping, Liu, Shuoyan, He, Ming, Chen, Keneng, Tian, Ziqiang, Li, Yin, Qin, Jianjun, Wang, Zhen, Chen, Haiquan, Tian, Hui, Yu, Yue, Qu, Wang, Xue, Liyan, He, Shun, Wang, Shuhang, Bie, Fenglong, Bai, Guangyu, Zhou, Bolun, Yang, Zhaoyang, Huang, Huiyao, Fang, Yan, Li, Benjamin, Dai, Xiangrong, Gao, Shugeng, and He, Jie

Published
2023
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31. Clinicopathologic Features and Response to Therapy of NRG1 Fusion-Driven Lung Cancers: The eNRGy1 Global Multicenter Registry.

Author

Drilon, Alexander, Duruisseaux, Michael, Han, Ji-Youn, Ito, Masaoki, Falcon, Christina, Yang, Soo-Ryum, Murciano-Goroff, Yonina, Chen, Haiquan, Okada, Morihito, Molina, Miguel, Wislez, Marie, Brun, Philippe, Dupont, Clarisse, Branden, Eva, Rossi, Giulio, Schrock, Alexa, Ali, Siraj, Gounant, Valérie, Magne, Fanny, Blum, Torsten, Schram, Alison, Monnet, Isabelle, Shih, Jin-Yuan, Sabari, Joshua, Pérol, Maurice, Zhu, Viola, Nagasaka, Misako, Doebele, Robert, Camidge, D, Arcila, Maria, Ou, Sai-Hong, Moro-Sibilot, Denis, Rosell, Rafael, Muscarella, Lucia, Liu, Stephen, and Cadranel, Jacques

Subjects
Adult, Aged, Aged, 80 and over, Antineoplastic Combined Chemotherapy Protocols, Asia, Biomarkers, Tumor, Europe, Female, Follow-Up Studies, Humans, Immunotherapy, Lung Neoplasms, Male, Middle Aged, Neuregulin-1, Oncogene Proteins, Fusion, Prognosis, Registries, Retrospective Studies, Survival Rate, United States
Abstract

PURPOSE: Although NRG1 fusions are oncogenic drivers across multiple tumor types including lung cancers, these are difficult to study because of their rarity. The global eNRGy1 registry was thus established to characterize NRG1 fusion-positive lung cancers in the largest and most diverse series to date. METHODS: From June 2018 to February 2020, a consortium of 22 centers from nine countries in Europe, Asia, and the United States contributed data from patients with pathologically confirmed NRG1 fusion-positive lung cancers. Profiling included DNA-based and/or RNA-based next-generation sequencing and fluorescence in situ hybridization. Anonymized clinical, pathologic, molecular, and response (RECIST v1.1) data were centrally curated and analyzed. RESULTS: Although the typified never smoking (57%), mucinous adenocarcinoma (57%), and nonmetastatic (71%) phenotype predominated in 110 patients with NRG1 fusion-positive lung cancer, further diversity, including in smoking history (43%) and histology (43% nonmucinous and 6% nonadenocarcinoma), was elucidated. RNA-based testing identified most fusions (74%). Molecularly, six (of 18) novel 5 partners, 20 unique epidermal growth factor domain-inclusive chimeric events, and heterogeneous 5/3 breakpoints were found. Platinum-doublet and taxane-based (post-platinum-doublet) chemotherapy achieved low objective response rates (ORRs 13% and 14%, respectively) and modest progression-free survival medians (PFS 5.8 and 4.0 months, respectively). Consistent with a low programmed death ligand-1 expressing (28%) and low tumor mutational burden (median: 0.9 mutations/megabase) immunophenotype, the activity of chemoimmunotherapy and single-agent immunotherapy was poor (ORR 0%/PFS 3.3 months and ORR 20%/PFS 3.6 months, respectively). Afatinib achieved an ORR of 25%, not contingent on fusion type, and a 2.8-month median PFS. CONCLUSION: NRG1 fusion-positive lung cancers were molecularly, pathologically, and clinically more heterogeneous than previously recognized. The activity of cytotoxic, immune, and targeted therapies was disappointing. Further research examining NRG1-rearranged tumor biology is needed to develop new therapeutic strategies.

Published
2021

43. Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint

Author

Sainju, Arpan Man, He, Wenchong, Jiang, Zhe, Yan, Da, and Chen, Haiquan

Subjects
Computer Science - Machine Learning
Abstract

Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. Existing research mostly focuses on addressing incomplete or missing data, e.g., data cleaning and imputation, classification models that allow for missing feature values or model missing features as hidden variables in the EM algorithm. These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations. To address this issue, we recently proposed a new approach that incorporates physics-aware structural constraint into the model representation. Our approach assumes that a spatial contextual feature is observed for all sample locations and establishes spatial structural constraint from the underlying spatial contextual feature map. We design efficient algorithms for model parameter learning and class inference. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. We propose learning algorithms for the extended model with multi-modal distribution. Evaluations on real-world hydrological applications show that our approach significantly outperforms baseline methods in classification accuracy, and the multi-modal extension is more robust than our early single-modal version especially when feature distribution in training samples is multi-modal. Computational experiments show that the proposed solution is computationally efficient on large datasets.

Published
2020

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