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1. A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE

Author: Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, and Zheng C
Subjects: hepatocellular carcinoma, type ii diabetes mellitus, transarterial chemoembolization, overall survival, shapley additive explanations., Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Linxia Wu,1– 3,&ast; Lei Chen,1– 3,&ast; Lijie Zhang,1,4,&ast; Yiming Liu,1,5,&ast; Die Ouyang,1 Wenlong Wu,5,6 Yu Lei,5,6 Ping Han,1 Huangxuan Zhao,1– 3 Chuansheng Zheng1– 3 1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430022, People’s Republic of China; 2Department of Interventional Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430022, People’s Republic of China; 3Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, Hubei Province, 430022, People’s Republic of China; 4Department of Interventional Radiology, The Fifth Medical Center of Chinese, PLA General Hospital, Beijing, 100039, People’s Republic of China; 5Department of Interventional Radiology, Auto Valley Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430056, People’s Republic of China; 6Department of Interventional Radiology, Jinyinhu Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430048, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Chuansheng Zheng; Huangxuan Zhao, Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430022, People’s Republic of China, Email hqzcsxh@sina.com; zhao_huangxuan@sina.comPurpose: Type II diabetes mellitus (T2DM) has been found to increase the mortality of patients with hepatocellular carcinoma (HCC). Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE).Patients and Methods: The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center. Further, five predictive models were employed to establish prognosis models for 1-, 2-, and 3-year survival, respectively. Prediction performance was assessed by the receiver operating characteristic (ROC), calibration, and decision curve analysis curves. Lastly, the SHapley Additive exPlanations (SHAP) method was used to interpret the final ML model.Results: A total of 636 patients were included. Thirteen variables were selected for the model development. The final random survival forest (RSF) model exhibited excellent performance in the internal validation cohort, with areas under the ROC curve (AUCs) of 0.824, 0.853, and 0.810 in the 1-, 2-, and 3-year survival groups, respectively. This model also demonstrated remarkable discrimination in the external validation cohort, achieving AUCs of 0.862, 0.815, and 0.798 in the 1-, 2-, and 3-year survival groups, respectively. SHAP summary plots were also created to interpret the RSF model.Conclusion: An RSF model with good predictive performance was developed by incorporating simple parameters. This prognostic prediction model may assist physicians in early clinical intervention and improve prognosis outcomes in patients with HCC and comorbid T2DM after TACE.Keywords: hepatocellular carcinoma, type II diabetes mellitus, transarterial chemoembolization, overall survival, SHapley Additive exPlanations
Published: 2025

2. Exploring the Causal Relationship Between Migraine and Insomnia Through Bidirectional Two-Sample Mendelian Randomization: A Bidirectional Causal Relationship

Author: Ouyang D, Liu Y, and Xie W
Subjects: migraine, insomnia, bidirectional two-sample mendelian randomization, wide association studies, inverse variance weighted, Medicine (General), R5-920
Abstract: Di Ouyang,1 Yuhe Liu,2 Weiming Xie3 1Department of Neurology, Traditional Chinese Medicine Hospital of YuLin, Yulin, Guangxi, People’s Republic of China; 2Department of Orthopedics, Traditional Chinese Medicine Hospital of YuLin, Yulin, Guangxi, People’s Republic of China; 3Department of Basic Medicine, Guangxi Medical University, Nanning, Guangxi, People’s Republic of ChinaCorrespondence: Weiming Xie, Department of Basic Medicine, Guangxi Medical University, Nanning, GuangxiṢ, People’s Republic of China, Email xieweiming2008@126.comIntroduction: The intricate relationship between migraine and insomnia has been a subject of great interest due to its complex mechanisms. Despite extensive research, understanding the causal link between these conditions remains a challenge.Material and Methods: This study employs a bidirectional Mendelian randomization approach to investigate the causal relationship between migraine and insomnia. Risk loci for both conditions were derived from large-scale Genome-Wide Association Studies (GWAS). The primary method of Mendelian Randomization utilized in this study is the Inverse Variance Weighted (IVW) method.Results: Our findings indicate a bidirectional causal relationship between migraine and insomnia. In the discovery set, migraine had a significant effect on insomnia (OR=1.02, 95% CI=1.02 (1.01– 1.03), PIVW=5.30E-04). However, this effect was not confirmed in the validation set (OR=1.03, 95% CI=1.03 (0.87– 1.21), PIVW=0.77). Insomnia also had a significant effect on migraine (OR=1.02, 95% CI=1.02 (0.01– 1.03), PIVW=2.67E-08), and this effect was validated in the validation set (OR=2.30, 95% CI=2.30 (1.60– 3.30), PIVW=5.78E-06).Conclusion: This study provides meaningful insights into the bidirectional causality between migraine and insomnia, highlighting a complex interplay between these conditions. While our findings advance the understanding of the relationship between migraine and insomnia, they also open up new avenues for further research. The results underscore the need for considering both conditions in clinical and therapeutic strategies.Keywords: migraine, insomnia, bidirectional two-sample Mendelian randomization, wide association studies, inverse variance weighted
Published: 2024

3. Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository

Author: Ju G, Liu X, Yang W, Xu N, Chen L, Zhang C, He Q, Zhu X, and Ouyang D
Subjects: isoniazid, model-informed precision dosing, population pharmacokinetics, nonlinear mixed-effects model, Therapeutics. Pharmacology, RM1-950
Abstract: Gehang Ju,1– 3 Xin Liu,1– 3 Wenyu Yang,4 Nuo Xu,4 Lulu Chen,3,5 Chenchen Zhang,6 Qingfeng He,4 Xiao Zhu,4,&ast; Dongsheng Ouyang1– 3,5,&ast; 1Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China; 3Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 4Department of Clinical Pharmacy, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China; 5Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 6School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dongsheng Ouyang; Xiao Zhu, Email 801940@csu.edu.cn; xiaozhu@fudan.edu.cnIntroduction: Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use.Methods: A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca.Results: Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00– 200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults).Discussion: Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.Keywords: Isoniazid, model-informed precision dosing, population pharmacokinetics, nonlinear mixed-effects model
Published: 2024

4. Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method

Author: Liu X, Ju G, Yang W, Chen L, Xu N, He Q, Zhu X, and Ouyang D
Subjects: escitalopram, population pharmacokinetics, precision medicine, cyp2c19, Therapeutics. Pharmacology, RM1-950
Abstract: Xin Liu,1– 3 Gehang Ju,1– 3 Wenyu Yang,4 Lulu Chen,3,5,6 Nuo Xu,4 Qingfeng He,4 Xiao Zhu,4,&ast; Dongsheng Ouyang1– 3,5,&ast; 1Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Institute of Clinical Pharmacology, Central South University, Changsha, People’s Republic of China; 3Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 4Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, People’s Republic of China; 5Changsha Duxact Biotech Co., Ltd, Changsha, People’s Republic of China; 6Department of Pharmacy, Affiliated Hospital of Xiangnan University, Chenzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dongsheng Ouyang, Changsha Duxact Biotech Co., Ltd, Lutian Road 28, Changsha, 410221, People’s Republic of China, Tel +86 0731-84805380, Email ouyangyj@163.com Xiao Zhu, Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Zhangheng Road 826, Shanghai, 201203, People’s Republic of China, Tel +862151980024, Email xiaozhu@fudan.edu.cnAbstract: Escitalopram (SCIT) represents a first-line antidepressant and antianxiety medication. Pharmacokinetic studies of SCIT have demonstrated considerable interindividual variability, emphasizing the need for personalized dosing. Accordingly, we aimed to create a repository of parametric population pharmacokinetic (PPK) models of SCIT to facilitate model-informed precision dosing. In November 2022, we searched PubMed, Embase, and Web of Science for published PPK models and identified eight models. All the structural models reported in the literature were either one- or two-compartment models. In order to investigate the variances in model performance, the parameters of all PPK models were derived from the literature published. A representative virtual population, characterized by an age of 30, a body weight of 70 kg, and a BMI of 23 kg/m2, was generated for the purpose of replicating these models. To accomplish this, the rxode2 package in the R programming language was employed. Subsequently, we compared simulated concentration–time profiles and evaluated the impact of covariates on clearance. The most significant covariates were CYP2C19 phenotype, weight, and age, indicating that dosing regimens should be tailored accordingly. Additionally, among Chinese psychiatric patients, SCIT showed nearly double the exposure compared to other populations, specifically when considering the same CYP2C19 population restriction, which is a knowledge gap that needs further investigation. Furthermore, this repository of parametric PPK models for SCIT has a wide range of potential applications, like design miss or delay dose remedy strategies and external PPK model validation.Keywords: escitalopram, population pharmacokinetics, precision medicine, CYP2C19
Published: 2023

5. Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering

Author: Che, H, Li, C, Leung, M-F, Ouyang, D, Dai, X, Wen, S, Che, H, Li, C, Leung, M-F, Ouyang, D, Dai, X, and Wen, S
Published: 2024

6. Graphene quantum dots-gated hollow mesoporous carbon nanoplatform for targeting drug delivery and synergistic chemo-photothermal therapy

Author: Fang J, Liu Y, Chen Y, Ouyang D, Yang G, and Yu T
Subjects: Hollow mesoporous carbon nanoparticles, graphene quantum dots, CD44 receptor targeting, chemo-photothermal therapy, Medicine (General), R5-920
Abstract: Junfeng Fang, Yanqing Liu, Yiwen Chen, Dimei Ouyang, Guangji Yang, Tao Yu Department of Gynecology, The First People’s Hospital of Yunnan Province, Kunming University of Science and Technology, Kunming, P.R. China Background: Carbon-based drug delivery systems have attracted great interest because of their excellent photothermal conversion capability and high specific surface area for drug loading. Herein, we report a multifunctional nanoplatform based on hyaluronic acid (HA)-modified and graphene quantum dot (GQD)-gated hollow mesoporous carbon nanoparticle (HMCN) for anticancer drug encapsulation and targeted chemo-photothermal therapy of CD44 receptor-overexpressed cancer cells.Methods: In this design, HMCN was not only used as a nanocarrier with high drug loading content to achieve chemotherapy, but also as a near-infrared absorbing agent to realize photothermal therapy. GQDs could not only prevent premature drug release during blood circulation, but also enhance the chemo-photothermal therapeutic efficacy for complete tumor growth suppression. After being modified with HA, the HA-HMCN(DOX)@GQDs could specifically target cancer cells. Results: As expected, the as-prepared HMCN exhibited high doxorubicin (DOX)-loading capacity of 410 mg/g and excellent light-to-heat conversion property. The DOX was released from HA-HMCN(DOX)@GQDs in a near-infrared laser and pH stimuli-responsive manner, which could enhance the therapeutic effect. In vitro cell biological experimental results confirmed that the nanoplatform possesses excellent biocompatibility, specifically target CD44 receptor-overexpressing human cervical carcinoma HeLa cells, and has remarkable synergistic chemo-photothermal killing capacity. The in vivo therapeutic studies in HeLa xenografts also showed negligible toxicity of HA-HMCN@GQDs and complete inhibition of tumor growth of HA-HMCN(DOX) @GQDs with near-infrared irradiation.Conclusion: The excellent therapeutic effects demonstrated in vitro and in vivo suggested the HMCN-based nanoplatform holds potential for efficient dual-responsive targeting drug delivery and synergistic chemo-photothermal therapy. Keywords: hollow mesoporous carbon nanoparticles, graphene quantum dots, CD44 receptor targeting, chemo-photothermal therapy
Published: 2018

7. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography

Author: Oikonomou, E K, primary, Holste, G, additional, Mcnamara, R L, additional, Velazquez, E J, additional, Nadkarni, G N, additional, Ouyang, D, additional, Krumholz, H M, additional, Wang, Z, additional, and Khera, R, additional
Published: 2023
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8. First Report of Fruit Rot Caused by Fusarium graminearum on Ponkan (Citrus reticulata cv. Ponkan) in China

Author: Wang, Y. B., primary, Kang, N. H., additional, Yang, X. Z., additional, Ouyang, D. M., additional, Wu, H., additional, Chen, J. Y., additional, Xiang, M. L., additional, and Chen, M., additional
Published: 2023
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9. First Report of Black Spot Disease Caused by Alternaria alternata on Persimmon Fruit in China

Author: Xiang, M. L., primary, Wang, Y. B., additional, Wang, D. H., additional, Fu, Y. Q., additional, Zeng, J. K., additional, Ouyang, D. M., additional, Chen, J. Y., additional, and Chen, M., additional
Published: 2023
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10. Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

Author: Zhang, D, Ouyang, D, Shu, L, Hu, C, Shi, K, Wen, S, Zhang, D, Ouyang, D, Shu, L, Hu, C, Shi, K, and Wen, S
Abstract: This paper concerns with the event-based dynamic output feedback control for the synchronization of fuzzy neural networks under mixed delay and deception attacks. A weighted sum-based dynamic event-triggered mechanism (WSDETM) is developed to save the communication resources while preserving a satisfactory system performance. A dynamic output feedback controller (DOFC) is designed to achieve exponential synchronization of fuzzy neural networks. To reduce the data traffic, both communication channels from the sensor to DOFC and DOFC to Zero-Order Holder are subject to WSDETM. Different from the traditional deception attacks modeled by Bernoulli process, we adopt the more general Markov process modeling deception attacks. By using the cone-complimentarity linearization algorithm, the DOFC and WSDETM parameters are carried out. The effectiveness of the proposed method is demonstrated with two numerical cases.
Published: 2023

11. When hierarchy meets 2-hop-labeling: efficient shortest distance and path queries on road networks

Author: Ouyang, D, Wen, D, Qin, L, Chang, L, Lin, X, Zhang, Y, Ouyang, D, Wen, D, Qin, L, Chang, L, Lin, X, and Zhang, Y
Abstract: Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra’s algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hop-based solutions. However, the hierarchy-based solutions require large search space for long-distance queries, while the hop-based solutions result in high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an efficient query processing algorithm with performance guarantees by visiting part of the labels for the source and the destination based on the hierarchy. We propose a novel algorithm to construct the H2H-Indexbased on distance preserved graphs. We also extend the H2H-Indexand propose a set of algorithms to identify the shortest path between vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.
Published: 2023

12. Euglycemic progression: Worsening of diabetic retinopathy in poorly controlled type 2 diabetes in minorities

Author: Shurter, A., Genter, P., Ouyang, D., and Ipp, E.
Published: 2013
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13. The Influence of the COVID-19 Pandemic on Author Gender and Manuscript Acceptance Rates Among Pulmonary and Critical Care Journals

Author: Vranas, K.C., primary, Gershengorn, H.B., additional, Ouyang, D., additional, Cheng, S., additional, Rogers, A., additional, Schweiger, L., additional, Cooke, C.R., additional, and Slatore, C.G., additional
Published: 2022
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14. P366: Evaluating the clinical applicability of nanoparticle-assisted axillary staging to predict the status of patients with nodepositive breast cancer following NAC: a prospective diagnostic study.

Author: Chen, R., Ouyang, D., and Chen, J
Published: 2025
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15. Design and optimization of a fast steering mirror driven by voice coil motor

Author: Fan, S. X., primary, Ouyang, D., additional, Chen, N., additional, Yu, Y., additional, and Fan, D. P., additional
Published: 2022
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16. Elucidation of Master Allostery Essential for Circadian Clock Oscillation in Cyanobacteria

Author: Furuike, Y., primary, Mukaiyama, A., additional, Ouyang, D., additional, Ito-Miwa, K., additional, Simon, D., additional, Yamashita, E., additional, Kondo, T., additional, and Akiyama, S., additional
Published: 2021
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17. The Efficacy of Pyrotinib as a Third- or Higher-Line Treatment in HER2-Positive Metastatic Breast Cancer Patients Exposed to Lapatinib Compared to Lapatinib-Naive Patients: A Real-World Study

Author: Ouyang, D. J, primary, Chen, Q. T, additional, Anwar, M., additional, Xie, N., additional, Ouyang, Q. C., additional, Fan, P. Z., additional, Qian, L. Y., additional, Chen, G. N., additional, Zhou, E. X., additional, Guo, L., additional, Gu, X. W., additional, Ding, B. N., additional, Yang, X. H., additional, Liu, L. P., additional, Deng, C., additional, Xiao, Z., additional, Li, J., additional, Wang, Y. Q., additional, Zeng, S., additional, Wang, Shouman, additional, and Yi, Wenjun, additional
Published: 2021
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18. Finite-time stability of coupled impulsive neural networks with time-varying delays and saturating actuators

Author: Ouyang, D, Shao, J, Jiang, H, Wen, S, Nguang, SK, Ouyang, D, Shao, J, Jiang, H, Wen, S, and Nguang, SK
Abstract: The paper considers the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time. Based on a delayed state feedback controller, the stability of coupled impulsive neural networks with time-varying delays and saturating actuators can be achieved in finite time. Combined with Lyapunov-based finite-time stability theory, some sufficient conditions are obtained to ensure the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time by using polytopic representation approach and sector nonlinearity model approach, respectively. Moreover, the setting time of coupled impulsive neural networks with saturating actuators is given, and it is found to be related to both the initial state and impulse effect. Furthermore, as special cases, some finite-time stability results of coupled impulsive neural networks with saturating actuators are given under a memoryless controller. Finally, two simulation examples are used to test the effectiveness of the obtained results.
Published: 2021

19. Linewidth enhancement factor in InGaAs quantum-dot amplifiers

Author: Schneider, Stephan, Borri, Paola, Langbein, Wolfgang, Woggon, Ulrike, Sellin, Roman L., Ouyang, D., and Bimberg, Dieter
Subjects: Spectrum analysis -- Methods, Semiconductor lasers -- Electric properties, Amplifiers (Electronics) -- Properties, Business, Computers, Electronics, Electronics and electrical industries
Abstract: Systematic measurements of the linewidth enhancement factor (LEF) in an electrically pumped InGaAs quantum-dot (QD) amplifier in the temperature range from 50 K to room temperature were reported. The results suggest that for low-injection currents, the LEF of the dot ground-state (GS) transition is between 0.4 and 1.
Published: 2004

20. Hyperammonemic coma in an ornithine transcarbamylase mutation carrier following antepartum corticosteroids

Author: Lipskind, S, Loanzon, S, Simi, E, and Ouyang, D W
Published: 2011
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21. Influence of Freezing Treatment on the Thermal Runaway Characteristics of Lithium-Ion Batteries

Author: Wweng, J, primary, Ouyang, D X, additional, Chen, MY, additional, Ding, C, additional, and Wang, J, additional
Published: 2020
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22. GITR agonist sensitizes MC38/OVA tumor to CTLA4 treatment by attenuating Tregs in GITR HuGEMM

Author: He, D.X., primary, Nie, C., additional, Zheng, L., additional, An, A.X., additional, Li, H.Q.X., additional, and Ouyang, D., additional
Published: 2020
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23. Benchmarking syngeneic tumor models to explore immunopathogenesis and anti-PD1 mechanism of action

Author: An, A.X., primary, Jin, Y., additional, Mao, B., additional, Ouyang, D., additional, and Li, H., additional
Published: 2020
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24. Availability of banded triple superphosphate with urea and phosphorus use efficiency by corn

Author: Ouyang, D. S., MacKenzie, A. F., and Fan, M. X.
Published: 1999
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25. Clopidogrel Inhibits CYP2C19-Dependent Hydroxylation of Omeprazole Related to CYP2C19 Genetic Polymorphisms

Author: Chen, B. L., Chen, Y., Tu, J. H., Li, Y. L., Zhang, W., Li, Q., Fan, L., Tan, Z. R., Hu, D. L., Wang, D., Wang, L. S., OuYang, D. S., and Zhou, H. H.
Published: 2009

26. Low serum betaine levels as a potential biomarker for type 2 diabetes mellitus in patients with coronary artery disease

Author: Guo F, Ouyang D, Qiu X, Tan Z, Li Z, and Zhu Y
Subjects: medicine.medical_specialty, business.industry, Type 2 Diabetes Mellitus, medicine.disease, Gastroenterology, Coronary artery disease, chemistry.chemical_compound, Text mining, Betaine, chemistry, Potential biomarkers, Internal medicine, medicine, In patient, business
Abstract: The authors have withdrawn this preprint from Research Square.
Published: 2020
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27. Lamivudine prophylaxis for chemotherapy-induced reactivation of chronic hepatitis B virus infection

Author: KOHRT, H. E., OUYANG, D. L., and KEEFFE, E. B.
Published: 2006

28. Speeding Up GED Verification for Graph Similarity Search

Author: Chang, L, Feng, X, Lin, X, Qin, L, Zhang, W, Ouyang, D, Chang, L, Feng, X, Lin, X, Qin, L, Zhang, W, and Ouyang, D
Abstract: Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar+-LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar+-LSa has a lower space and time complexity than A*GED. We further modify AStar+-LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar+-LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.
Published: 2020

29. Progressive Top-K Nearest Neighbors Search in Large Road Networks

Author: Maier, D, Pottinger, R, Doan, A, Tan, W-C, Alawini, A, Ngo, HQ, Ouyang, D, Wen, D, Qin, L, Chang, L, Zhang, Y, Lin, X, Maier, D, Pottinger, R, Doan, A, Tan, W-C, Alawini, A, Ngo, HQ, Ouyang, D, Wen, D, Qin, L, Chang, L, Zhang, Y, and Lin, X
Abstract: © 2020 Association for Computing Machinery. Computing top-k nearest neighbors (kNN) is a fundamental problem in road networks. Existing solutions either need a complicated parameter configuration in index construction or incur high costs when scanning an unbounded number of vertices in query processing. In this paper, we propose a novel parameter-free index-based solution for the kNN query based on the concept of tree decomposition in large road networks. Based on our index structure, we propose an efficient and progressive algorithm that returns each result in a bounded delay. We also optimize the index structure, which improves the efficiency of both index construction and index maintenance in large road networks. We conduct extensive experiments to show the efficiency of our proposed algorithms and the effectiveness of our optimization techniques in real-world road networks from ten regions.
Published: 2020

30. Efficient shortest path index maintenance on dynamic road networks with theoretical guarantees

Author: Ouyang, D, Yuan, L, Qin, L, Chang, L, Zhang, Y, Lin, X, Ouyang, D, Yuan, L, Qin, L, Chang, L, Zhang, Y, and Lin, X
Abstract: Computing the shortest path between two vertices is a fundamental problem in road networks that is applied in a wide variety of applications. To support efficient shortest path query processing, a plethora of index-based methods have been proposed in the literature, but few of them can support dynamic road networks commonly encountered in practice, as their corresponding index structures cannot be efficiently maintained when the input road network is dynamically updated. Motivated by this, we study the shortest path index maintenance problem on dynamic road networks in this paper. We adopt Contraction Hierarchies (CH) as our underlying shortest path computation method because of its outstanding overall performance in pre-processing time, space cost, and query processing time and aim to design efficient algorithms to maintain the index structure, shortcut index, of CH when the input road network is dynamically updated. To achieve this goal, we propose a shortcut-centric paradigm focusing on exploring a small number of shortcuts to maintain the shortcut index. Following this paradigm, we design an auxiliary data structure named SS-Graph and propose a shortcut weight propagation mechanism based on the SS-Graph. With them, we devise efficient algorithms to maintain the shortcut index in the streaming update and batch update scenarios with non-trivial theoretical guarantees. We experimentally evaluate our algorithms on real road networks and the results demonstrate that our approach achieves 2-3 orders of magnitude speedup compared to the state-of-the-art algorithm for the streaming update.
Published: 2020

31. 42P The establishment of a large tumor organoid biobank using a well characterized/annotated patient-derived xenograft (PDX) library to enable drug discovery and translational research

Author: Bourre, L., primary, Xu, X., additional, Shang, L., additional, Wang, L., additional, Li, C., additional, Liu, Y., additional, Han, P., additional, Sun, Z., additional, Qu, Y., additional, Zhang, L., additional, Chen, B., additional, Ouyang, D., additional, Huang, Y., additional, and Li, H., additional
Published: 2020
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32. Characterizing Inpatient Medicine Resident Electronic Health Record Usage Patterns Using Event Log Data

Author: Ouyang D, Chi J, Chen Jh, Hom J, and Wang Jk
Subjects: 020205 medical informatics, business.industry, Event (computing), health care facilities, manpower, and services, Psychological intervention, 02 engineering and technology, Burnout, EPIC, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Workflow, Chart, Electronic health record, Log data, health services administration, 0202 electrical engineering, electronic engineering, information engineering, Medicine, 030212 general & internal medicine, Medical emergency, business, health care economics and organizations
Abstract: Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow.To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016.Logged EHR timestamps were extracted from Stanford Hospital’s EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift’s end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation=2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.
Published: 2018
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33. When Hierarchy meets 2-hop-labeling: Effiicient shortest distance ?eries on road networks

Author: Ouyang, D, Qin, L, Chang, L, Lin, X, Zhang, Y, and Zhu, Q
Abstract: © 2018 Association for Computing Machinery. Computing the shortest distance between two vertices is a fundamental problem in road networks. Since a direct search using the Dijkstra's algorithm results in a large search space, researchers resort to indexing-based approaches. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hopbased solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. To overcome the drawbacks of both solutions, in this paper, we propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an e?cient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can achieve a speedup of an order of magnitude in query processing compared to the state-of-the-art while consuming comparable indexing time and index size.
Published: 2018

34. Investigation of the mechanism of action of anti-PD-1 treatment by systematic depletion of different immune cell populations in syngeneic models

Author: Bourre, L., primary, Jin, Y., additional, Muntel, J., additional, Yu, H., additional, Beeler, K., additional, Bruderer, R., additional, Shan, Y., additional, An, A. Xiaoyu, additional, Ouyang, D. Xuesong, additional, and Li, H. Qixiang, additional
Published: 2019
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35. Polarization doping technology towards high performance GaN-based heterostructure devices

Author: Wei, J, primary, Ouyang, D F, additional, Deng, S Y, additional, Peng, F, additional, Yang, C, additional, and Luo, X R, additional
Published: 2019
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36. Video-based person re-identification via self-paced learning and deep reinforcement learning framework

Author: Ouyang, D, Shao, J, Zhang, Y, Yang, Y, Shen, HT, Ouyang, D, Shao, J, Zhang, Y, Yang, Y, and Shen, HT
Abstract: © 2018 Association for Computing Machinery. Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.
Published: 2018

37. Towards efficient path skyline computation in bicriteria networks

Author: Ouyang, D, Yuan, L, Zhang, F, Qin, L, Lin, X, Ouyang, D, Yuan, L, Zhang, F, Qin, L, and Lin, X
Abstract: © Springer International Publishing AG, part of Springer Nature 2018. Path skyline query is a fundamental problem in bicriteria network analysis and is widely applied in a variety of applications. Given a source s and a destination t in a bicriteria network G, path skyline query aims to identify all the skyline paths from s to t in G. In the literature, PSQ is a fundamental algorithm for path skyline query and is also used as a building block for the afterwards proposed algorithms. In PSQ, a key operation is to record the skyline paths from s to v for each node v that is possible on the skyline paths from s to t. However, to obtain the skyline paths for v, PSQ has to maintain other paths that are not skyline paths for v, which makes PSQ inefficient. Motivated by this, in this paper, we propose a new algorithm PSQ+ for the path skyline query. By adopting an ordered path exploring strategy, our algorithm can totally avoid the fruitless path maintenance problem in PSQ. We evaluate our proposed algorithm on real networks and the experimental results demonstrate the efficiency of our proposed algorithm. Besides, the experimental results also demonstrate the algorithm that uses PSQ as a building block for the path skyline query can achieve a significant performance improvement after we substitute PSQ+ for PSQ.
Published: 2018

38. IL-22-induced antimicrobial peptides are key determinants of mucosal vaccine-induced protection against H. pylori in mice

Author: Moyat, M, Bouzourene, H, Ouyang, W, Iovanna, J., Renauld, J-C, Velin, D, Moyat, D, Ouyang, D, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), UNISciences, Genentech, Inc., Genentech, Inc. [San Francisco], Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Ludwig Institute for Cancer Research, and UCL - SSS/DDUV - Institut de Duve
Subjects: 0301 basic medicine, MESH: Th17 Cells, Pancreatitis-Associated Proteins, MESH: Mice, Knockout, Mice, 0302 clinical medicine, Immunology and Allergy, MESH: Animals, MESH: Proteins, Helicobacter, Cells, Cultured, Mice, Knockout, Mice, Inbred BALB C, biology, MESH: Antimicrobial Cationic Peptides, Interleukin, Urease, MESH: Gene Expression Regulation, 3. Good health, Bacterial vaccine, Bacterial Vaccines, 030211 gastroenterology & hepatology, Female, Antibody, MESH: Cells, Cultured, MESH: Interleukins, Immunology, MESH: Urease, MESH: Mice, Inbred BALB C, Helicobacter Infections, 03 medical and health sciences, Antigen, Immunity, MESH: Mice, Inbred C57BL, Animals, Humans, MESH: Pancreatitis-Associated Proteins, MESH: Mice, Antigens, Bacterial, Mucous Membrane, MESH: Humans, Helicobacter pylori, Interleukins, MESH: Helicobacter Infections, Proteins, MESH: Mucous Membrane, biology.organism_classification, Mice, Inbred C57BL, Disease Models, Animal, 030104 developmental biology, Mucosal immunology, Gene Expression Regulation, biology.protein, Th17 Cells, MESH: Helicobacter pylori, MESH: Disease Models, Animal, [SDV.IMM.VAC]Life Sciences [q-bio]/Immunology/Vaccinology, MESH: Female, MESH: Antigens, Bacterial, MESH: Bacterial Vaccines, Antimicrobial Cationic Peptides
Abstract: International audience; Despite the recent description of the mucosal vaccine-induced reduction of Helicobacter pylori natural infection in a phase 3 clinical trial, the absence of immune correlates of protection slows the final development of the vaccine. In this study, we evaluated the role of interleukin (IL)-22 in mucosal vaccine-induced protection. Gastric IL-22 levels were increased in mice intranasally immunized with urease þ cholera toxin and challenged with H. felis, as compared with controls. Flow cytometry analysis showed that a peak of CD4 þ IL-22 þ IL-17 þ Tcells infiltrating the gastric mucosa occurred in immunized mice in contrast to control mice. The inhibition of the IL-22 biological activity prevented the vaccine-induced reduction of H. pylori infection. Remarkably, anti-microbial peptides (AMPs) extracted from the stomachs of vaccinated mice, but not from the stomachs of non-immunized or immunized mice, injected with anti-IL-22 antibodies efficiently killed H. pylori in vitro. Finally, H. pylori infection in vaccinated RegIIIb-deficient mice was not reduced as efficiently as in wild-type mice. These results demonstrate that IL-22 has a critical role in vaccine-induced protection, by promoting the expression of AMPs, such as RegIIIb, capable of killing Helicobacter. Therefore, it can be concluded that urease-specific memory Th17/Th22 cells could constitute immune correlates of vaccine protection in humans.
Published: 2017
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39. 207 Poster - Benchmarking syngeneic tumor models to explore immunopathogenesis and anti-PD1 mechanism of action

Author: An, A.X., Jin, Y., Mao, B., Ouyang, D., and Li, H.
Published: 2020
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40. 29 Poster Discussion - GITR agonist sensitizes MC38/OVA tumor to CTLA4 treatment by attenuating Tregs in GITR HuGEMM

Author: He, D.X., Nie, C., Zheng, L., An, A.X., Li, H.Q.X., and Ouyang, D.
Published: 2020
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41. Building Kras (G12D)/Trp53 null/Pdx1-cre (KPC) mouse allograft tumor model for efficacy evaluation of combinatory immunotherapie

Author: Song, Y., primary, Liu, B., additional, Xiaoyu An, A., additional, Zhang, L., additional, Cai, J., additional, Shi, Q., additional, Qixiang Li, H., additional, and Xuesong Ouyang, D., additional
Published: 2018
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42. Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

Author: Klionsky, D.J. Abdelmohsen, K. Abe, A. Abedin, M.J. Abeliovich, H. Arozena, A.A. Adachi, H. Adams, C.M. Adams, P.D. Adeli, K. Adhihetty, P.J. Adler, S.G. Agam, G. Agarwal, R. Aghi, M.K. Agnello, M. Agostinis, P. Aguilar, P.V. Aguirre-Ghiso, J. Airoldi, E.M. Ait-Si-Ali, S. Akematsu, T. Akporiaye, E.T. Al-Rubeai, M. Albaiceta, G.M. Albanese, C. Albani, D. Albert, M.L. Aldudo, J. Algül, H. Alirezaei, M. Alloza, I. Almasan, A. Almonte-Beceril, M. Alnemri, E.S. Alonso, C. Altan-Bonnet, N. Altieri, D.C. Alvarez, S. Alvarez-Erviti, L. Alves, S. Amadoro, G. Amano, A. Amantini, C. Ambrosio, S. Amelio, I. Amer, A.O. Amessou, M. Amon, A. An, Z. Anania, F.A. Andersen, S.U. Andley, U.P. Andreadi, C.K. Andrieu-Abadie, N. Anel, A. Ann, D.K. Anoopkumar-Dukie, S. Antonioli, M. Aoki, H. Apostolova, N. Aquila, S. Aquilano, K. Araki, K. Arama, E. Aranda, A. Araya, J. Arcaro, A. Arias, E. Arimoto, H. Ariosa, A.R. Armstrong, J.L. Arnould, T. Arsov, I. Asanuma, K. Askanas, V. Asselin, E. Atarashi, R. Atherton, S.S. Atkin, J.D. Attardi, L.D. Auberger, P. Auburger, G. Aurelian, L. Autelli, R. Avagliano, L. Avantaggiati, M.L. Avrahami, L. Azad, N. Awale, S. Bachetti, T. Backer, J.M. Bae, D.-H. Bae, J.-S. Bae, O.-N. Bae, S.H. Baehrecke, E.H. Baek, S.-H. Baghdiguian, S. Bagniewska-Zadworna, A. Bai, H. Bai, J. Bai, X.-Y. Bailly, Y. Balaji, K.N. Balduini, W. Ballabio, A. Balzan, R. Banerjee, R. Bánhegyi, G. Bao, H. Barbeau, B. Barrachina, M.D. Barreiro, E. Bartel, B. Bartolomé, A. Bassham, D.C. Bassi, M.T. Bast, R.C., Jr. Basu, A. Batista, M.T. Batoko, H. Battino, M. Bauckman, K. Baumgarner, B.L. Bayer, K.U. Beale, R. Beaulieu, J.-F. Beck, G.R., Jr. Becker, C. Beckham, J.D. Bédard, P.-A. Bednarski, P.J. Begley, T.J. Behl, C. Behrends, C. Behrens, G.M.N. Behrns, K.E. Bejarano, E. Belaid, A. Belleudi, F. Bénard, G. Berchem, G. Bergamaschi, D. Bergami, M. Berkhout, B. Berliocchi, L. Bernard, A. Bernard, M. Bernassola, F. Bertolotti, A. Bess, A.S. Besteiro, S. Bettuzzi, S. Bhalla, S. Bhattacharyya, S. Bhutia, S.K. Biagosch, C. Bianchi, M.W. Biard-Piechaczyk, M. Billes, V. Bincoletto, C. Bingol, B. Bird, S.W. Bitoun, M. Bjedov, I. Blackstone, C. Blanc, L. Blanco, G.A. Blomhoff, H.K. Boada-Romero, E. Böckler, S. Boes, M. Boesze-Battaglia, K. Boise, L.H. Bolino, A. Boman, A. Bonaldo, P. Bordi, M. Bosch, J. Botana, L.M. Botti, J. Bou, G. Bouché, M. Bouchecareilh, M. Boucher, M.-J. Boulton, M.E. Bouret, S.G. Boya, P. Boyer-Guittaut, M. Bozhkov, P.V. Brady, N. Braga, V.M.M. Brancolini, C. Braus, G.H. Bravo-San-Pedro, J.M. Brennan, L.A. Bresnick, E.H. Brest, P. Bridges, D. Bringer, M.-A. Brini, M. Brito, G.C. Brodin, B. Brookes, P.S. Brown, E.J. Brown, K. Broxmeyer, H.E. Bruhat, A. Brum, P.C. Brumell, J.H. Brunetti-Pierri, N. Bryson-Richardson, R.J. Buch, S. Buchan, A.M. Budak, H. Bulavin, D.V. Bultman, S.J. Bultynck, G. Bumbasirevic, V. Burelle, Y. Burke, R.E. Burmeister, M. Bütikofer, P. Caberlotto, L. Cadwell, K. Cahova, M. Cai, D. Cai, J. Cai, Q. Calatayud, S. Camougrand, N. Campanella, M. Campbell, G.R. Campbell, M. Campello, S. Candau, R. Caniggia, I. Cantoni, L. Cao, L. Caplan, A.B. Caraglia, M. Cardinali, C. Cardoso, S.M. Carew, J.S. Carleton, L.A. Carlin, C.R. Carloni, S. Carlsson, S.R. Carmona-Gutierrez, D. Carneiro, L.A.M. Carnevali, O. Carra, S. Carrier, A. Carroll, B. Casas, C. Casas, J. Cassinelli, G. Castets, P. Castro-Obregon, S. Cavallini, G. Ceccherini, I. Cecconi, F. Cederbaum, A.I. Ceña, V. Cenci, S. Cerella, C. Cervia, D. Cetrullo, S. Chaachouay, H. Chae, H.-J. Chagin, A.S. Chai, C.-Y. Chakrabarti, G. Chamilos, G. Chan, E.Y.W. Chan, M.T.V. Chandra, D. Chandra, P. Chang, C.-P. Chang, R.C.-C. Chang, T.Y. Chatham, J.C. Chatterjee, S. Chauhan, S. Che, Y. Cheetham, M.E. Cheluvappa, R. Chen, C.-J. Chen, G. Chen, G.-C. Chen, G. Chen, H. Chen, J.W. Chen, J.-K. Chen, M. Chen, M. Chen, P. Chen, Q. Chen, Q. Chen, S.-D. Chen, S. Chen, S.S.-L. Chen, W. Chen, W.-J. Chen, W.Q. Chen, W. Chen, X. Chen, Y.-H. Chen, Y.-G. Chen, Y. Chen, Y. Chen, Y. Chen, Y.-J. Chen, Y.-Q. Chen, Y. Chen, Z. Chen, Z. Cheng, A. Cheng, C.H.K. Cheng, H. Cheong, H. Cherry, S. Chesney, J. Cheung, C.H.A. Chevet, E. Chi, H.C. Chi, S.-G. Chiacchiera, F. Chiang, H.-L. Chiarelli, R. Chiariello, M. Chieppa, M. Chin, L.-S. Chiong, M. Chiu, G.N.C. Cho, D.-H. Cho, S.-G. Cho, W.C. Cho, Y.-Y. Cho, Y.-S. Choi, A.M.K. Choi, E.-J. Choi, E.-K. Choi, J. Choi, M.E. Choi, S.-I. Chou, T.-F. Chouaib, S. Choubey, D. Choubey, V. Chow, K.-C. Chowdhury, K. Chu, C.T. Chuang, T.-H. Chun, T. Chung, H. Chung, T. Chung, Y.-L. Chwae, Y.-J. Cianfanelli, V. Ciarcia, R. Ciechomska, I.A. Ciriolo, M.R. Cirone, M. Claerhout, S. Clague, M.J. Cl� ria, J. Clarke, P.G.H. Clarke, R. Clementi, E. Cleyrat, C. Cnop, M. Coccia, E.M. Cocco, T. Codogno, P. Coers, J. Cohen, E.E.W. Colecchia, D. Coletto, L. Coll, N.S. Colucci-Guyon, E. Comincini, S. Condello, M. Cook, K.L. Coombs, G.H. Cooper, C.D. Cooper, J.M. Coppens, I. Corasaniti, M.T. Corazzari, M. Corbalan, R. Corcelle-Termeau, E. Cordero, M.D. Corral-Ramos, C. Corti, O. Cossarizza, A. Costelli, P. Costes, S. Cotman, S.L. Coto-Montes, A. Cottet, S. Couve, E. Covey, L.R. Cowart, L.A. Cox, J.S. Coxon, F.P. Coyne, C.B. Cragg, M.S. Craven, R.J. Crepaldi, T. Crespo, J.L. Criollo, A. Crippa, V. Cruz, M.T. Cuervo, A.M. Cuezva, J.M. Cui, T. Cutillas, P.R. Czaja, M.J. Czyzyk-Krzeska, M.F. Dagda, R.K. Dahmen, U. Dai, C. Dai, W. Dai, Y. Dalby, K.N. Valle, L.D. Dalmasso, G. D'amelio, M. Damme, M. Darfeuille-Michaud, A. Dargemont, C. Darley-Usmar, V.M. Dasarathy, S. Dasgupta, B. Dash, S. Dass, C.R. Davey, H.M. Davids, L.M. Dávila, D. Davis, R.J. Dawson, T.M. Dawson, V.L. Daza, P. de Belleroche, J. de Figueiredo, P. de Figueiredo, R.C.B.Q. de la Fuente, J. De Martino, L. De Matteis, A. De Meyer, G.R.Y. De Milito, A. De Santi, M. de Souza, W. De Tata, V. De Zio, D. Debnath, J. Dechant, R. Decuypere, J.-P. Deegan, S. Dehay, B. Del Bello, B. Del Re, D.P. Delage-Mourroux, R. Delbridge, L.M.D. Deldicque, L. Delorme-Axford, E. Deng, Y. Dengjel, J. Denizot, M. Dent, P. Der, C.J. Deretic, V. Derrien, B. Deutsch, E. Devarenne, T.P. Devenish, R.J. Di Bartolomeo, S. Di Daniele, N. Di Domenico, F. Di Nardo, A. Di Paola, S. Di Pietro, A. Di Renzo, L. Di Antonio, A. Díaz-Araya, G. Díaz-Laviada, I. Diaz-Meco, M.T. Diaz-Nido, J. Dickey, C.A. Dickson, R.C. Diederich, M. Digard, P. Dikic, I. Dinesh-Kumar, S.P. Ding, C. Ding, W.-X. Ding, Z. Dini, L. Distler, J.H.W. Diwan, A. Djavaheri-Mergny, M. Dmytruk, K. Dobson, R.C.J. Doetsch, V. Dokladny, K. Dokudovskaya, S. Donadelli, M. Dong, X.C. Dong, X. Dong, Z. Donohue, T.M., Jr. Donohue-Jr, T.M. Doran, K.S. D'orazi, G. Dorn, G.W., II Dosenko, V. Dridi, S. Drucker, L. Du, J. Du, L.-L. Du, L. du Toit, A. Dua, P. Duan, L. Duann, P. Dubey, V.K. Duchen, M.R. Duchosal, M.A. Duez, H. Dugail, I. Dumit, V.I. Duncan, M.C. Dunlop, E.A. Dunn, W.A., Jr. Dupont, N. Dupuis, L. Durán, R.V. Durcan, T.M. Duvezin-Caubet, S. Duvvuri, U. Eapen, V. Ebrahimi-Fakhari, D. Echard, A. Eckhart, L. Edelstein, C.L. Edinger, A.L. Eichinger, L. Eisenberg, T. Eisenberg-Lerner, A. Eissa, N.T. El-Deiry, W.S. El-Khoury, V. Elazar, Z. Eldar-Finkelman, H. Elliott, C.J.H. Emanuele, E. Emmenegger, U. Engedal, N. Engelbrecht, A.-M. Engelender, S. Enserink, J.M. Erdmann, R. Erenpreisa, J. Eri, R. Eriksen, J.L. Erman, A. Escalante, R. Eskelinen, E.-L. Espert, L. Esteban-Martínez, L. Evans, T.J. Fabri, M. Fabrias, G. Fabrizi, C. Facchiano, A. Færgeman, N.J. Faggioni, A. Fairlie, W.D. Fan, C. Fan, D. Fan, J. Fang, S. Fanto, M. Fanzani, A. Farkas, T. Faure, M. Favier, F.B. Fearnhead, H. Federici, M. Fei, E. Felizardo, T.C. Feng, H. Feng, Y. Feng, Y. Ferguson, T.A. Fernández, Á.F. Fernandez-Barrena, M.G. Fernandez-Checa, J.C. Fernández-López, A. Fernandez-Zapico, M.E. Feron, O. Ferraro, E. Ferreira-Halder, C.V. Fesus, L. Feuer, R. Fiesel, F.C. Filippi-Chiela, E.C. Filomeni, G. Fimia, G.M. Fingert, J.H. Finkbeiner, S. Finkel, T. Fiorito, F. Fisher, P.B. Flajolet, M. 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Steegborn, C. Steele, J.W. Stefanis, L. Steffan, J. Stellrecht, C.M. Stenmark, H. Stepkowski, T.M. Stern, S.T. Stevens, C. Stockwell, B.R. Stoka, V. Storchova, Z. Stork, B. Stratoulias, V. Stravopodis, D.J. Strnad, P. Strohecker, A.M. Ström, A.-L. Stromhaug, P. Stulik, J. Su, Y.-X. Su, Z. Subauste, C.S. Subramaniam, S. Sue, C.M. Suh, S.W. Sui, X. Sukseree, S. Sulzer, D. Sun, F.-L. Sun, J. Sun, J. Sun, S.-Y. Sun, Y. Sun, Y. Sun, Y. Sundaramoorthy, V. Sung, J. Suzuki, H. Suzuki, K. Suzuki, N. Suzuki, T. Suzuki, Y.J. Swanson, M.S. Swanton, C. Swärd, K. Swarup, G. Sweeney, S.T. Sylvester, P.W. Szatmari, Z. Szegezdi, E. Szlosarek, P.W. Taegtmeyer, H. Tafani, M. Taillebourg, E. Tait, S.W.G. Takacs-Vellai, K. Takahashi, Y. Takáts, S. Takemura, G. Takigawa, N. Talbot, N.J. Tamagno, E. Tamburini, J. Tan, C.-P. Tan, L. Tan, M.L. Tan, M. Tan, Y.-J. Tanaka, K. Tanaka, M. Tang, D. Tang, D. Tang, G. Tanida, I. Tanji, K. Tannous, B.A. Tapia, J.A. Tasset-Cuevas, I. Tatar, M. Tavassoly, I. 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Vaccaro, M.I. Váchová, L. Vakifahmetoglu-Norberg, H. Valdor, R. Valente, E.M. Vallette, F. Valverde, A.M. Van den Berghe, G. Van Den Bosch, L. van den Brink, G.R. van der Goot, F.G. van der Klei, I.J. van der Laan, L.J.W. van Doorn, W.G. van Egmond, M. van Golen, K.L. Van Kaer, L. Campagne, M.L. Vandenabeele, P. Vandenberghe, W. Vanhorebeek, I. Varela-Nieto, I. Vasconcelos, M.H. Vasko, R. Vavvas, D.G. Vega-Naredo, I. Velasco, G. Velentzas, A.D. Velentzas, P.D. Vellai, T. Vellenga, E. Vendelbo, M.H. Venkatachalam, K. Ventura, N. Ventura, S. Veras, P.S.T. Verdier, M. Vertessy, B.G. Viale, A. Vidal, M. Vieira, H.L.A. Vierstra, R.D. Vigneswaran, N. Vij, N. Vila, M. Villar, M. Villar, V.H. Villarroya, J. Vindis, C. Viola, G. Viscomi, M.T. Vitale, G. Vogl, D.T. Voitsekhovskaja, O.V. von Haefen, C. von Schwarzenberg, K. Voth, D.E. Vouret-Craviari, V. Vuori, K. Vyas, J.M. Waeber, C. Walker, C.L. Walker, M.J. Walter, J. Wan, L. Wan, X. Wang, B. Wang, C. Wang, C.-Y. Wang, C. Wang, C. Wang, C. 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Zhang, J. Zhang, J. Zhang, J.-P. Zhang, L. Zhang, L. Zhang, L. Zhang, M.-Y. Zhang, X. Zhang, X.D. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Y. Zhao, M. Zhao, W.-L. Zhao, X. Zhao, Y.G. Zhao, Y. Zhao, Y. Zhao, Y.-X. Zhao, Z. Zhao, Z.J. Zheng, D. Zheng, X.-L. Zheng, X. Zhivotovsky, B. Zhong, Q. Zhou, G.-Z. Zhou, G. Zhou, H. Zhou, S.-F. Zhou, X.-J. Zhu, H. Zhu, H. Zhu, W.-G. Zhu, W. Zhu, X.-F. Zhu, Y. Zhuang, S.-M. Zhuang, X. Ziparo, E. Zois, C.E. Zoladek, T. Zong, W.-X. Zorzano, A. Zughaier, S.M.
Published: 2016

43. Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Author: Klionsky, D.J. Abdelmohsen, K. Abe, A. Abedin, M.J. Abeliovich, H. Arozena, A.A. Adachi, H. Adams, C.M. Adams, P.D. Adeli, K. Adhihetty, P.J. Adler, S.G. Agam, G. Agarwal, R. Aghi, M.K. Agnello, M. Agostinis, P. Aguilar, P.V. Aguirre-Ghiso, J. Airoldi, E.M. Ait-Si-Ali, S. Akematsu, T. Akporiaye, E.T. Al-Rubeai, M. Albaiceta, G.M. Albanese, C. Albani, D. Albert, M.L. Aldudo, J. Algül, H. Alirezaei, M. Alloza, I. Almasan, A. Almonte-Beceril, M. Alnemri, E.S. Alonso, C. Altan-Bonnet, N. Altieri, D.C. Alvarez, S. Alvarez-Erviti, L. Alves, S. Amadoro, G. Amano, A. Amantini, C. Ambrosio, S. Amelio, I. Amer, A.O. Amessou, M. Amon, A. An, Z. Anania, F.A. Andersen, S.U. Andley, U.P. Andreadi, C.K. Andrieu-Abadie, N. Anel, A. Ann, D.K. Anoopkumar-Dukie, S. Antonioli, M. Aoki, H. Apostolova, N. Aquila, S. Aquilano, K. Araki, K. Arama, E. Aranda, A. Araya, J. Arcaro, A. Arias, E. Arimoto, H. Ariosa, A.R. Armstrong, J.L. Arnould, T. Arsov, I. Asanuma, K. Askanas, V. Asselin, E. Atarashi, R. Atherton, S.S. Atkin, J.D. Attardi, L.D. Auberger, P. Auburger, G. Aurelian, L. Autelli, R. Avagliano, L. Avantaggiati, M.L. Avrahami, L. Azad, N. Awale, S. Bachetti, T. Backer, J.M. Bae, D.-H. Bae, J.-S. Bae, O.-N. Bae, S.H. Baehrecke, E.H. Baek, S.-H. Baghdiguian, S. Bagniewska-Zadworna, A. Bai, H. Bai, J. Bai, X.-Y. Bailly, Y. Balaji, K.N. Balduini, W. Ballabio, A. Balzan, R. Banerjee, R. Bánhegyi, G. Bao, H. Barbeau, B. Barrachina, M.D. Barreiro, E. Bartel, B. Bartolomé, A. Bassham, D.C. Bassi, M.T. Bast, R.C., Jr. Basu, A. Batista, M.T. Batoko, H. Battino, M. Bauckman, K. Baumgarner, B.L. Bayer, K.U. Beale, R. Beaulieu, J.-F. Beck, G.R., Jr. Becker, C. Beckham, J.D. Bédard, P.-A. Bednarski, P.J. Begley, T.J. Behl, C. Behrends, C. Behrens, G.M.N. Behrns, K.E. Bejarano, E. Belaid, A. Belleudi, F. Bénard, G. Berchem, G. Bergamaschi, D. Bergami, M. Berkhout, B. Berliocchi, L. Bernard, A. Bernard, M. Bernassola, F. Bertolotti, A. Bess, A.S. Besteiro, S. Bettuzzi, S. Bhalla, S. Bhattacharyya, S. Bhutia, S.K. Biagosch, C. Bianchi, M.W. Biard-Piechaczyk, M. Billes, V. Bincoletto, C. Bingol, B. Bird, S.W. Bitoun, M. Bjedov, I. Blackstone, C. Blanc, L. Blanco, G.A. Blomhoff, H.K. Boada-Romero, E. Böckler, S. Boes, M. Boesze-Battaglia, K. Boise, L.H. Bolino, A. Boman, A. Bonaldo, P. Bordi, M. Bosch, J. Botana, L.M. Botti, J. Bou, G. Bouché, M. Bouchecareilh, M. Boucher, M.-J. Boulton, M.E. Bouret, S.G. Boya, P. Boyer-Guittaut, M. Bozhkov, P.V. Brady, N. Braga, V.M.M. Brancolini, C. Braus, G.H. Bravo-San-Pedro, J.M. Brennan, L.A. Bresnick, E.H. Brest, P. Bridges, D. Bringer, M.-A. Brini, M. Brito, G.C. Brodin, B. Brookes, P.S. Brown, E.J. Brown, K. Broxmeyer, H.E. Bruhat, A. Brum, P.C. Brumell, J.H. Brunetti-Pierri, N. Bryson-Richardson, R.J. Buch, S. Buchan, A.M. Budak, H. Bulavin, D.V. Bultman, S.J. Bultynck, G. Bumbasirevic, V. Burelle, Y. Burke, R.E. Burmeister, M. Bütikofer, P. Caberlotto, L. Cadwell, K. Cahova, M. Cai, D. Cai, J. Cai, Q. Calatayud, S. Camougrand, N. Campanella, M. Campbell, G.R. Campbell, M. Campello, S. Candau, R. Caniggia, I. Cantoni, L. Cao, L. Caplan, A.B. Caraglia, M. Cardinali, C. Cardoso, S.M. Carew, J.S. Carleton, L.A. Carlin, C.R. Carloni, S. Carlsson, S.R. Carmona-Gutierrez, D. Carneiro, L.A.M. Carnevali, O. Carra, S. Carrier, A. Carroll, B. Casas, C. Casas, J. Cassinelli, G. Castets, P. Castro-Obregon, S. Cavallini, G. Ceccherini, I. Cecconi, F. Cederbaum, A.I. Ceña, V. Cenci, S. Cerella, C. Cervia, D. Cetrullo, S. Chaachouay, H. Chae, H.-J. Chagin, A.S. Chai, C.-Y. Chakrabarti, G. Chamilos, G. Chan, E.Y.W. Chan, M.T.V. Chandra, D. Chandra, P. Chang, C.-P. Chang, R.C.-C. Chang, T.Y. Chatham, J.C. Chatterjee, S. Chauhan, S. Che, Y. Cheetham, M.E. Cheluvappa, R. Chen, C.-J. Chen, G. Chen, G.-C. Chen, G. Chen, H. Chen, J.W. Chen, J.-K. Chen, M. Chen, M. Chen, P. Chen, Q. Chen, Q. Chen, S.-D. Chen, S. Chen, S.S.-L. Chen, W. Chen, W.-J. Chen, W.Q. Chen, W. Chen, X. Chen, Y.-H. Chen, Y.-G. Chen, Y. Chen, Y. Chen, Y. Chen, Y.-J. Chen, Y.-Q. Chen, Y. Chen, Z. Chen, Z. Cheng, A. Cheng, C.H.K. Cheng, H. Cheong, H. Cherry, S. Chesney, J. Cheung, C.H.A. Chevet, E. Chi, H.C. Chi, S.-G. Chiacchiera, F. Chiang, H.-L. Chiarelli, R. Chiariello, M. Chieppa, M. Chin, L.-S. Chiong, M. Chiu, G.N.C. Cho, D.-H. Cho, S.-G. Cho, W.C. Cho, Y.-Y. Cho, Y.-S. Choi, A.M.K. Choi, E.-J. Choi, E.-K. Choi, J. Choi, M.E. Choi, S.-I. Chou, T.-F. Chouaib, S. Choubey, D. Choubey, V. Chow, K.-C. Chowdhury, K. Chu, C.T. Chuang, T.-H. Chun, T. Chung, H. Chung, T. Chung, Y.-L. Chwae, Y.-J. Cianfanelli, V. Ciarcia, R. Ciechomska, I.A. Ciriolo, M.R. Cirone, M. Claerhout, S. Clague, M.J. Clària, J. Clarke, P.G.H. Clarke, R. Clementi, E. Cleyrat, C. Cnop, M. Coccia, E.M. Cocco, T. Codogno, P. Coers, J. Cohen, E.E.W. Colecchia, D. Coletto, L. Coll, N.S. Colucci-Guyon, E. Comincini, S. Condello, M. Cook, K.L. Coombs, G.H. Cooper, C.D. Cooper, J.M. Coppens, I. Corasaniti, M.T. Corazzari, M. Corbalan, R. 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Published: 2016

44. Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

Author: Klionsky, D. J., Abdelmohsen, K., Abe, A., Abedin, M. J., Abeliovich, H., Arozena, A. A., Adachi, H., Adams, C. M., Adams, P. D., Adeli, K., Adhihetty, P. J., Adler, S. G., Agam, G., Agarwal, R., Aghi, M. K., Agnello, M., Agostinis, P., Aguilar, P. V., Aguirre-Ghiso, J., Airoldi, E. M., Ait-Si-Ali, S., Akematsu, T., Akporiaye, E. T., Al-Rubeai, M., Albaiceta, G. M., Albanese, C., Albani, D., Albert, M. L., Aldudo, J., Algül, H., Alirezaei, M., Alloza, I., Almasan, A., Almonte-Beceril, M., Alnemri, E. S., Alonso, C., Altan-Bonnet, N., Altieri, D. C., Alvarez, S., Alvarez-Erviti, L., Alves, S., Amadoro, G., Amano, A., Amantini, C., Ambrosio, S., Amelio, I., Amer, A. O., Amessou, M., Amon, A., An, Z., Anania, F. A., Andersen, S. U., Andley, U. P., Andreadi, C. K., Andrieu-Abadie, N., Anel, A., Ann, D. K., Anoopkumar-Dukie, S., Antonioli, M., Aoki, H., Apostolova, N., Aquila, S., Aquilano, K., Araki, K., Arama, E., Aranda, A., Araya, J., Arcaro, A., Arias, E., Arimoto, H., Ariosa, A. R., Armstrong, J. L., Arnould, T., Arsov, I., Asanuma, K., Askanas, V., Asselin, E., Atarashi, R., Atherton, S. S., Atkin, J. D., Attardi, L. D., Auberger, P., Auburger, G., Aurelian, L., Autelli, R., Avagliano, L., Avantaggiati, M. L., Avrahami, L., Azad, N., Awale, S., Bachetti, T., Backer, J. M., Bae, D. -H., Bae, J. -S., Bae, O. -N., Bae, S. H., Baehrecke, E. H., Baek, S. -H., Baghdiguian, S., Bagniewska-Zadworna, A., Bai, H., Bai, J., Bai, X. -Y., Bailly, Y., Balaji, K. N., Balduini, W., Ballabio, A., Balzan, R., Banerjee, R., Bánhegyi, G., Bao, H., Barbeau, B., Barrachina, M. D., Barreiro, E., Bartel, B., Bartolomé, A., Bassham, D. C., Bassi, M. T., Bast, R. C., J, R., Basu, A., Batista, M. T., Batoko, H., Battino, M., Bauckman, K., Baumgarner, B. L., Bayer, K. 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H., Bravo-San-Pedro, J. M., Brennan, L. A., Bresnick, E. H., Brest, P., Bridges, D., Bringer, M. -A., Brini, M., Brito, G. C., Brodin, B., Brookes, P. S., Brown, E. J., Brown, K., Broxmeyer, H. E., Bruhat, A., Brum, P. C., Brumell, J. H., Brunetti-Pierri, N., Bryson-Richardson, R. J., Buch, S., Buchan, A. M., Budak, H., Bulavin, D. V., Bultman, S. J., Bultynck, G., Bumbasirevic, V., Burelle, Y., Burke, R. E., Burmeister, M., Bütikofer, P., Caberlotto, L., Cadwell, K., Cahova, M., Cai, D., Cai, J., Cai, Q., Calatayud, S., Camougrand, N., Campanella, M., Campbell, G. R., Campbell, M., Campello, S., Candau, R., Caniggia, I., Cantoni, L., Cao, L., Caplan, A. B., Caraglia, M., Cardinali, C., Cardoso, S. M., Carew, J. S., Carleton, L. A., Carlin, C. R., Carloni, S., Carlsson, S. R., Carmona-Gutierrez, D., Carneiro, L. A. 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Murat, Ke, Po-Yuan, Ke, Zun-Ji, Kehrl, John H., Keller, Kate E., Kemper, Jongsook Kim, Kenworthy, Anne K., Kepp, Oliver, Kern, Andrea, Kesari, Santosh, Kessel, David, Ketteler, Robin, Kettelhut, Isis do Carmo, Khambu, Bilon, Khan, Muzamil Majid, Khandelwal, Vinoth K.M., Khare, Sangeeta, Kiang, Juliann G., Kiger, Amy A., Kihara, Akio, Kim, Arianna L., Kim, Cheol Hyeon, Kim, Deok Ryong, Kim, Do-Hyung, Kim, Eung Kweon, Kim, Hye Young, Kim, Hyung-Ryong, Kim, Jae-Sung, Kim, Jeong Hun, Kim, Jin Cheon, Kim, Jin Hyoung, Kim, Kwang Woon, Kim, Michael D., Kim, Moon-Moo, Kim, Peter K., Kim, Seong Who, Kim, Soo-Youl, Kim, Yong-Sun, Kim, Yonghyun, Kimchi, Adi, Kimmelman, Alec C., Kimura, Tomonori, King, Jason S., Kirkegaard, Karla, Kirkin, Vladimir, Kirshenbaum, Lorrie A., Kishi, Shuji, Kitajima, Yasuo, Kitamoto, Katsuhiko, Kitaoka, Yasushi, Kitazato, Kaio, Kley, Rudolf A., Klimecki, Walter T., Klinkenberg, Michael, Klucken, Jochen, Knævelsrud, Helene, Knecht, Erwin, Knuppertz, Laura, Ko, Jiunn-Liang, Kobayashi, Satoru, Koch, Jan C., Koechlin-Ramonatxo, Christelle, Koenig, Ulrich, Koh, Young Ho, Köhler, Katja, Kohlwein, Sepp D., Koike, Masato, Komatsu, Masaaki, Kominami, Eiki, Kong, Dexin, Kong, Hee Jeong, Konstantakou, Eumorphia G., Kopp, Benjamin T., Korcsmaros, Tama, Korhonen, Laura, Korolchuk, Viktor I., Koshkina, Nadya V., Kou, Yanjun, Koukourakis, Michael I., Koumenis, Constantino, Kovács, Attila L., Kovács, Tibor, Kovacs, Werner J., Koya, Daisuke, Kraft, Claudine, Krainc, Dimitri, Kramer, Helmut, Kravic-Stevovic, Tamara, Krek, Wilhelm, Kretz-Remy, Carole, Krick, Roswitha, Krishnamurthy, Malathi, Kriston-Vizi, Jano, Kroemer, Guido, Kruer, Michael C., Kruger, Rejko, Ktistakis, Nicholas T., Kuchitsu, Kazuyuki, Kuhn, Christian, Kumar, Addanki Pratap, Kumar, Anuj, Kumar, Ashok, Kumar, Deepak, Kumar, Dhiraj, Kumar, Rakesh, Kumar, Sharad, Kundu, Mondira, Kung, Hsing-Jien, Kuno, Atsushi, Kuo, Sheng-Han, Kuret, Jeff, Kurz, Tino, Kwok, Terry, Kwon, Taeg Kyu, Kwon, Yong Tae, Kyrmizi, Irene, La Spada, Albert R., Lafont, Frank, Lahm, Tim, Lakkaraju, Aparna, Lam, Truong, Lamark, Trond, Lancel, Steve, Landowski, Terry H., Lane, Darius J.R., Lane, Jon D., Lanzi, Cinzia, Lapaquette, Pierre, Lapierre, Louis R., Laporte, Jocelyn, Laukkarinen, Johanna, Laurie, Gordon W., Lavandero, Sergio, Lavie, Lena, Lavoie, Matthew J., Law, Betty Yuen Kwan, Law, Helen Ka-Wai, Law, Kelsey B., Layfield, Robert, Lazo, Pedro A., Le Cam, Laurent, Le Roch, Karine G., Le Stunff, Hervé, Leardkamolkarn, Vijittra, Lecuit, Marc, Lee, Byung-Hoon, Lee, Che-Hsin, Lee, Erinna F., Lee, Gyun Min, Lee, He-Jin, Lee, Hsinyu, Lee, Jae Keun, Lee, Jongdae, Lee, Ju-Hyun, Lee, Jun Hee, Lee, Michael, Lee, Myung-Shik, Lee, Patty J., Lee, Sam W., Lee, Seung-Jae, Lee, Shiow-Ju, Lee, Stella Y., Lee, Sug Hyung, Lee, Sung Sik, Lee, Sung-Joon, Lee, Sunhee, Lee, Ying-Ray, Lee, Yong J., Lee, Young H., Leeuwenburgh, Christiaan, Lefort, Sylvain, Legouis, Renaud, Lei, Jinzhi, Lei, Qun-Ying, Leib, David A., Leibowitz, Gil, Lekli, Istvan, Lemaire, Stéphane D., Lemasters, John J., Lemberg, Marius K., Lemoine, Antoinette, Leng, Shuilong, Lenz, Guido, Lenzi, Paola, Lerman, Lilach O., Barbato, Daniele Lettieri, Leu, Julia I. 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Sue, Menna-Barreto, Rubem F.S., Menon, Manoj B., Meraz-Ríos, Marco A., Merla, Giuseppe, Merlini, Luciano, Merlot, Angelica M., Meryk, Andrea, Meschini, Stefania, Meyer, Joel N., Mi, Man-Tian, Miao, Chao-Yu, Micale, Lucia, Michaeli, Simon, Michiels, Carine, Migliaccio, Anna Rita, Mihailidou, Anastasia Susie, Mijaljica, Dalibor, Mikoshiba, Katsuhiko, Milan, Enrico, Miller-Fleming, Leonor, Mills, Gordon B., Mills, Ian G., Minakaki, Georgia, Minassian, Berge A., Ming, Xiu-Fen, Minibayeva, Farida, Minina, Elena A., Mintern, Justine D., Minucci, Saverio, Miranda-Vizuete, Antonio, Mitchell, Claire H., Miyamoto, Shigeki, Miyazawa, Keisuke, Mizushima, Noboru, Mnich, Katarzyna, Mograbi, Baharia, Mohseni, Simin, Moita, Luis Ferreira, Molinari, Marco, Molinari, Maurizio, Møller, Andreas Buch, Mollereau, Bertrand, Mollinedo, Faustino, Mongillo, Marco, Monick, Martha M., Montagnaro, Serena, Montell, Craig, Moore, Darren J., Moore, Michael N., Mora-Rodriguez, Rodrigo, Moreira, Paula I., Morel, Etienne, Morelli, Maria Beatrice, Moreno, Sandra, Morgan, Michael J., Moris, Arnaud, Moriyasu, Yuji, Morrison, Janna L., Morrison, Lynda A., Morselli, Eugenia, Moscat, Jorge, Moseley, Pope L., Mostowy, Serge, Motori, Elisa, Mottet, Deni, Mottram, Jeremy C., Moussa, Charbel E.-H., Mpakou, Vassiliki E., Mukhtar, Hasan, Levy, Jean M. Mulcahy, Muller, Sylviane, Muñoz-Moreno, Raquel, Muñoz-Pinedo, Cristina, Münz, Christian, Murphy, Maureen E., Murray, James T., Murthy, Aditya, Mysorekar, Indira U., Nabi, Ivan R., Nabissi, Massimo, Nader, Gustavo A., Nagahara, Yukitoshi, Nagai, Yoshitaka, Nagata, Kazuhiro, Nagelkerke, Anika, Nagy, Péter, Naidu, Samisubbu R., Nair, Sreejayan, Nakano, Hiroyasu, Nakatogawa, Hitoshi, Nanjundan, Meera, Napolitano, Gennaro, Naqvi, Naweed I., Nardacci, Roberta, Narendra, Derek P., Narita, Masashi, Nascimbeni, Anna Chiara, Natarajan, Ramesh, Navegantes, Luiz C., Nawrocki, Steffan T., Nazarko, Taras Y., Nazarko, Volodymyr Y., Neill, Thoma, Neri, Luca M., Netea, Mihai G., Netea-Maier, Romana T., Neves, Bruno M., Ney, Paul A., Nezis, Ioannis P., Nguyen, Hang T.T., Nguyen, Huu Phuc, Nicot, Anne-Sophie, Nilsen, Hilde, Nilsson, Per, Nishimura, Mikio, Nishino, Ichizo, Niso-Santano, Mireia, Niu, Hua, Nixon, Ralph A., Njar, Vincent C.O., Noda, Takeshi, Noegel, Angelika A., Nolte, Elsie Magdalena, Norberg, Erik, Norga, Koenraad K., Noureini, Sakineh Kazemi, Notomi, Shoji, Notterpek, Lucia, Nowikovsky, Karin, Nukina, Nobuyuki, Nürnberger, Thorsten, O'donnell, Valerie B., O'donovan, Tracey, O'dwyer, Peter J., Oehme, Ina, Oeste, Clara L., Ogawa, Michinaga, Ogretmen, Besim, Ogura, Yuji, Oh, Young J., Ohmuraya, Masaki, Ohshima, Takayuki, Ojha, Rani, Okamoto, Koji, Okazaki, Toshiro, Oliver, F. Javier, Ollinger, Karin, Olsson, Stefan, Orban, Daniel P., Ordonez, Paulina, Orhon, Idil, Orosz, Laszlo, O'rourke, Eyleen J., Orozco, Helena, Ortega, Angel L., Ortona, Elena, Osellame, Laura D., Oshima, Junko, Oshima, Shigeru, Osiewacz, Heinz D., Otomo, Takanobu, Otsu, Kinya, Ou, Jing-Hsiung Jame, Outeiro, Tiago F., Ouyang, Dong-Yun, Ouyang, Hongjiao, Overholtzer, Michael, Ozbun, Michelle A., Ozdinler, P. Hande, Ozpolat, Bulent, Pacelli, Consiglia, Paganetti, Paolo, Page, Guyléne, Pages, Gille, Pagnini, Ugo, Pajak, Beata, Pak, Stephen C., Pakos-Zebrucka, Karolina, Pakpour, Nazzy, Palková, Zdena, Palladino, Francesca, Pallauf, Kathrin, Pallet, Nicola, Palmieri, Marta, Paludan, Søren R., Palumbo, Camilla, Palumbo, Silvia, Pampliega, Olatz, Pan, Hongming, Pan, Wei, Panaretakis, Theochari, Pandey, Aseem, Pantazopoulou, Areti, Papackova, Zuzana, Papademetrio, Daniela L., Papassideri, Issidora, Papini, Alessio, Parajuli, Nirmala, Pardo, Julian, Parekh, Vrajesh V., Parenti, Giancarlo, Park, Jong-In, Park, Junsoo, Park, Ohkmae K., Parker, Roy, Parlato, Rosanna, Parys, Jan B., Parzych, Katherine R., Pasquet, Jean-Max, Pasquier, Benoit, Pasumarthi, Kishore B.S., Patschan, Daniel, Pattingre, Sophie, Pattison, Scott, Pause, Arnim, Pavenstädt, Hermann, Pavone, Flaminia, Pedrozo, Zully, Peña, Fernando J., Peñalva, Miguel A., Pende, Mario, Peng, Jianxin, Penna, Fabio, Penninger, Josef M., Pensalfini, Anna, Pepe, Salvatore, Pereira, Gustavo J.S., Pereira, Paulo C., de la Cruz, Verónica Pérez, Pérez-Pérez, María Esther, Pérez-Rodríguez, Diego, Pérez-Sala, Dolore, Perier, Celine, Perl, Andra, Perlmutter, David H., Perrotta, Ida, Pervaiz, Shazib, Pesonen, Maija, Pessin, Jeffrey E., Peters, Godefridus J., Petersen, Morten, Petrache, Irina, Petrof, Basil J., Petrovski, Goran, Phang, James M., Piacentini, Mauro, Pierdominici, Marina, Pierre, Philippe, Pierrefite-Carle, Valérie, Pietrocola, Federico, Pimentel-Muiños, Felipe X., Pinar, Mario, Pineda, Benjamin, Pinkas-Kramarski, Ronit, Pinti, Marcello, Pinton, Paolo, Piperdi, Bilal, Piret, James M., Platanias, Leonidas C., Platta, Harald W., Plowey, Edward D., Pöggeler, Stefanie, Poirot, Marc, Polčic, Peter, Poletti, Angelo, Poon, Audrey H., Popelka, Hana, Popova, Blagovesta, Poprawa, Izabela, Poulose, Shibu M., Poulton, Joanna, Powers, Scott K., Powers, Ted, Pozuelo-Rubio, Mercede, Prak, Krisna, Prange, Reinhild, Prescott, Mark, Priault, Muriel, Prince, Sharon, Proia, Richard L., Proikas-Cezanne, Tassula, Prokisch, Holger, Promponas, Vasilis J., Przyklenk, Karin, Puertollano, Rosa, Pugazhenthi, Subbiah, Puglielli, Luigi, Pujol, Aurora, Puyal, Julien, Pyeon, Dohun, Qi, Xin, Qian, Wen-Bin, Qin, Zheng-Hong, Qiu, Yu, Qu, Ziwei, Quadrilatero, Joe, Quinn, Frederick, Raben, Nina, Rabinowich, Hannah, Radogna, Flavia, Ragusa, Michael J., Rahmani, Mohamed, Raina, Komal, Ramanadham, Sasanka, Ramesh, Rajagopal, Rami, Abdelhaq, Randall-Demllo, Sarron, Randow, Felix, Rao, Hai, Rao, V. Ashutosh, Rasmussen, Blake B., Rasse, Tobias M., Ratovitski, Edward A., Rautou, Pierre-Emmanuel, Ray, Swapan K., Razani, Babak, Reed, Bruce H., Reggiori, Fulvio, Rehm, Marku, Reichert, Andreas S., Rein, Theo, Reiner, David J., Reits, Eric, Ren, Jun, Ren, Xingcong, Renna, Maurizio, Reusch, Jane E.B., Revuelta, Jose L., Reyes, Leticia, Rezaie, Alireza R., Richards, Robert I., Richardson, Des R., Richetta, Clémence, Riehle, Michael A., Rihn, Bertrand H., Rikihisa, Yasuko, Riley, Brigit E., Rimbach, Gerald, Rippo, Maria Rita, Ritis, Konstantino, Rizzi, Federica, Rizzo, Elizete, Roach, Peter J., Robbins, Jeffrey, Roberge, Michel, Roca, Gabriela, Roccheri, Maria Carmela, Rocha, Sonia, Rodrigues, Cecilia M.P., Rodríguez, Clara I., de Cordoba, Santiago Rodriguez, Rodriguez-Muela, Natalia, Roelofs, Jeroen, Rogov, Vladimir V., Rohn, Troy T., Rohrer, Bärbel, Romanelli, Davide, Romani, Luigina, Romano, Patricia Silvia, Roncero, M. Isabel G., Rosa, Jose Lui, Rosello, Alicia, Rosen, Kirill V., Rosenstiel, Philip, Rost-Roszkowska, Magdalena, Roth, Kevin A., Roué, Gael, Rouis, Mustapha, Rouschop, Kasper M., Ruan, Daniel T., Ruano, Diego, Rubinsztein, David C., Rucker, Edmund B., Rudich, Assaf, Rudolf, Emil, Rudolf, Ruediger, Ruegg, Markus A., Ruiz-Roldan, Carmen, Ruparelia, Avnika Ashok, Rusmini, Paola, Russ, David W., Russo, Gian Luigi, Russo, Giuseppe, Russo, Rossella, Rusten, Tor Erik, Ryabovol, Victoria, Ryan, Kevin M., Ryter, Stefan W., Sabatini, David M., Sacher, Michael, Sachse, Carsten, Sack, Michael N., Sadoshima, Junichi, Saftig, Paul, Sagi-Eisenberg, Ronit, Sahni, Sumit, Saikumar, Pothana, Saito, Tsunenori, Saitoh, Tatsuya, Sakakura, Koichi, Sakoh-Nakatogawa, Machiko, Sakuraba, Yasuhito, Salazar-Roa, María, Salomoni, Paolo, Saluja, Ashok K., Salvaterra, Paul M., Salvioli, Rosa, Samali, Afshin, Sanchez, Anthony M.J., Sánchez-Alcázar, José A., Sanchez-Prieto, Ricardo, Sandri, Marco, Sanjuan, Miguel A., Santaguida, Stefano, Santambrogio, Laura, Santoni, Giorgio, Dos Santos, Claudia Nune, Saran, Shweta, Sardiello, Marco, Sargent, Graeme, Sarkar, Pallabi, Sarkar, Sovan, Sarrias, Maria Rosa, Sarwal, Minnie M., Sasakawa, Chihiro, Sasaki, Motoko, Sass, Miklo, Sato, Ken, Sato, Miyuki, Satriano, Joseph, Savaraj, Niramol, Saveljeva, Svetlana, Schaefer, Liliana, Schaible, Ulrich E., Scharl, Michael, Schatzl, Hermann M., Schekman, Randy, Scheper, Wiep, Schiavi, Alfonso, Schipper, Hyman M., Schmeisser, Hana, Schmidt, Jen, Schmitz, Ingo, Schneider, Bianca E., Schneider, E. Marion, Schneider, Jaime L., Schon, Eric A., Schönenberger, Miriam J., Schönthal, Axel H., Schorderet, Daniel F., Schröder, Bernd, Schuck, Sebastian, Schulze, Ryan J., Schwarten, Melanie, Schwarz, Thomas L., Sciarretta, Sebastiano, Scotto, Kathleen, Scovassi, A. Ivana, Screaton, Robert A., Screen, Mark, Seca, Hugo, Sedej, Simon, Segatori, Laura, Segev, Nava, Seglen, Per O., Seguí-Simarro, Jose M., Segura-Aguilar, Juan, Seiliez, Iban, Seki, Ekihiro, Sell, Christian, Semenkovich, Clay F., Semenza, Gregg L., Sen, Utpal, Serra, Andreas L., Serrano-Puebla, Ana, Sesaki, Hiromi, Setoguchi, Takao, Settembre, Carmine, Shacka, John J., Shajahan-Haq, Ayesha N., Shapiro, Irving M., Sharma, Shweta, She, Hua, Shen, C.-K. Jame, Shen, Chiung-Chyi, Shen, Han-Ming, Shen, Sanbing, Shen, Weili, Sheng, Rui, Sheng, Xianyong, Sheng, Zu-Hang, Shepherd, Trevor G., Shi, Junyan, Shi, Qiang, Shi, Qinghua, Shi, Yuguang, Shibutani, Shusaku, Shibuya, Kenichi, Shidoji, Yoshihiro, Shieh, Jeng-Jer, Shih, Chwen-Ming, Shimada, Yohta, Shimizu, Shigeomi, Shin, Dong Wook, Shinohara, Mari L., Shintani, Michiko, Shintani, Takahiro, Shioi, Tetsuo, Shirabe, Ken, Shiri-Sverdlov, Ronit, Shirihai, Orian, Shore, Gordon C., Shu, Chih-Wen, Shukla, Deepak, Sibirny, Andriy A., Sica, Valentina, Sigurdson, Christina J., Sigurdsson, Einar M., Sijwali, Puran Singh, Sikorska, Beata, Silveira, Wilian A., Silvente-Poirot, Sandrine, Silverman, Gary A., Simak, Jan, Simmet, Thoma, Simon, Anna Katharina, Simon, Hans-Uwe, Simone, Cristiano, Simons, Matia, Simonsen, Anne, Singh, Rajat, Singh, Shivendra V., Singh, Shrawan K., Sinha, Debasish, Sinha, Sangita, Sinicrope, Frank A., Sirko, Agnieszka, Sirohi, Kapil, Sishi, Balindiwe J.N., Sittler, Annie, Siu, Parco M., Sivridis, Efthimio, Skwarska, Anna, Slack, Ruth, Slaninová, Iva, Slavov, Nikolai, Smaili, Soraya S., Smalley, Keiran S.M., Smith, Duncan R., Soenen, Stefaan J., Soleimanpour, Scott A., Solhaug, Anita, Somasundaram, Kumaravel, Son, Jin H., Sonawane, Avinash, Song, Chunjuan, Song, Fuyong, Song, Hyun Kyu, Song, Ju-Xian, Song, Wei, Soo, Kai Y., Sood, Anil K., Soong, Tuck Wah, Soontornniyomkij, Virawudh, Sorice, Maurizio, Sotgia, Federica, Soto-Pantoja, David R., Sotthibundhu, Areechun, Sousa, Maria João, Spaink, Herman P., Span, Paul N., Spang, Anne, Sparks, Janet D., Speck, Peter G., Spector, Stephen A., Spies, Claudia D., Springer, Wolfdieter, Clair, Daret St, Stacchiotti, Alessandra, Staels, Bart, Stang, Michael T., Starczynowski, Daniel T., Starokadomskyy, Petro, Steegborn, Clemen, Steele, John W., Stefanis, Leonida, Steffan, Joan, Stellrecht, Christine M., Stenmark, Harald, Stepkowski, Tomasz M., Stern, Stęphan T., Stevens, Craig, Stockwell, Brent R., Stoka, Veronika, Storchova, Zuzana, Stork, Björn, Stratoulias, Vassili, Stravopodis, Dimitrios J., Strnad, Pavel, Strohecker, Anne Marie, Ström, Anna-Lena, Stromhaug, Per, Stulik, Jiri, Su, Yu-Xiong, Su, Zhaoliang, Subauste, Carlos S., Subramaniam, Srinivasa, Sue, Carolyn M., Suh, Sang Won, Sui, Xinbing, Sukseree, Supawadee, Sulzer, David, Sun, Fang-Lin, Sun, Jiaren, Sun, Jun, Sun, Shi-Yong, Sun, Yang, Sun, Yi, Sun, Yingjie, Sundaramoorthy, Vinod, Sung, Joseph, Suzuki, Hidekazu, Suzuki, Kuninori, Suzuki, Naoki, Suzuki, Tadashi, Suzuki, Yuichiro J., Swanson, Michele S., Swanton, Charle, Swärd, Karl, Swarup, Ghanshyam, Sweeney, Sean T., Sylvester, Paul W., Szatmari, Zsuzsanna, Szegezdi, Eva, Szlosarek, Peter W., Taegtmeyer, Heinrich, Tafani, Marco, Taillebourg, Emmanuel, Tait, Stephen W.G., Takacs-Vellai, Krisztina, Takahashi, Yoshinori, Takáts, Szabolc, Takemura, Genzou, Takigawa, Nagio, Talbot, Nicholas J., Tamagno, Elena, Tamburini, Jerome, Tan, Cai-Ping, Tan, Lan, Tan, Mei Lan, Tan, Ming, Tan, Yee-Joo, Tanaka, Keiji, Tanaka, Masaki, Tang, Daolin, Tang, Dingzhong, Tang, Guomei, Tanida, Isei, Tanji, Kunikazu, Tannous, Bakhos A., Tapia, Jose A., Tasset-Cuevas, Inmaculada, Tatar, Marc, Tavassoly, Iman, Tavernarakis, Nektario, Taylor, Allen, Taylor, Graham S., Taylor, Gregory A., Taylor, J. Paul, Taylor, Mark J., Tchetina, Elena V., Tee, Andrew R., Teixeira-Clerc, Fatima, Telang, Sucheta, Tencomnao, Tewin, Teng, Ba-Bie, Teng, Ru-Jeng, Terro, Faraj, Tettamanti, Gianluca, Theiss, Arianne L., Theron, Anne E., Thomas, Kelly Jean, Thomé, Marcos P., Thomes, Paul G., Thorburn, Andrew, Thorner, Jeremy, Thum, Thoma, Thumm, Michael, Thurston, Teresa L.M., Tian, Ling, Till, Andrea, Ting, Jenny Pan-Yun, Ting, Jenny Pan Yun, Titorenko, Vladimir I., Toker, Lilach, Toldo, Stefano, Tooze, Sharon A., Topisirovic, Ivan, Torgersen, Maria Lyngaa, Torosantucci, Liliana, Torriglia, Alicia, Torrisi, Maria Rosaria, Tournier, Cathy, Towns, Roberto, Trajkovic, Vladimir, Travassos, Leonardo H., Triola, Gemma, Tripathi, Durga Nand, Trisciuoglio, Daniela, Troncoso, Rodrigo, Trougakos, Ioannis P., Truttmann, Anita C., Tsai, Kuen-Jer, Tschan, Mario P., Tseng, Yi-Hsin, Tsukuba, Takayuki, Tsung, Allan, Tsvetkov, Andrey S., Tu, Shuiping, Tuan, Hsing-Yu, Tucci, Marco, Tumbarello, David A., Turk, Bori, Turk, Vito, Turner, Robin F.B., Tveita, Anders A., Tyagi, Suresh C., Ubukata, Makoto, Uchiyama, Yasuo, Udelnow, Andrej, Ueno, Takashi, Umekawa, Midori, Umemiya-Shirafuji, Rika, Underwood, Benjamin R., Ungermann, Christian, Ureshino, Rodrigo P., Ushioda, Ryo, Uversky, Vladimir N., Uzcátegui, Néstor L., Vaccari, Thoma, Vaccaro, Maria I., Váchová, Libuše, Vakifahmetoglu-Norberg, Helin, Valdor, Rut, Valente, Enza Maria, Vallette, Francoi, Valverde, Angela M., Van den Berghe, Greet, Van Den Bosch, Ludo, van den Brink, Gijs R., van der Goot, F. Gisou, van der Klei, Ida J., van der Laan, Luc J.W., van Doorn, Wouter G., van Egmond, Marjolein, van Golen, Kenneth L., Van Kaer, Luc, Campagne, Menno van Lookeren, Vandenabeele, Peter, Vandenberghe, Wim, Vanhorebeek, Ilse, Varela-Nieto, Isabel, Vasconcelos, M. Helena, Vasko, Radovan, Vavvas, Demetrios G., Vega-Naredo, Ignacio, Velasco, Guillermo, Velentzas, Athanassios D., Velentzas, Panagiotis D., Vellai, Tibor, Vellenga, Edo, Vendelbo, Mikkel Holm, Venkatachalam, Kartik, Ventura, Natascia, Ventura, Salvador, Veras, Patrícia S.T., Verdier, Mireille, Vertessy, Beata G., Viale, Andrea, Vidal, Michel, Vieira, Helena L.A., Vierstra, Richard D., Vigneswaran, Nadarajah, Vij, Neeraj, Vila, Miquel, Villar, Margarita, Villar, Victor H., Villarroya, Joan, Vindis, Cécile, Viola, Giampietro, Viscomi, Maria Teresa, Vitale, Giovanni, Vogl, Dan T., Voitsekhovskaja, Olga V., von Haefen, Clarissa, von Schwarzenberg, Karin, Voth, Daniel E., Vouret-Craviari, Valérie, Vuori, Kristina, Vyas, Jatin M., Waeber, Christian, Walker, Cheryl Lyn, Walker, Mark J., Walter, Jochen, Wan, Lei, Wan, Xiangbo, Wang, Bo, Wang, Caihong, Wang, Chao-Yung, Wang, Chengshu, Wang, Chenran, Wang, Chuangui, Wang, Dong, Wang, Fen, Wang, Fuxin, Wang, Guanghui, Wang, Hai-Jie, Wang, Haichao, Wang, Hong-Gang, Wang, Hongmin, Wang, Horng-Dar, Wang, Jing, Wang, Junjun, Wang, Mei, Wang, Mei-Qing, Wang, Pei-Yu, Wang, Peng, Wang, Richard C., Wang, Shuo, Wang, Ting-Fang, Wang, Xian, Wang, Xiao-Jia, Wang, Xiao-Wei, Wang, Xin, Wang, Xuejun, Wang, Yan, Wang, Yanming, Wang, Ying, Wang, Ying-Jan, Wang, Yipeng, Wang, Yu, Wang, Yu Tian, Wang, Yuqing, Wang, Zhi-Nong, Wappner, Pablo, Ward, Carl, Ward, Diane McVey, Warnes, Gary, Watada, Hirotaka, Watanabe, Yoshihisa, Watase, Kei, Weaver, Timothy E., Weekes, Colin D., Wei, Jiwu, Weide, Thoma, Weihl, Conrad C., Weindl, Günther, Weis, Simone Nardin, Wen, Longping, Wen, Xin, Wen, Yunfei, Westermann, Benedikt, Weyand, Cornelia M., White, Anthony R., White, Eileen, Whitton, J. Lindsay, Whitworth, Alexander J., Wiels, Joëlle, Wild, Franziska, Wildenberg, Manon E., Wileman, Tom, Wilkinson, Deepti Sriniva, Wilkinson, Simon, Willbold, Dieter, Williams, Chri, Williams, Katherine, Williamson, Peter R., Winklhofer, Konstanze F., Witkin, Steven S., Wohlgemuth, Stephanie E., Wollert, Thoma, Wolvetang, Ernst J., Wong, Esther, Wong, G. William, Wong, Richard W., Wong, Vincent Kam Wai, Woodcock, Elizabeth A., Wright, Karen L., Wu, Chunlai, Wu, Defeng, Wu, Gen Sheng, Wu, Jian, Wu, Junfang, Wu, Mian, Wu, Min, Wu, Shengzhou, Wu, William K.K., Wu, Yaohua, Wu, Zhenlong, Xavier, Cristina P.R., Xavier, Ramnik J., Xia, Gui-Xian, Xia, Tian, Xia, Weiliang, Xia, Yong, Xiao, Hengyi, Xiao, Jian, Xiao, Shi, Xiao, Wuhan, Xie, Chuan-Ming, Xie, Zhiping, Xie, Zhonglin, Xilouri, Maria, Xiong, Yuyan, Xu, Chuanshan, Xu, Congfeng, Xu, Feng, Xu, Haoxing, Xu, Hongwei, Xu, Jian, Xu, Jianzhen, Xu, Jinxian, Xu, Liang, Xu, Xiaolei, Xu, Yangqing, Xu, Ye, Xu, Zhi-Xiang, Xu, Ziheng, Xue, Yu, Yamada, Takahiro, Yamamoto, Ai, Yamanaka, Koji, Yamashina, Shunhei, Yamashiro, Shigeko, Yan, Bing, Yan, Bo, Yan, Xianghua, Yan, Zhen, Yanagi, Yasuo, Yang, Dun-Sheng, Yang, Jin-Ming, Yang, Liu, Yang, Minghua, Yang, Pei-Ming, Yang, Peixin, Yang, Qian, Yang, Wannian, Yang, Wei Yuan, Yang, Xuesong, Yang, Yi, Yang, Ying, Yang, Zhifen, Yang, Zhihong, Yao, Meng-Chao, Yao, Pamela J., Yao, Xiaofeng, Yao, Zhenyu, Yao, Zhiyuan, Yasui, Linda S., Ye, Mingxiang, Yedvobnick, Barry, Yeganeh, Behzad, Yeh, Elizabeth S., Yeyati, Patricia L., Yi, Fan, Yi, Long, Yin, Xiao-Ming, Yip, Calvin K., Yoo, Yeong-Min, Yoo, Young Hyun, Yoon, Seung-Yong, Yoshida, Ken-Ichi, Yoshimori, Tamotsu, Young, Ken H., Yu, Huixin, Yu, Jane J., Yu, Jin-Tai, Yu, Jun, Yu, Li, Yu, W. Haung, Yu, Xiao-Fang, Yu, Zhengping, Yuan, Junying, Yuan, Zhi-Min, Yue, Beatrice Y.J.T., Yue, Jianbo, Yue, Zhenyu, Zacks, David N., Zacksenhaus, Eldad, Zaffaroni, Nadia, Zaglia, Tania, Zakeri, Zahra, Zecchini, Vincent, Zeng, Jinsheng, Zeng, Min, Zeng, Qi, Zervos, Antonis S., Zhang, Donna D., Zhang, Fan, Zhang, Guo, Zhang, Guo-Chang, Zhang, Hao, Zhang, Hong, Zhang, Hongbing, Zhang, Jian, Zhang, Jiangwei, Zhang, Jianhua, Zhang, Jing-Pu, Zhang, Li, Zhang, Lin, Zhang, Long, Zhang, Ming-Yong, Zhang, Xiangnan, Zhang, Xu Dong, Zhang, Yan, Zhang, Yang, Zhang, Yanjin, Zhang, Yingmei, Zhang, Yunjiao, Zhao, Mei, Zhao, Wei-Li, Zhao, Xiaonan, Zhao, Yan G., Zhao, Ying, Zhao, Yongchao, Zhao, Yu-Xia, Zhao, Zhendong, Zhao, Zhizhuang J., Zheng, Dexian, Zheng, Xi-Long, Zheng, Xiaoxiang, Zhivotovsky, Bori, Zhong, Qing, Zhou, Guang-Zhou, Zhou, Guofei, Zhou, Huiping, Zhou, Shu-Feng, Zhou, Xu-Jie, Zhu, Hongxin, Zhu, Hua, Zhu, Wei-Guo, Zhu, Wenhua, Zhu, Xiao-Feng, Zhu, Yuhua, Zhuang, Shi-Mei, Zhuang, Xiaohong, Ziparo, Elio, Zois, Christos E., Zoladek, Teresa, Zong, Wei-Xing, Zorzano, Antonio, and Zughaier, Susu M.
Subjects: Molecular Biology, Cell Biology, Settore BIO/06 - Anatomia Comparata E Citologia
Abstract: non presente
Published: 2016

45. Incidence and Outcomes of Acute Circulatory Support Prior to Heart Transplantation

Author: Ouyang, D., primary, Gulati, G., additional, Ha, R., additional, and Banerjee, D., additional
Published: 2017
Full Text: View/download PDF

46. 151P - Investigation of the mechanism of action of anti-PD-1 treatment by systematic depletion of different immune cell populations in syngeneic models

Author: Bourre, L., Jin, Y., Muntel, J., Yu, H., Beeler, K., Bruderer, R., Shan, Y., An, A. Xiaoyu, Ouyang, D. Xuesong, and Li, H. Qixiang
Published: 2019
Full Text: View/download PDF

47. Mouse tumor HuGEMM-h/mCTLA-4 models for assessing human anti-CTLA-4 therapeutics

Author: Wang, Z., primary, Ouyang, D., additional, An, A., additional, Cai, B., additional, Chen, G., additional, Liu, J., additional, Dong, X., additional, and Li, H., additional
Published: 2016
Full Text: View/download PDF

48. Screening of drug metabolizing enzymes for fusidic acid and its interactions with isoform-selective substrates in vitro

Author: Chen, D., primary, Lin, X.-X., additional, Zhao, Q., additional, Xiao, J., additional, Peng, S.-F., additional, Xiao, M.-F., additional, Ouyang, D.-S., additional, Tan, Z.-R., additional, Wang, Y.-C., additional, Peng, J.-B., additional, Zhang, W., additional, and Chen, Y., additional
Published: 2016
Full Text: View/download PDF

49. Strip of the Month: May 2016

Author: Diaz, N. M., primary and Ouyang, D. W., additional
Published: 2016
Full Text: View/download PDF

50. Minimal diagnosis of discrete-event systems

Author: Zhao, X., Lamperti, Gian Franco, and Ouyang, D.
Subjects: Discrete event systems, Automata-based representation, Fault diagnosis
Published: 2013

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