Author: "Chen, Ning" / Topic: business - Searchworks@Jio Institute Digital Library Search Results

192 results on '"Chen, Ning"'

1. External Validation of Two Predictive Models for Postoperative Fever After Retrograde Intrarenal Surgery in Pediatric Patients

Author: Wenying Wang, Jun Li, Lei Tang, Zhang Yu, Chen Ning, Ye Tian, Chun-Ming Li, and Fang-Zhou Zhao
Subjects: Male, medicine.medical_specialty, Fever, business.industry, Urology, External validation, Reproducibility of Results, Nomogram, medicine.disease, Surgery, Kidney Calculi, Nomograms, Postoperative fever, Treatment Outcome, Text mining, Child, Preschool, Humans, Medicine, Female, Child, business, Reliability (statistics), Retrospective Studies
Abstract: Objective: The aim of this study is to validate the efficacy and reliability of two predictive models for postoperative fever after retrograde intrarenal surgery (RIRS) in pediatric patients Materi...
Published: 2021

2. Development and validation of a predictive model for severe postpartum hemorrhage in women undergoing vaginal delivery: A retrospective cohort study

Author: Fang He, Jin-sheng Li, Zhi-hong Guan, Chen-ning Liu, Chen-an Liu, and Yun-zhe Xu
Subjects: medicine.medical_specialty, Pregnancy, Placental abruption, Cesarean Section, Obstetrics, business.industry, Placenta accreta, Vaginal delivery, Placenta, Postpartum Hemorrhage, Obstetrics and Gynecology, Retrospective cohort study, General Medicine, Nomogram, medicine.disease, Confidence interval, Cohort Studies, Cohort, medicine, Humans, Female, business, Retrospective Studies
Abstract: OBJECTIVE To develop a predictive tool to accurately screen women at high risk of severe postpartum hemorrhage (SPPH) undergoing vaginal delivery. METHODS We analyzed 28 150 mothers who underwent vaginal delivery after 28 weeks of pregnancy in the Third Affiliated Hospital of Guangzhou Medical University from January 2015 to August 2019. Two-thirds of the cohort were randomly allocated to a training set (n = 18 766) and the rest to a validation set (n = 9384). In the training set, we built a radiomic nomogram based on multivariate logistic analysis, and calibration and C-index were evaluated. The performance of the validated nomogram was then tested in the validation cohort. RESULTS Independent risk factors for SPPH in women undergoing vaginal delivery were previous cesarean section, history of PPH, in vitro fertilization, anemia, intrauterine death, prolonged labor, low-lying placenta, placental abruption, placenta accreta spectrum, and macrosomia. Good calibration was observed for the probability of SPPH in the validation cohort, and the C-index of the nomogram for the prediction of SPPH was 0.861 (95% confidence interval 0.820-0.902). CONCLUSION This model would be a useful tool to accurately screen for women at high-risk of SPPH undergoing vaginal delivery. It would be expected to be an effective tool to guide clinical practice and further reduce maternal mortality.
Published: 2021

3. Quantitative ultrasound imaging of intrinsic hand muscles after traumatic cervical spinal cord injury

Author: Cliff S. Klein, Xinghua Yang, Hui Liu, and Chen Ning Zhao
Subjects: Adult, Hand muscles, Electromyography, business.industry, Cervical Cord, Work injury, General Medicine, Hand, medicine.disease, Quantitative ultrasound, Atrophy, Neurology, Anesthesia, Cervical spinal cord injury, Paralysis, Ultrasound imaging, Humans, Medicine, Neurology (clinical), medicine.symptom, Muscle, Skeletal, business, Spinal Cord Injuries, Ultrasonography, Echo intensity
Abstract: STUDY DESIGN This is a cross-sectional descriptive study. OBJECTIVES To quantify differences in hand muscle morphology between persons with cervical spinal cord injury (SCI) and uninjured adults. SETTING The study was performed at the Guangdong Work Injury Rehabilitation Hospital. METHODS We quantified hand muscle cross-sectional area (CSA), thickness, and echo intensity (EI) in 18 persons with subacute to chronic SCI and 23 controls using ultrasound imaging. RESULTS Mean SCI abductor pollicis brevis (APB), abductor digiti minimi (ADM), and first dorsal interosseous (FDI) CSA were ~26%, 43%, and 37% smaller than the control means, the deficit in the APB being less than the ADM (P
Published: 2021

4. Simulation of Ship Trajectory in Waves Based on STAR-CCM+

Author: Chen Ning and Han Baochen
Subjects: business.industry, Computer science, 020101 civil engineering, Star ccm, 02 engineering and technology, General Medicine, Computational fluid dynamics, 01 natural sciences, 010305 fluids & plasmas, 0201 civil engineering, Control theory, 0103 physical sciences, business, Trajectory (fluid mechanics)
Abstract: The reliable ship motion trajectory signal provides a basis for six-degree-of-freedom test bench to simulate ship motion in waves. The characteristics of ship motion in waves were analyzed. Aiming at the problem of complex hull shape and large displacement at sea, the dynamic overlapping grid technique was used. Based on STAR-CCM+, the free surface was solved by CFD (Computational Fluid Dynamics) numerical simulation method. The numerical model of ship motion in waves was established, and the DFBI (Dynamic Fluid Body Interaction) method was used to simulate the ship motion in the waves. The ship’s motion trajectory was obtained in the regular and irregular heading waves, which provides important support for the six-degree-of-freedom test bench wave simulation system.
Published: 2021

5. A machine vision-based defect detection system for nuclear-fuel rod groove

Author: Chen Shengfeng, Dong Licheng, Lu Enhui, Liu Jian, Suo Xinyu, and Chen Ning
Subjects: Production line, 0209 industrial biotechnology, Materials science, Nuclear fuel, Machine vision, business.industry, 02 engineering and technology, Industrial and Manufacturing Engineering, 020901 industrial engineering & automation, Optics, Artificial Intelligence, Region of interest, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Performance improvement, Coaxial, business, Groove (engineering), Software, Template method pattern
Abstract: The processing quality of the grooves of a nuclear-fuel rod will directly affect the quality of the finished nuclear-fuel rod. Due to the highly reflective, microscopic, and annular characteristics of nuclear-fuel rod grooves, it has been quite challenging to realize imaging and microscopic defect detection for these grooves. In this work, a machine vision-based defect detection system was developed for nuclear-fuel rod grooves. Through the performance improvement and application of the self-reference template defect detection method, efficient online inspection of nuclear-fuel rod grooves was realized. In the developed system, a combined-light-source imaging system was first designed by combining a coaxial light and a ring light, which realized the clear imaging of a groove. After that, an image expansion strategy was employed to expand the annular groove into a strip-shaped region of interest (ROI). Then, according to the turning processing characteristic of the nuclear-fuel rod groove, the large-size defect detection effect of the self-reference template method was improved by eliminating the anomalous columns prior to generating the self-reference template. The experimental results indicated that the average inspection efficiency of the developed system was 8.026 s/rod, the average false detection rate was 0.183%. The accuracy of the self-reference template method was 87.6%, higher than that of YOLOv2 and Faster R-CNN. The developed system exhibits high inspection efficiency and accuracy, so it can meet the actual detection functions and requirements of production lines, and now it has been successfully applied to actual production.
Published: 2021

6. Global Context-Based Multilevel Feature Fusion Networks for Multilabel Remote Sensing Image Scene Classification

Author: Xin Wang, Lin Duan, and Chen Ning
Subjects: Atmospheric Science, remote sensing (RS), Contextual image classification, QC801-809, Computer science, business.industry, Semantic feature, Deep learning, Geophysics. Cosmic physics, Feature extraction, Context (language use), multilevel fusion, Object (computer science), Semantics, Ocean engineering, Deep learning (DL), multilabel classification (MLC), Artificial intelligence, Computers in Earth Sciences, business, Representation (mathematics), TC1501-1800, Remote sensing
Abstract: Different from the traditional remote sensing (RS) scene classification which uses a single scene label to holistically annotate an image, multilabel RS image classification uses a series of object labels to interpret a scene more deeply. For multilabel RS scene classification, there exist two vital problems. First, the objects with different semantic labels have smaller sizes and more scattered arrangements compared to backgrounds, making meaningful semantic feature extraction and representation severely hard. Second, an RS scene usually contains various kinds of objects, leading to exponential magnification of output label space size with the increase of the number of object categories. To simultaneously solve the challenges in features as well as label space and produce significant performance improvements, this article proposes a novel end-to-end deep learning architecture, which we term the global context-based multilevel feature fusion network. We verify the whole framework by conducting a great number of experiments on two publicly available multilabel datasets, and we also provide an ablation study exploring different modules inclusion in the framework. Experimental results demonstrate that the proposed method is superior to some popular networks for multilabel RS image scene classification.
Published: 2021

7. Multifunction Control Strategy for Single-Phase AC/DC Power Conversion Systems With Voltage-Sensorless Power-Decoupling Function

Author: Bodong Li, Xiaoqing Wang, Chen Ning, and Min Chen
Subjects: Total harmonic distortion, Computer science, business.industry, Capacitive sensing, 020208 electrical & electronic engineering, Ripple, 02 engineering and technology, Power factor, Decoupling (cosmology), Single-phase electric power, Inductor, law.invention, Capacitor, Semiconductor, Control theory, law, 0202 electrical engineering, electronic engineering, information engineering, Harmonic, Power quality, Electrical and Electronic Engineering, business, Decoupling (electronics), Electronic circuit, Voltage
Abstract: In this article, a novel voltage-sensorless controller for the single-phase ac/dc power conversion systems with a self-adaptive power-decoupling function is proposed. Existing control strategies for achieving power decoupling usually require additional active switches or extra measurement circuits to a certain power-decoupling controller. By employing the proposed method, the decoupling voltage could be accurately evaluated with the fluctuations of the other measured state variables in the original converter, and the issues of instability and inaccuracy on power-decoupling control under transient conditions could be solved. Furthermore, the proposed control scheme involves a phase-shift modification to improve the power quality as well as achieve accurate power-decoupling reference, which could eliminate the harmonic influence on the original PFC circuit. Finally, a 1.2-kW single-phase PFC prototype using SiC-based semiconductor switching devices is tested to validate the proposed control scheme. The experimental results demonstrate that the power factor of 0.99 with total harmonic distortion around 3%, and a dc-link voltage with 5% ripple could be achieved with the proposed control strategy.
Published: 2020

8. The Impact of Antidiabetic Agents on Sarcopenia in Type 2 Diabetes: A Literature Review

Author: Kai-Jen Tien and Chen-Ning Wu
Subjects: Sarcopenia, Endocrinology, Diabetes and Metabolism, Review Article, Type 2 diabetes, Bioinformatics, Diseases of the endocrine glands. Clinical endocrinology, Endocrinology, Diabetes mellitus, Humans, Hypoglycemic Agents, Medicine, In patient, Muscle, Skeletal, Wasting, Antidiabetic agents, business.industry, Poor glycemic control, Type 2 Diabetes Mellitus, RC648-665, musculoskeletal system, medicine.disease, body regions, Diabetes Mellitus, Type 2, medicine.symptom, business, human activities
Abstract: Sarcopenia is a geriatric syndrome characterized by decline of skeletal muscle mass and function. Contributing factors include nutritional, genetic, inflammatory, and endocrinal factors. The reported prevalence of sarcopenia in type 2 diabetes mellitus is high, especially in patients with poor glycemic control. Additionally, antidiabetic agents may alter the balance between protein synthesis and degradation through various mechanisms of skeletal muscle mass regulation. This study reviewed the literature on the pathogenesis of sarcopenia in diabetes mellitus and the current understanding of whether antidiabetic agents contribute positively or negatively to sarcopenia and muscle wasting.
Published: 2020

9. Clinical outcome of high-intensity focused ultrasound as the preoperative management of cesarean scar pregnancy

Author: Yan Sun, Chen-Ning Liu, Hong-Jin Yu, Li Tang, and Yu-Hui Liu
Subjects: Adult, medicine.medical_specialty, medicine.medical_treatment, Gestational sac, Blood Loss, Surgical, Cesarean Scar Pregnancy, Hysteroscopy, Cicatrix, 03 medical and health sciences, Postoperative Complications, 0302 clinical medicine, Blood loss, Pregnancy, Preoperative Care, medicine, Humans, Adverse effect, Retrospective Studies, 030219 obstetrics & reproductive medicine, medicine.diagnostic_test, Cesarean Section, business.industry, Obstetrics and Gynecology, Ablation, High-intensity focused ultrasound, Pregnancy, Ectopic, Treatment Outcome, medicine.anatomical_structure, Vacuum Curettage, Hifu treatment, Feasibility Studies, High-Intensity Focused Ultrasound Ablation, Female, Radiology, business
Abstract: The aim of this study was to retrospectively evaluate the feasibility and safety of high-intensity focused ultrasound (HIFU) treatment as the preoperative management of cesarean scar pregnancy (CSP).225 patients with definite Type I CSP were treated with suction curettage under hysteroscopic guidance. Among them, 103 patients chose HIFU treatment before hysteroscopy (assign to the HIFU group), and the other 122 patients without any pretreatment before hysteroscopy to the control group. The successful rate, volume of intraoperative blood loss, time for serum β-human chorionic gonadotropin (β-hCG) level returned to normal, gestational sac disappeared, normal menstrual recovery, and adverse effects were collected and analyzed to compare the two approaches.The successful rate (98.06%) in the HIFU group was higher than that (91.80%) in the contrast group. The median ablation time was 39 min and the median HIFU sonication time was 106.6 s. The median volume of intraoperative blood loss in the HIFU group was lower than that in the contrast group (P 0.001), and the median time of gestational sac disappeared in the HIFU group was shorter than that in the contrast group. There were no statistically significant differences in the time of serum β-hCG returned to normal and days of menstrual recovery between the 2 groups.Based on our results, it appears that HIFU ablation is a safe and effective modality as pre-treatment before hysteroscopy in the management of CSP.
Published: 2020

10. Association of ABCB1 polymorphisms with lipid homeostasis and liver injury response to atorvastatin in the Chinese population

Author: Zhong-Dong Zhang, Chen-Ning Zhang, Shu-Shan Wang, Lin Zhang, Kan-Kan Qu, and Li-Xia Dong
Subjects: Male, 0301 basic medicine, medicine.medical_specialty, ATP Binding Cassette Transporter, Subfamily B, Genotype, Physiology, Atorvastatin, 030204 cardiovascular system & hematology, Polymorphism, Single Nucleotide, Transaminase, 03 medical and health sciences, 0302 clinical medicine, Asian People, Physiology (medical), Internal medicine, Homeostasis, Humans, Medicine, Transaminases, Triglycerides, Aged, Pharmacology, Liver injury, business.industry, Haplotype, General Medicine, Lipid Metabolism, medicine.disease, Lipoproteins, LDL, Cholesterol, 030104 developmental biology, Endocrinology, Liver, Alkaline phosphatase, Female, Analysis of variance, Lipoproteins, HDL, business, Lipoprotein, medicine.drug
Abstract: The present research was to assess the relationship between ABCB1 (G2677T/A, C3435T) polymorphisms and lipid homeostasis as well as risk of liver injury induced by atorvastatin in in-patients from China. The lipid levels (total cholesterol, high-density lipoprotein, triglycerides) as well as metabolic enzymes of hepar (glutamic–pyruvic transaminase, glutamic–oxalacetic transaminase, alkaline phosphatase, γ-glutamyl transpeptidase) in plasma for 162 patients were measured at baseline and after approximately 6 months of atorvastatin treatment. Polymorphisms of the ABCB1 gene were determined using the Snapshot technique. The associations between genetic polymorphisms and lipid levels as well as hepar indexes were evaluated at the end of medical treatment. Based on one-way ANOVA analysis, patients with the 2677GG or 3435TT genotypes showed a remarkable decrease in percentage when the level of TC was above 4.00 mmol·L−1, separately (P < 0.05). There was a significant decrease in percentage in the frequency of patients with the 2677GG genotype (low-density lipoprotein > 2.00 mmol·L−1) (P < 0.05). The level of glutamic–pyruvic transaminase in patients with the 2677GG or 3435CC genotype displayed a significantly increase in percentage, respectively (P < 0.05). The ABCB1 G-C haplotype carriers were associated with an increased risk of AILI. The results provide evidence for clinically individualised utilisation of atorvastatin for lipid homeostasis as well as risk of induced liver injury in the Chinese population.
Published: 2020

11. An Improved Sampling-Based Approach for Spacecraft Proximity Operation Path Planning in Near-Circular Orbit

Author: Li Jilian, Fang Yuqiang, Chen Ning, Weilin Wang, Diao Huafei, Wenhua Cheng, Zhi Li, and Yasheng Zhang
Subjects: Collision avoidance (spacecraft), General Computer Science, Spacecraft, Computer science, business.industry, General Engineering, Spacecraft proximity operation, sampling-based approach, Ellipse, Control theory, FMT, Obstacle, Trajectory, State space, General Materials Science, Collision detection, Motion planning, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, path planning, lcsh:TK1-9971
Abstract: This paper proposes an improved sampling-based approach for spacecraft proximity operation path planning under Clohessy-Wiltshire-Hill dynamics. The proposed approach is based on a modified version of the FMT* (Fast Marching Tree) algorithm with safety strategy which is divided into three parts: (1) incorporating relative ellipse to simplify the sampling state space and avoid collision with target; (2) combining internal/external ellipse-based collision detection algorithms for hovering obstacle and non-coplanar ellipse obstacle; (3) using rotating hyperplane method to handle the coplanar ellipse obstacle and uncertainty obstacle. By referring the safety strategy to simplify the state space before FMT* algorithm, the approach can reduce the complexity of path planning, especially the resampling and collision avoidance detection cyclic process in FMT*, thereby improve the planning efficiency. Two simulated scenarios, a coplanar path planning with/without coplanar ellipse obstacle, are developed to illustrate the approach. As a result, the proposed approach appears to be potential for spacecraft proximity real-time path planning as well as other complex space mission path planning generalized to different dynamics and environments, such as On-Orbit Service, and enabling a real-time computation of low-cost trajectory.
Published: 2020

12. Alteration of Fungal Microbiota After 5-ASA Treatment in UC Patients

Author: Song Yang, Chen Ning, Xu Jun, Liu Yu-lan, Wu Na, Ren Xinhua, Wu Zhe, and Zhang Yifan
Subjects: Adult, Male, Fusarium, Fungus, Microbiology, Feces, Wickerhamomyces, Scytalidium, Humans, Immunology and Allergy, Medicine, Microbiome, Intestinal Mucosa, DNA, Fungal, Mesalamine, biology, Ascomycota, business.industry, Fungi, Gastroenterology, Middle Aged, medicine.disease, biology.organism_classification, Ulcerative colitis, Gastrointestinal Tract, Dysbiosis, Colitis, Ulcerative, Female, Paecilomyces, business, Mycobiome
Abstract: The effect of treatment regimens on fungal microbiota is unclear in ulcerative colitis (UC) patients. Here, we aimed to clarify the effect of 5-aminosalicylic acid (5-ASA) treatment on gut fungal microbiota in UC patients. Fifty-seven UC patients, including 20 treatment-naïve and 37 5-ASA-treated, were recruited into an exploration study. We compared the gut fungal profiles of these 2 groups of patients using ITS1-2 rDNA sequencing. Ten out of 20 treatment-naïve UC patients were followed up and enrolled for a validation study and underwent a 5-ASA treatment. We assessed the longitudinal differences of fungal microbiota in these patients before and after 5-ASA treatment. Results acquired from the validation study were accordant to those from the exploration study. Ascomycota was the dominant phylum in both noninflamed and inflamed mucosae. At the phylum level, Ascomycota decreased in inflamed mucosae before 5-ASA treatment. At the genus level, pathogens such as Scytalidium, Morchella, and Paecilomyces increased, and Humicola and Wickerhamomyces decreased in inflamed mucosae. After 5-ASA treatment, Ascomycota and Wickerhamomyces increased and Scytalidium, Fusarium, Morchella, and Paecilomyces decreased in both noninflamed and inflamed mucosae. Additionally, the balanced bacteria–fungi correlation was interrupted in inflamed mucosae, and 5-ASA treatment altered group-specific fungal microbiota and restored bacteria–fungi correlation in UC patients. Our results demonstrated that fungal diversity and composition were altered and the bacteria–fungi correlation was restored in inflamed mucosae after 5-ASA treatment.
Published: 2019

13. Truthful generalized assignments via stable matching

Author: Chen, Ning, Gravin, Nick, and Lu, Pinyan
Subjects: Chebyshev approximation -- Analysis, Combinatorial optimization -- Analysis, Business, Computers and office automation industries, Mathematics
Abstract: In the generalized assignment problem (gap), a set of jobs seek to be assigned to a set of machines. For every job-machine pair, there are a specific value and an [...]
Published: 2014
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14. Prevalence and risk factors of severe postpartum hemorrhage: a retrospective cohort study

Author: Fang He, Fu-bing Yu, Yun-zhe Xu, Zhi-hong Guan, Dunjin Chen, Man-na Sun, Chen-an Liu, Jin-sheng Li, and Chen-ning Liu
Subjects: China, medicine.medical_specialty, Anemia, Critical Illness, Reproductive medicine, Gestational Age, Risk Assessment, Severity of Illness Index, 03 medical and health sciences, 0302 clinical medicine, Pregnancy, Prevalence, medicine, Humans, 030212 general & internal medicine, Health Services Needs and Demand, 030219 obstetrics & reproductive medicine, Placental abruption, Cesarean Section, business.industry, Obstetrics, Postpartum Hemorrhage, Obstetrics and Gynecology, Retrospective cohort study, Gynecology and obstetrics, Causes, medicine.disease, Obstetric Labor Complications, Placenta previa, Pregnancy Complications, Uterine atony, Perinatal Care, Risk factors, Cohort, RG1-991, Gestation, Female, business, Maternal Age, Research Article
Abstract: Background Although maternal deaths are rare in developed regions, the morbidity associated with severe postpartum hemorrhage (SPPH) remains a major problem. To determine the prevalence and risk factors of SPPH, we analyzed data of women who gave birth in Guangzhou Medical Centre for Critical Pregnant Women, which received a large quantity of critically ill obstetric patients who were transferred from other hospitals in Southern China. Methods In this study, we conducted a retrospective case-control study to determine the prevalence and risk factors for SPPH among a cohort of women who gave birth after 28 weeks of gestation between January 2015 and August 2019. SPPH was defined as an estimated blood loss ≥1000 mL and total blood transfusion≥4 units. Logistic regression analysis was used to identify independent risk factors for SPPH. Results SPPH was observed in 532 mothers (1.56%) among the total population of 34,178 mothers. Placenta-related problems (55.83%) were the major identified causes of SPPH, while uterine atony without associated retention of placental tissues accounted for 38.91%. The risk factors for SPPH were maternal age Conclusion Maternal age
Published: 2021

15. The origin of major choice, academic commitment, and career-decision readiness among Taiwanese college students

Author: Liao, Chen Ning and Ji, Chang-Ho
Subjects: Student aspirations -- Analysis, Decision-making -- Analysis -- Psychological aspects, Asian students -- Vocational guidance -- Psychological aspects -- Research, Business, Human resources and labor relations
Abstract: The present study aimed to examine if and how career-decision readiness relates to the origin of college major choice among Taiwanese college students. A total of 375 junior and senior [...]
Published: 2015
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16. An Effective Algorithm for Single Image Fog Removal

Author: Xin Wang, Qiong Wang, Xin Zhang, Hangcheng Zhu, and Chen Ning
Subjects: Computer Networks and Communications, Scattering, Computer science, business.industry, Diffuse sky radiation, 020206 networking & telecommunications, 02 engineering and technology, Sparse approximation, Effective algorithm, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Single image, business, Visibility, Software, Image restoration, Information Systems, Degradation (telecommunications)
Abstract: Poor visibility due to the effects of light absorption and scattering is challenging for processing images captured in foggy weather conditions. This paper proposes an effective algorithm for single image fog removal based on degradation model and group-based sparse representation (GSR). The proposed degradation model is constructed based on a classical physical model, i.e., dichromatic atmospheric scattering model. Then, the new degradation model is integrated into the group-based sparse representation framework. Finally, the single image defogging problem is regarded as an image restoration problem, which can be well optimized by GSR. The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations, and results indicate considerable improvement over existing algorithms.
Published: 2019

17. An Object Recognition Approach for Synthetic Aperture Radar Images

Author: Wenbo Liu, Chen Ning, Gong Zhang, and Xin Wang
Subjects: Synthetic aperture radar, Computer Networks and Communications, Computer science, business.industry, Feature vector, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Cognitive neuroscience of visual object recognition, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Kernel principal component analysis, Automatic target recognition, Kernel (image processing), Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Kernel Fisher discriminant analysis, business, Software, Information Systems
Abstract: In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the l1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.
Published: 2019

18. Post-surgical outcomes of patients with chronic kidney disease and end stage renal disease undergoing radical prostatectomy: 10-year results from the US National Inpatient Sample

Author: Xinyi Hu, Jian Zhang, Yichen Zhu, Zhipeng Wang, Jun Lin, Chen Ning, and Fangming Liu
Subjects: Adult, Male, Nephrology, medicine.medical_specialty, Time Factors, Databases, Factual, medicine.medical_treatment, 030232 urology & nephrology, Chronic kidney disease (CKD), 030204 cardiovascular system & hematology, Lower risk, urologic and male genital diseases, lcsh:RC870-923, End stage renal disease, 03 medical and health sciences, Prostate cancer, Postoperative Complications, 0302 clinical medicine, Internal medicine, medicine, Humans, Renal Insufficiency, Chronic, Retrospective Studies, Prostatectomy, business.industry, Acute kidney injury, Prostatic Neoplasms, Retrospective cohort study, Middle Aged, Robot-assisted, medicine.disease, lcsh:Diseases of the genitourinary system. Urology, Radical prostatectomy, United States, female genital diseases and pregnancy complications, End-stage renal disease (ESRD), Treatment Outcome, National Inpatient Sample (NIS), Kidney Failure, Chronic, business, Research Article, Kidney disease
Abstract: Background Chronic kidney disease (CKD) and end stage renal disease (ESRD) are not well characterized in prostate cancer patients. This study aimed to examine the clinical characteristics and postsurgical outcomes of patients with or without CKD and ESRD undergoing radical prostatectomy for prostate cancer. Methods This population-based, retrospective study used patient data from the Nationwide Inpatient Sample, the largest all-payer US inpatient care database. From 2005 to 2014, 136,790 male patients aged > 20 years diagnosed with prostate cancer and who received radical prostatectomy were included. Postoperative complications, postoperative acute kidney injury (AKI) and urinary complications, and length of hospital stay were compared between patients with or without underlying CKD and ESRD. Results After adjusting for relevant factors, the CKD group had a significantly higher risk of postoperative complications than the non-CKD group. In addition, the CKD group had a 5-times greater risk of postoperative AKI and urinary complications than the non-CKD group. Both CKD and ESRD groups had significantly longer hospital stays than the non-CKD group. Patients receiving RARP had a lower risk of postoperative complications than those who received open radical prostatectomy, regardless of having CKD or not. Both non-CKD and CKD patients receiving RARP had shorter hospital stays than those who received open surgery. Conclusions Prostate cancer patients with underlying CKD had significantly greater risk of postoperative complications, postoperative AKI and urinary complications, and longer hospital stays than those without CKD. The use of RARP significantly shortened hospital stays and reduced complications for these patients. Electronic supplementary material The online version of this article (10.1186/s12882-019-1455-2) contains supplementary material, which is available to authorized users.
Published: 2019

19. Synthetic Aperture Radar Target Recognition Using Weighted Multi-Task Kernel Sparse Representation

Author: Chen Ning, Wenbo Liu, Xin Wang, and Gong Zhang
Subjects: Synthetic aperture radar, General Computer Science, Computer science, Feature vector, 02 engineering and technology, 01 natural sciences, Kernel (linear algebra), multi-task, Covariate, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, sparse representation, business.industry, 010401 analytical chemistry, General Engineering, Pattern recognition, Sparse approximation, Target acquisition, 0104 chemical sciences, Nonlinear system, target recognition, kernel, Kernel (statistics), 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, lcsh:TK1-9971, Subspace topology
Abstract: As an extension of traditional sparse representation (SR), kernel SR has received great interest recently in the areas of computer vision and pattern recognition. It shows a considerable capacity to map linearly inseparable data into high-dimensional feature space via nonlinear mapping technique, and has been widely used in target recognition problems. In this paper, we propose a new weighted multi-task kernel sparse representation method to solve the synthetic aperture radar (SAR) target recognition problem. To capture the spatial and spectral information of a SAR target simultaneously, the proposed method explores the monogenic signal transformation to generate multi-scale monogenic features at first. Then, the proposed method provides a unified framework, named multi-task kernel sparse representation, for SAR target classification. The framework implicitly maps monogenic features into a high-dimensional kernel feature space by using the nonlinear mapping associated with a kernel function. In the kernelized subspace, SAR target recognition is formulated as a joint covariate selection problem across a group of related tasks. Furthermore, a multi-task weight optimization scheme is developed to compensate for the heterogeneity of the multi-scale features and enhance the recognition performance. Extensive experimental results tested on the public moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that our proposed method achieves better recognition performance than other existing competitive algorithms.
Published: 2019

20. High-clearance chassis design of Camellia oleifera fruit vibratory harvester

Author: Tengfei Shen, Chen Ning, Anguo Hu, Du Xiaoqiang, Yin Qian, Guofeng Zhang, and Zhang Zhaojie
Subjects: Chassis, Bearing (mechanical), biology, Bending (metalworking), business.industry, Camellia oleifera, Topology optimization, Structural engineering, biology.organism_classification, law.invention, Vibration, Stress (mechanics), law, Suspension (vehicle), business, Mathematics
Abstract: At present, Camellia oleifera fruit is planted in hilly areas in China. The harvest of Camellia oleifera fruit is mainly manual operation, with high labor intensity. A self-propelled high-clearance vibration harvester for Camellia oleifera was designed to solve the problems of less matching equipment, poor performance, low automation level and low efficiency. Due to the influence of terrain factors, the stability of self-propelled harvester is required to be high. As the key bearing part of the whole vehicle, the stability of the high-clearance chassis itself has a significant impact on the stability of the whole vehicle. In this paper, the stress of the chassis under different full load working conditions was simulated by ANSYS, including full-load bending condition, left front wheel suspension condition, right rear wheel suspension condition and diagonal wheel suspension condition. A three-dimensional model of Camellia oleifera fruit vibratory harvester was established. The static analysis of the chassis was carried out, and the stress distribution and deformation of the chassis were discussed. The simulation results show that, under the four conditions, the chassis stress is within the safety range, and there is much room for topology optimization, which lays the foundation for subsequent optimization.
Published: 2021

21. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) 1

Author: Klionsky, Daniel, Abdel-Aziz, Amal Kamal, Abdelfatah, Sara, Abdellatif, Mahmoud, Abdoli, Asghar, Abel, Steffen, Abeliovich, Hagai, Abildgaard, Marie, Abudu, Yakubu Princely, Acevedo-Arozena, Abraham, Adamopoulos, Iannis, Adeli, Khosrow, Adolph, Timon, Adornetto, Annagrazia, Aflaki, Elma, Agam, Galila, Agarwal, Anupam, Aggarwal, Bharat, Agnello, Maria, Agostinis, Patrizia, Agrewala, Javed, Agrotis, Alexander, Aguilar, Patricia, Ahmad, S Tariq, Ahmed, Zubair, Ahumada-Castro, Ulises, Aits, Sonja, Aizawa, Shu, Akkoc, Yunus, Akoumianaki, Tonia, Akpinar, Hafize Aysin, Al-Abd, Ahmed, Al-Akra, Lina, Al-Gharaibeh, Abeer, Alaoui-Jamali, Moulay, Alberti, Simon, Alcocer-Gómez, Elísabet, Alessandri, Cristiano, Ali, Muhammad, Alim Al-Bari, M Abdul, Aliwaini, Saeb, Alizadeh, Javad, Almacellas, Eugènia, Almasan, Alexandru, Alonso, Alicia, Alonso, Guillermo, Altan-Bonnet, Nihal, Altieri, Dario, Álvarez, Élida, Alves, Sara, Alves Da Costa, Cristine, Alzaharna, Mazen, Amadio, Marialaura, Amantini, Consuelo, Amaral, Cristina, Ambrosio, Susanna, Amer, Amal, Ammanathan, Veena, An, Zhenyi, Andersen, Stig, Andrabi, Shaida, Andrade-Silva, Magaiver, Andres, Allen, Angelini, Sabrina, Ann, David, Anozie, Uche, Ansari, Mohammad, Antas, Pedro, Antebi, Adam, Antón, Zuriñe, Anwar, Tahira, Apetoh, Lionel, Apostolova, Nadezda, Araki, Toshiyuki, Araki, Yasuhiro, Arasaki, Kohei, Araújo, Wagner, Araya, Jun, Arden, Catherine, Arévalo, Maria-Angeles, Arguelles, Sandro, Arias, Esperanza, Arikkath, Jyothi, Arimoto, Hirokazu, Ariosa, Aileen, Armstrong-James, Darius, Arnauné-Pelloquin, Laetitia, Aroca, Angeles, Arroyo, Daniela, Arsov, Ivica, Artero, Rubén, Asaro, Dalia Maria Lucia, Aschner, Michael, Ashrafizadeh, Milad, Ashur-Fabian, Osnat, Atanasov, Atanas, Au, Alicia, Auberger, Patrick, Auner, Holger, Aurelian, Laure, Autelli, Riccardo, Avagliano, Laura, Ávalos, Yenniffer, Aveic, Sanja, Aveleira, Célia Alexandra, Avin-Wittenberg, Tamar, Aydin, Yucel, Ayton, Scott, Ayyadevara, Srinivas, Azzopardi, Maria, Baba, Misuzu, Backer, Jonathan, Backues, Steven, Bae, Dong-Hun, Bae, Ok-Nam, Bae, Soo Han, Baehrecke, Eric, Baek, Ahruem, Baek, Seung-Hoon, Baek, Sung Hee, Bagetta, Giacinto, Bagniewska-Zadworna, Agnieszka, Bai, Hua, Bai, Jie, Bai, Xiyuan, Bai, Yidong, Bairagi, Nandadulal, Baksi, Shounak, Balbi, Teresa, Baldari, Cosima, Balduini, Walter, Ballabio, Andrea, Ballester, Maria, Balazadeh, Salma, Balzan, Rena, Bandopadhyay, Rina, Banerjee, Sreeparna, Banerjee, Sulagna, Bánréti, Ágnes, Bao, Yan, Baptista, Mauricio, Baracca, Alessandra, Barbati, Cristiana, Bargiela, Ariadna, Barilà, Daniela, Barlow, Peter, Barmada, Sami, Barreiro, Esther, Barreto, George, Bartek, Jiri, Bartel, Bonnie, Bartolome, Alberto, Barve, Gaurav, Basagoudanavar, Suresh, Bassham, Diane, Bast, Robert, Basu, Alakananda, Batoko, Henri, Batten, Isabella, Baulieu, Etienne, Baumgarner, Bradley, Bayry, Jagadeesh, Beale, Rupert, Beau, Isabelle, Beaumatin, Florian, Bechara, Luiz, Beck, George, Beers, Michael, Begun, Jakob, Behrends, Christian, Behrens, Georg, Bei, Roberto, Bejarano, Eloy, Bel, Shai, Behl, Christian, Belaid, Amine, Belgareh-Touzé, Naïma, Bellarosa, Cristina, Belleudi, Francesca, Belló Pérez, Melissa, Bello-Morales, Raquel, Beltran, Jackeline Soares de Oliveira, Beltran, Sebastián, Benbrook, Doris Mangiaracina, Bendorius, Mykolas, Benitez, Bruno, Benito-Cuesta, Irene, Bensalem, Julien, Berchtold, Martin, Berezowska, Sabina, Bergamaschi, Daniele, Bergami, Matteo, Bergmann, Andreas, Berliocchi, Laura, Berlioz-Torrent, Clarisse, Bernard, Amélie, Berthoux, Lionel, Besirli, Cagri, Besteiro, Sebastien, Betin, Virginie, Beyaert, Rudi, Bezbradica, Jelena, Bhaskar, Kiran, Bhatia-Kissova, Ingrid, Bhattacharya, Resham, Bhattacharya, Sujoy, Bhattacharyya, Shalmoli, Bhuiyan, Md Shenuarin, Bhutia, Sujit Kumar, Bi, Lanrong, Bi, Xiaolin, Biden, Trevor, Bijian, Krikor, Billes, Viktor, Binart, Nadine, Bincoletto, Claudia, Birgisdottir, Asa, Bjorkoy, Geir, Blanco, Gonzalo, Blas-Garcia, Ana, Blasiak, Janusz, Blomgran, Robert, Blomgren, Klas, Blum, Janice, Boada-Romero, Emilio, Boban, Mirta, Boesze-Battaglia, Kathleen, Boeuf, Philippe, Boland, Barry, Bomont, Pascale, Bonaldo, Paolo, Bonam, Srinivasa Reddy, Bonfili, Laura, Bonifacino, Juan, Boone, Brian, Bootman, Martin, Bordi, Matteo, Borner, Christoph, Bornhauser, Beat, Borthakur, Gautam, Bosch, Jürgen, Bose, Santanu, botana, luis, Botas, Juan, Boulanger, Chantal, Boulton, Michael, Bourdenx, Mathieu, Bourgeois, Benjamin, Bourke, Nollaig, Bousquet, Guilhem, Boya, Patricia, Bozhkov, Peter, Bozi, Luiz, Bozkurt, Tolga, Brackney, Doug, Brandts, Christian, Braun, Ralf, Braus, Gerhard, Bravo-Sagua, Roberto, Bravo-San Pedro, José, Brest, Patrick, Bringer, Marie-Agnès, Briones-Herrera, Alfredo, Broaddus, V Courtney, Brodersen, Peter, Brodsky, Jeffrey, Brody, Steven, Bronson, Paola, Bronstein, Jeff, Brown, Carolyn, Brown, Rhoderick, Brum, Patricia, Brumell, John, Brunetti-Pierri, Nicola, Bruno, Daniele, Bryson-Richardson, Robert, Bucci, Cecilia, Buchrieser, Carmen, Bueno, Marta, Buitrago-Molina, Laura Elisa, Buraschi, Simone, Buch, Shilpa, Buchan, J Ross, Buckingham, Erin, Budak, Hikmet, Budini, Mauricio, Bultynck, Geert, Burada, Florin, Burgoyne, Joseph, Burón, M Isabel, Bustos, Victor, Büttner, Sabrina, Butturini, Elena, Byrd, Aaron, Cabas, Isabel, Cabrera-Benitez, Sandra, Cadwell, Ken, Cai, Jingjing, Cai, Lu, Cai, Qian, Cairó, Montserrat, Calbet, Jose, Caldwell, Guy, Caldwell, Kim, Call, Jarrod, Calvani, Riccardo, Calvo, Ana, Calvo-Rubio Barrera, Miguel, Camara, Niels OS, Camonis, Jacques, Camougrand, Nadine, Campanella, Michelangelo, Campbell, Edward, Campbell-Valois, François-Xavier, Campello, Silvia, Campesi, Ilaria, Campos, Juliane, Camuzard, Olivier, Cancino, Jorge, Candido de Almeida, Danilo, Canesi, Laura, Caniggia, Isabella, Canonico, Barbara, Cantí, Carles, Cao, Bin, Caraglia, Michele, Caramés, Beatriz, Carchman, Evie, Cardenal-Muñoz, Elena, Cardenas, Cesar, Cardenas, Luis, Cardoso, Sandra, Carew, Jennifer, Carle, Georges, Carleton, Gillian, Carloni, Silvia, Carmona-Gutierrez, Didac, Carneiro, Leticia, Carnevali, Oliana, Carosi, Julian, Carra, Serena, Carrier, Alice, Carrier, Lucie, Carroll, Bernadette, Carter, A Brent, Carvalho, Andreia Neves, Casanova, Magali, Casas, Caty, Casas, Josefina, Cassioli, Chiara, Castillo, Eliseo, Castillo, Karen, Castillo-Lluva, Sonia, Castoldi, Francesca, Castori, Marco, Castro, Ariel, Castro-Caldas, Margarida, Castro-Hernandez, Javier, Castro-Obregon, Susana, Catz, Sergio, Cavadas, Claudia, Cavaliere, Federica, Cavallini, Gabriella, Cavinato, Maria, Cayuela, Maria, Cebollada Rica, Paula, Cecarini, Valentina, Cecconi, Francesco, Cechowska-Pasko, Marzanna, Cenci, Simone, Ceperuelo-Mallafré, Victòria, Cerqueira, João, Cerutti, Janete, Cervia, Davide, Cetintas, Vildan Bozok, Cetrullo, Silvia, Chae, Han-Jung, Chagin, Andrei, Chai, Chee-Yin, Chakrabarti, Gopal, Chakrabarti, Oishee, Chakraborty, Tapas, Chakraborty, Trinad, Chami, mounia, Chamilos, Georgios, Chan, David, Chan, Edmond, Chan, Edward, Chan, H.Y. Edwin, Chan, Helen, Chan, Hung, Chan, Matthew, Chan, Yau Sang, Chandra, Partha, Chang, Chih-Peng, Chang, Chunmei, Chang, Hao-Chun, Chang, Kai, Chao, Jie, Chapman, Tracey, Charlet-Berguerand, Nicolas, Chatterjee, Samrat, Chaube, Shail, Chaudhary, Anu, Chauhan, Santosh, Chaum, Edward, Checler, Frédéric, Cheetham, Michael, Chen, Chang-Shi, Chen, Guang-Chao, Chen, Jian-Fu, Chen, Liam, Chen, Leilei, Chen, Lin, Chen, Mingliang, Chen, Mu-Kuan, Chen, Ning, Chen, Quan, Chen, Ruey-Hwa, Chen, Shi, Chen, Wei, Chen, Weiqiang, Chen, Xin-Ming, Chen, Xiong-Wen, Chen, Xu, Chen, Yan, Chen, Ye-Guang, Chen, Yingyu, Chen, Yongqiang, Chen, Yu-Jen, Chen, Yue-Qin, Chen, Zhefan Stephen, Chen, Zhi, Chen, Zhi-Hua, Chen, Zhijian, Chen, Zhixiang, Cheng, Hanhua, Cheng, Jun, Cheng, Shi-Yuan, Cheng, Wei, Cheng, Xiaodong, Cheng, Xiu-Tang, Cheng, Yiyun, Cheng, Zhiyong, Chen, Zhong, Cheong, Heesun, Cheong, Jit Kong, Chernyak, Boris, Cherry, Sara, Cheung, Chi Fai Randy, Cheung, Chun Hei Antonio, Cheung, King-Ho, Chevet, Eric, Chi, Richard, Chiang, Alan Kwok Shing, Chiaradonna, Ferdinando, Chiarelli, Roberto, Chiariello, Mario, Chica, Nathalia, Chiocca, Susanna, Chiong, Mario, Chiou, Shih-Hwa, Chiramel, Abhilash, Chiurchiù, Valerio, Cho, Dong-Hyung, Choe, Seong-Kyu, Choi, Augustine, Choi, Mary, Choudhury, Kamalika Roy, Chow, Norman, Chu, Charleen, Chua, Jason, Chua, John Jia En, Chung, Hyewon, Chung, Kin Pan, Chung, Seockhoon, Chung, So-Hyang, Chung, Yuen-Li, Cianfanelli, Valentina, Ciechomska, Iwona, Cifuentes, Mariana, Cinque, Laura, Cirak, Sebahattin, Cirone, Mara, Clague, Michael, Clarke, Robert, Clementi, Emilio, Coccia, Eliana, Codogno, Patrice, Cohen, Ehud, Cohen, Mickael, Colasanti, Tania, Colasuonno, Fiorella, Colbert, Robert, Colell, Anna, Čolić, Miodrag, Coll, Nuria, Collins, Mark, Colombo, María, Colón-Ramos, Daniel, Combaret, Lydie, Comincini, Sergio, Cominetti, Márcia, Consiglio, Antonella, Conte, Andrea, Conti, Fabrizio, Contu, Viorica Raluca, Cookson, Mark, Coombs, Kevin, Coppens, Isabelle, Corasaniti, Maria Tiziana, Corkery, Dale, Cordes, Nils, Cortese, Katia, Costa, Maria do Carmo, Costantino, Sarah, Costelli, Paola, Coto-Montes, Ana, Crack, Peter, Crespo, Jose, Criollo, Alfredo, Crippa, Valeria, Cristofani, Riccardo, Csizmadia, Tamas, Cuadrado, Antonio, Cui, Bing, Cui, Jun, Cui, Yixian, Cui, Yong, Culetto, Emmanuel, Cumino, Andrea, Cybulsky, Andrey, Czaja, Mark, Czuczwar, Stanislaw, D'Adamo, Stefania, D'Amelio, Marcello, D'Arcangelo, Daniela, D'Lugos, Andrew, D'Orazi, Gabriella, da Silva, James, Dafsari, Hormos Salimi, Dagda, Ruben, Dagdas, Yasin, Daglia, Maria, Dai, Xiaoxia, Dai, Yun, Dai, Yuyuan, Dal Col, Jessica, Dalhaimer, Paul, Dalla Valle, Luisa, Dallenga, Tobias, Dalmasso, Guillaume, Damme, Markus, Dando, Ilaria, Dantuma, Nico, Darling, April, Das, Hiranmoy, Dasarathy, Srinivasan, Dasari, Santosh, Dash, Srikanta, Daumke, Oliver, Dauphinee, Adrian, Davies, Jeffrey, Dávila, Valeria, Davis, Roger, Davis, Tanja, Dayalan Naidu, Sharadha, De Amicis, Francesca, De Bosscher, Karolien, De Felice, Francesca, De Franceschi, Lucia, De Leonibus, Chiara, de Mattos Barbosa, Mayara, De Meyer, Guido, De Milito, Angelo, De Nunzio, Cosimo, De Palma, Clara, De Santi, Mauro, De Virgilio, Claudio, De Zio, Daniela, Debnath, Jayanta, DeBosch, Brian, Decuypere, Jean-Paul, Deehan, Mark, Deflorian, Gianluca, DeGregori, James, Dehay, Benjamin, Del Rio, Gabriel, Delaney, Joe, Delbridge, Lea, Delorme-Axford, Elizabeth, Delpino, M Victoria, Demarchi, Francesca, Dembitz, Vilma, Demers, Nicholas, Deng, Hongbin, Deng, Zhiqiang, Dengjel, Joern, Dent, Paul, Denton, Donna, DePamphilis, Melvin, Der, Channing, Deretic, Vojo, Descoteaux, Albert, Devis, Laura, Devkota, Sushil, Devuyst, Olivier, Dewson, Grant, Dharmasivam, Mahendiran, Dhiman, Rohan, di Bernardo, Diego, Di Cristina, Manlio, Di Domenico, Fabio, Di Fazio, Pietro, Di Fonzo, Alessio, Di Guardo, Giovanni, Di Guglielmo, Gianni, Di Leo, Luca, Di Malta, Chiara, Di Nardo, Alessia, Di Rienzo, Martina, Di Sano, Federica, Diallinas, George, Diao, Jiajie, Diaz-Araya, Guillermo, Díaz-Laviada, Inés, Dickinson, Jared, Diederich, Marc, Dieudé, Mélanie, Dikic, Ivan, Ding, Shiping, Ding, Wen-Xing, Dini, Luciana, Dinić, Jelena, Dinic, Miroslav, Dinkova-Kostova, Albena, Dionne, Marc, Distler, Jörg, Diwan, Abhinav, Dixon, Ian, Djavaheri-Mergny, Mojgan, Dobrinski, Ina, Dobrovinskaya, Oxana, Dobrowolski, Radek, Dobson, Renwick, Đokić, Jelena, Dokmeci Emre, Serap, Donadelli, Massimo, Dong, Bo, Dong, Xiaonan, Dong, Zhiwu, Dorn Ii, Gerald, Dotsch, Volker, Dou, Huan, Dou, Juan, Dowaidar, Moataz, Dridi, Sami, Drucker, Liat, Du, Ailian, Du, Caigan, Du, Guangwei, Du, Hai-Ning, Du, Li-Lin, du Toit, André, Duan, Shao-Bin, Duan, Xiaoqiong, Duarte, Sónia, Dubrovska, Anna, Dunlop, Elaine, Dupont, Nicolas, Durán, Raúl, Dwarakanath, Bilikere, Dyshlovoy, Sergey, Ebrahimi-Fakhari, Darius, Eckhart, Leopold, Edelstein, Charles, Efferth, Thomas, Eftekharpour, Eftekhar, Eichinger, Ludwig, Eid, Nabil, Eisenberg, Tobias, Eissa, N Tony, Eissa, Sanaa, Ejarque, Miriam, El Andaloussi, Abdeljabar, El-Hage, Nazira, El-Naggar, Shahenda, Eleuteri, Anna Maria, El-Shafey, Eman, Elgendy, Mohamed, Eliopoulos, Aristides, Elizalde, María, Elks, Philip, Elsasser, Hans-Peter, Elsherbiny, Eslam, Emerling, Brooke, Emre, N., Eng, Christina, Engedal, Nikolai, Engelbrecht, Anna-Mart, Engelsen, Agnete, Enserink, Jorrit, Escalante, Ricardo, Esclatine, Audrey, Escobar-Henriques, Mafalda, Eskelinen, Eeva-Liisa, Espert, Lucile, Eusebio, Makandjou-Ola, Fabrias, Gemma, Fabrizi, Cinzia, Facchiano, Antonio, Facchiano, Francesco, Fadeel, Bengt, Fader, Claudio, Faesen, Alex, Fairlie, W Douglas, Falcó, Alberto, Falkenburger, Bjorn, Fan, Daping, Fan, Jie, Fan, Yanbo, Fang, Evandro, Fang, Yanshan, Fang, Yognqi, Fanto, Manolis, Farfel-Becker, Tamar, Faure, Mathias, Fazeli, Gholamreza, Fedele, Anthony, Feldman, Arthur, Feng, Du, Feng, Jiachun, Feng, Lifeng, Feng, Yibin, Feng, Yuchen, Feng, Wei, Fenz Araujo, Thais, Ferguson, Thomas, Fernández, Álvaro, Fernandez-Checa, Jose, Fernández-Veledo, Sonia, Fernie, Alisdair, Ferrante, Anthony, Ferraresi, Alessandra, Ferrari, Merari, Ferreira, Julio, Ferro-Novick, Susan, Figueras, Antonio, Filadi, Riccardo, Filigheddu, Nicoletta, Filippi-Chiela, Eduardo, Filomeni, Giuseppe, Fimia, Gian Maria, Fineschi, Vittorio, Finetti, Francesca, Finkbeiner, Steven, Fisher, Edward, Fisher, Paul, Flamigni, Flavio, Fliesler, Steven, Flo, Trude, Florance, Ida, Florey, Oliver, Florio, Tullio, Fodor, Erika, Follo, Carlo, Fon, Edward, Forlino, Antonella, Fornai, Francesco, Fortini, Paola, Fracassi, Anna, Fraldi, Alessandro, Franco, Brunella, Franco, Rodrigo, Franconi, Flavia, Frankel, Lisa, Friedman, Scott, Fröhlich, Leopold, Frühbeck, Gema, Fuentes, Jose, Fujiki, Yukio, Fujita, Naonobu, Fujiwara, Yuuki, Fukuda, Mitsunori, Fulda, Simone, Furic, Luc, Furuya, Norihiko, Fusco, Carmela, Gack, Michaela, Gaffke, Lidia, Galadari, Sehamuddin, Galasso, Alessia, Galindo, Maria, Gallolu Kankanamalage, Sachith, Galluzzi, Lorenzo, Galy, Vincent, Gammoh, Noor, Gan, Boyi, Ganley, Ian, Gao, Feng, Gao, Hui, Gao, Minghui, Gao, Ping, Gao, Shou-Jiang, Gao, Wentao, Gao, Xiaobo, Garcera, Ana, Garcia, Maria Noé, Garcia, Verónica, García-del Portillo, Francisco, Garcia-Escudero, Vega, Garcia-Garcia, Aracely, Garcia-Macia, Marina, García-Moreno, Diana, Garcia-Ruiz, Carmen, García-Sanz, Patricia, Garg, Abhishek, Gargini, Ricardo, Garofalo, Tina, Garry, Robert, Gassen, Nils, Gatica, Damian, Ge, Liang, Ge, Wanzhong, Geiss-Friedlander, Ruth, Gelfi, Cecilia, Genschik, Pascal, Gentle, Ian, Gerbino, Valeria, Gerhardt, Christoph, Germain, Kyla, Germain, Marc, Gewirtz, David, Ghasemipour Afshar, Elham, Ghavami, Saeid, Ghigo, Alessandra, Ghosh, Manosij, Giamas, Georgios, Giampietri, Claudia, Giatromanolaki, Alexandra, Gibson, Gary, Gibson, Spencer, Ginet, Vanessa, Giniger, Edward, Giorgi, Carlotta, Girao, Henrique, Girardin, Stephen, Giridharan, Mridhula, Giuliano, Sandy, Giulivi, Cecilia, Giuriato, Sylvie, Giustiniani, Julien, Gluschko, Alexander, Goder, Veit, Goginashvili, Alexander, Golab, Jakub, Goldstone, David, Golebiewska, Anna, Gomes, Luciana, Gomez, Rodrigo, Gómez-Sánchez, Rubén, Gomez-Puerto, Maria Catalina, Gomez-Sintes, Raquel, Gong, Qingqiu, Goni, Felix, González-Gallego, Javier, Gonzalez-Hernandez, Tomas, Gonzalez-Polo, Rosa, Gonzalez-Reyes, Jose, González-Rodríguez, Patricia, Goping, Ing Swie, Gorbatyuk, Marina, Gorbunov, Nikolai, Görgülü, Kıvanç, Gorojod, Roxana, Gorski, Sharon, Goruppi, Sandro, Gotor, Cecilia, Gottlieb, Roberta, Gozes, Illana, Gozuacik, Devrim, Graef, Martin, Gräler, Markus, Granatiero, Veronica, Grasso, Daniel, Gray, Joshua, Green, Douglas, Greenhough, Alexander, Gregory, Stephen, Griffin, Edward, Grinstaff, Mark, Gros, Frederic, Grose, Charles, Gross, Angelina, Gruber, Florian, Grumati, Paolo, Grune, Tilman, Gu, Xueyan, Guan, Jun-Lin, Guardia, Carlos, Guda, Kishore, Guerra, Flora, Guerri, Consuelo, Guha, Prasun, Guillén, Carlos, Gujar, Shashi, Gukovskaya, Anna, Gukovsky, Ilya, Gunst, Jan, Günther, Andreas, Guntur, Anyonya, Guo, Chuanyong, Guo, Chun, Guo, Hongqing, Guo, Lian-Wang, Guo, Ming, Gupta, Pawan, Gupta, Shashi Kumar, Gupta, Swapnil, Gupta, Veer Bala, Gupta, Vivek, Gustafsson, Asa, Gutterman, David, H B, Ranjitha, Haapasalo, Annakaisa, Haber, James, Hać, Aleksandra, Hadano, Shinji, Hafrén, Anders, Haidar, Mansour, Hall, Belinda, Halldén, Gunnel, Hamacher-Brady, Anne, Hamann, Andrea, Hamasaki, Maho, Han, Weidong, Hansen, Malene, Hanson, Phyllis, Hao, Zijian, Harada, Masaru, Harhaji-Trajkovic, Ljubica, Hariharan, Nirmala, Haroon, Nigil, Harris, James, Hasegawa, Takafumi, Hasima Nagoor, Noor, Haspel, Jeffrey, Haucke, Volker, Hawkins, Wayne, Hay, Bruce, Haynes, Cole, Hayrabedyan, Soren, Hays, Thomas, He, Congcong, He, Qin, He, Rong-Rong, He, You-Wen, He, Yu-Ying, Heakal, Yasser, Heberle, Alexander, Hejtmancik, J Fielding, Helgason, Gudmundur Vignir, Henkel, Vanessa, Herb, Marc, Hergovich, Alexander, Herman-Antosiewicz, Anna, Hernández, Agustín, Hernandez, Carlos, Hernandez-Diaz, Sergio, Hernandez-Gea, Virginia, Herpin, Amaury, Herreros, Judit, Hervás, Javier, Hesselson, Daniel, Hetz, Claudio, Heussler, Volker, Higuchi, Yujiro, Hilfiker, Sabine, Hill, Joseph, Hlavacek, William, Ho, Emmanuel, Ho, Idy, Ho, Philip Wing-Lok, Ho, Shu-Leong, Ho, Wan Yun, Hobbs, G Aaron, Hochstrasser, Mark, Hoet, Peter, Hofius, Daniel, Hofman, Paul, Höhn, Annika, Holmberg, Carina, Hombrebueno, Jose, Yi-Ren Hong, Chang-Won Hong, Hooper, Lora, Hoppe, Thorsten, Horos, Rastislav, Hoshida, Yujin, Hsin, I-Lun, Hsu, Hsin-Yun, Hu, Bing, Hu, Dong, Hu, Li-Fang, Hu, Ming Chang, Hu, Ronggui, Hu, Wei, Hu, Yu-Chen, Hu, Zhuo-Wei, Hua, Fang, Hua, Jinlian, Hua, Yingqi, Huan, Chongmin, Huang, Canhua, Huang, Chuanshu, Huang, Chuanxin, Huang, Chunling, Huang, Haishan, Huang, Kun, Huang, Michael, Huang, Rui, Huang, Shan, Huang, Tianzhi, Huang, Xing, Huang, Yuxiang Jack, Huber, Tobias, Hubert, Virginie, Hubner, Christian, Hughes, Stephanie, Hughes, William, Humbert, Magali, Hummer, Gerhard, Hurley, James, Hussain, Sabah, Hussain, Salik, Hussey, Patrick, Hutabarat, Martina, Hwang, Hui-Yun, Hwang, Seungmin, Ieni, Antonio, Ikeda, Fumiyo, Imagawa, Yusuke, Imai, Yuzuru, Imbriano, Carol, Imoto, Masaya, Inman, Denise, Inoki, Ken, Iovanna, Juan, Iozzo, Renato, Ippolito, Giuseppe, Irazoqui, Javier, Iribarren, Pablo, Ishaq, Mohd, ISHIKAWA, Makoto, Ishimwe, Nestor, Isidoro, Ciro, Ismail, Nahed, Issazadeh-Navikas, Shohreh, Itakura, Eisuke, Ito, Daisuke, Ivankovic, Davor, Ivanova, Saška, Iyer, Anand Krishnan V, Izquierdo, José, Izumi, Masanori, Jäättelä, Marja, Jabir, Majid Sakhi, Jackson, William, Jacobo-Herrera, Nadia, Jacomin, Anne-Claire, Jacquin, Elise, Jadiya, Pooja, Jaeschke, Hartmut, Jagannath, Chinnaswamy, Jakobi, Arjen, Jakobsson, Johan, Janji, Bassam, Jansen-Dürr, Pidder, Jansson, Patric, Jantsch, Jonathan, Januszewski, Sławomir, Jassey, Alagie, Jean, Steve, Jeltsch-David, Hélène, Jendelova, Pavla, Jenny, Andreas, Jensen, Thomas, Jessen, Niels, Jewell, Jenna, Ji, Jing, Jia, Lijun, Jia, Rui, Jiang, Liwen, Jiang, Qing, Jiang, Richeng, Jiang, Teng, Jiang, Xuejun, Jiang, Yu, Jimenez-Sanchez, Maria, Jin, Eun-Jung, Jin, Fengyan, Jin, Hongchuan, Jin, Li, Jin, Luqi, Jin, Meiyan, Jin, Si, Jo, Eun-Kyeong, Joffre, Carine, Johansen, Terje, Johnson, Gail, Johnston, Simon, Jokitalo, Eija, Jolly, Mohit Kumar, Joosten, Leo, Jordan, Joaquin, Joseph, Bertrand, Ju, Dianwen, Ju, Jeong-Sun, Ju, Jingfang, Juárez, Esmeralda, Judith, Delphine, Juhász, Gábor, Jun, Youngsoo, Jung, Chang Hwa, Jung, Sung-Chul, Jung, Yong Keun, Jungbluth, Heinz, Jungverdorben, Johannes, Just, Steffen, Kaarniranta, Kai, Kaasik, Allen, Kabuta, Tomohiro, Kaganovich, Daniel, Kahana, Alon, Kain, Renate, Kajimura, Shinjo, Kalamvoki, Maria, Kalia, Manjula, Kalinowski, Danuta, Kaludercic, Nina, Kalvari, Ioanna, Kaminska, Joanna, Kaminskyy, Vitaliy, Kanamori, Hiromitsu, Kanasaki, Keizo, Kang, Chanhee, Kang, Rui, Kang, Sang Sun, Kaniyappan, Senthilvelrajan, 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Humberto, Roelofs, Jeroen, Rogers, Robert, Rogov, Vladimir, ROJO, Ana, Rolka, Krzysztof, Romanello, Vanina, Romani, Luigina, Romano, Alessandra, Romano, Patricia, Romeo-Guitart, David, Romero, Luis, Romero, Montserrat, Roney, Joseph, Rongo, Christopher, Roperto, Sante, Rosenfeldt, Mathias, Rosenstiel, Philip, Rosenwald, Anne, Roth, Kevin, Roth, Lynn, Roth, Steven, Rouschop, Kasper, Roussel, Benoit, Roux, Sophie, Rovere-Querini, Patrizia, Roy, Ajit, Rozieres, Aurore, Ruano, Diego, Rubinsztein, David, Rubtsova, Maria, Ruckdeschel, Klaus, Ruckenstuhl, Christoph, Rudolf, Emil, Rudolf, Rüdiger, Ruggieri, Alessandra, Ruparelia, Avnika Ashok, Rusmini, Paola, Russell, Ryan, Russo, Gian Luigi, Russo, Maria, Russo, Rossella, Ryabaya, Oxana, Ryan, Kevin, Ryu, Kwon-Yul, Sabater-Arcis, Maria, Sachdev, Ulka, Sacher, Michael, Sachse, Carsten, Sadhu, Abhishek, Sadoshima, Junichi, Safren, Nathaniel, Saftig, Paul, Sagona, Antonia, Sahay, Gaurav, Sahebkar, Amirhossein, Sahin, Mustafa, Sahin, Ozgur, Sahni, Sumit, Saito, Nayuta, Saito, Shigeru, Saito, Tsunenori, Sakai, Ryohei, Sakai, Yasuyoshi, Sakamaki, Jun-Ichi, Saksela, Kalle, Salazar, Gloria, Salazar-Degracia, Anna, Salekdeh, Ghasem, Saluja, Ashok, Sampaio-Marques, Belém, Sanchez, Maria Cecilia, Sanchez-Alcazar, Jose, Sanchez-Vera, Victoria, Sancho-Shimizu, Vanessa, Sanderson, J Thomas, Sandri, Marco, Santaguida, Stefano, Santambrogio, Laura, Santana, Magda, Santoni, Giorgio, Sanz, Alberto, Sanz, Pascual, Saran, Shweta, Sardiello, Marco, Sargeant, Timothy, Sarin, Apurva, Sarkar, Chinmoy, Sarkar, Sovan, Sarrias, Maria-Rosa, Sarkar, Surajit, Sarmah, Dipanka Tanu, Sarparanta, Jaakko, Sathyanarayan, Aishwarya, Sathyanarayanan, Ranganayaki, Scaglione, K Matthew, Scatozza, Francesca, Schaefer, Liliana, Schafer, Zachary, Schaible, Ulrich, Schapira, Anthony, Scharl, Michael, Schatzl, Hermann, Schein, Catherine, Scheper, Wiep, Scheuring, David, Schiaffino, Maria Vittoria, Schiappacassi, Monica, Schindl, Rainer, Schlattner, Uwe, Schmidt, Oliver, Schmitt, Roland, Schmidt, Stephen, Schmitz, Ingo, Schmukler, Eran, Schneider, Anja, Schneider, Bianca, Schober, Romana, Schoijet, Alejandra, Schott, Micah, Schramm, Michael, Schröder, Bernd, Schuh, Kai, Schüller, Christoph, Schulze, Ryan, Schürmanns, Lea, Schwamborn, Jens, Schwarten, Melanie, Scialo, Filippo, Sciarretta, Sebastiano, Scott, Melanie, Scotto, Kathleen, Scovassi, A Ivana, Scrima, Andrea, Scrivo, Aurora, Sebastian, David, Sebti, Salwa, Sedej, Simon, Segatori, Laura, Segev, Nava, Seglen, Per, Seiliez, Iban, Seki, Ekihiro, Selleck, Scott, Sellke, Frank, Selsby, Joshua, Sendtner, Michael, Senturk, Serif, Seranova, Elena, Sergi, Consolato, Serra-Moreno, Ruth, Sesaki, Hiromi, Settembre, Carmine, Setty, Subba Rao Gangi, Sgarbi, Gianluca, Sha, Ou, Shacka, John, Shah, Javeed, Shang, Dantong, Shao, Changshun, Shao, Feng, Sharbati, Soroush, Sharkey, Lisa, Sharma, Dipali, Sharma, Gaurav, Sharma, Kulbhushan, Sharma, Pawan, Sharma, Surendra, Shen, Han-Ming, Shen, Hongtao, Shen, Jiangang, Shen, Ming, Shen, Weili, Shen, Zheni, Sheng, Rui, Sheng, Zhi, Sheng, Zu-Hang, Shi, Jianjian, Shi, Xiaobing, Shi, Ying-Hong, Shiba-Fukushima, Kahori, Shieh, Jeng-Jer, Shimada, Yohta, Shimizu, Shigeomi, Shimozawa, Makoto, Shintani, Takahiro, Shoemaker, Christopher, Shojaei, Shahla, Shoji, Ikuo, Shravage, Bhupendra, Shridhar, Viji, Shu, Chih-Wen, Shu, Hong-Bing, Shui, Ke, Shukla, Arvind, Shutt, Timothy, Sica, Valentina, Siddiqui, Aleem, Sierra, Amanda, Sierra-Torre, Virginia, Signorelli, Santiago, Sil, Payel, Silva, Bruno, Silva, Johnatas, Silva-Pavez, Eduardo, Silvente-Poirot, Sandrine, Simmonds, Rachel, Simon, Anna Katharina, Simon, Hans-Uwe, Simons, Matias, Singh, Anurag, Singh, Lalit, Singh, Rajat, Singh, Shivendra, Singh, Shrawan, Singh, Sudha, Singh, Sunaina, Singh, Surinder Pal, Sinha, Debasish, Sinha, Rohit Anthony, Sinha, Sangita, Sirko, Agnieszka, Sirohi, Kapil, Sivridis, Efthimios, Skendros, Panagiotis, Skirycz, Aleksandra, Slaninová, Iva, Smaili, Soraya, Smertenko, Andrei, Smith, Matthew, Soenen, Stefaan, Sohn, Eun Jung, Sok, Sophia, Solaini, Giancarlo, Soldati, Thierry, Soleimanpour, Scott, Soler, Rosa, Solovchenko, Alexei, Somarelli, Jason, Sonawane, Avinash, Song, Fuyong, Song, Hyun Kyu, Song, Ju-Xian, Song, Kunhua, Song, Zhiyin, Soria, Leandro, Sorice, Maurizio, Soukas, Alexander, Soukup, Sandra-Fausia, Sousa, Diana, Sousa, Nadia, Spagnuolo, Paul, Spector, Stephen, Srinivas Bharath, M., St Clair, Daret, Stagni, Venturina, Staiano, Leopoldo, Stalnecker, Clint, Stankov, Metodi, Stathopulos, Peter, Stefan, Katja, Stefan, Sven Marcel, Stefanis, Leonidas, Steffan, Joan, Steinkasserer, Alexander, Stenmark, Harald, Sterneckert, Jared, Stevens, Craig, Stoka, Veronika, Storch, Stephan, Stork, Björn, Strappazzon, Flavie, Strohecker, Anne Marie, Stupack, Dwayne, Su, Huanxing, Su, Ling-Yan, Su, Longxiang, Suarez-Fontes, Ana, Subauste, Carlos, Subbian, Selvakumar, Subirada, Paula, Sudhandiran, Ganapasam, Sue, Carolyn, Sui, Xinbing, Summers, Corey, Sun, Guangchao, Sun, Jun, SUN, Kang, Sun, Meng-xiang, Sun, Qiming, Sun, Yi, Sun, Zhongjie, Sunahara, Karen, Sundberg, Eva, Susztak, Katalin, Sutovsky, Peter, Suzuki, Hidekazu, Sweeney, Gary, Symons, J David, Sze, Stephen Cho Wing, Szewczyk, Nathaniel, Tabęcka-Łonczynska, Anna, Tabolacci, Claudio, Tacke, Frank, Taegtmeyer, Heinrich, Tafani, Marco, Tagaya, Mitsuo, Tai, Haoran, Tait, Stephen, Takahashi, Yoshinori, Takats, Szabolcs, Talwar, Priti, Tam, Chit, Tam, Shing Yau, Tampellini, Davide, Tamura, Atsushi, Tan, Chong Teik, Tan, Eng-King, Tan, Ya-Qin, Tanaka, Masaki, Tanaka, Motomasa, Tang, Daolin, Tang, Jingfeng, Tang, Tie-Shan, Tanida, Isei, Tao, Zhipeng, Taouis, Mohammed, Tatenhorst, Lars, Tavernarakis, Nektarios, Taylor, Allen, Taylor, Gregory, Taylor, Joan, Tchetina, Elena, Tee, Andrew, Tegeder, Irmgard, Teis, David, Teixeira, Natercia, Teixeira-Clerc, Fatima, Tekirdag, Kumsal, Tencomnao, Tewin, Tenreiro, Sandra, Tepikin, Alexei, Testillano, Pilar, Tettamanti, Gianluca, Tharaux, Pierre-Louis, Thedieck, Kathrin, Thekkinghat, Arvind, Thellung, Stefano, Thinwa, Josephine, Thirumalaikumar, V.P., Thomas, Sufi Mary, Thomes, Paul, Thorburn, Andrew, Thukral, Lipi, Thum, Thomas, Thumm, Michael, Tian, Ling, Tichy, Ales, Till, Andreas, Timmerman, Vincent, Titorenko, Vladimir, Todi, Sokol, Todorova, Krassimira, Toivonen, Janne, Tomaipitinca, Luana, Tomar, Dhanendra, Tomas-Zapico, Cristina, Tomić, Sergej, Tong, Benjamin Chun-Kit, Tong, Chao, Tong, Xin, Tooze, Sharon, Torgersen, Maria, Torii, Satoru, Torres-López, Liliana, Torriglia, Alicia, Towers, Christina, Towns, Roberto, Toyokuni, Shinya, Trajkovic, Vladimir, Tramontano, Donatella, Tran, Quynh-Giao, Travassos, Leonardo, Trelford, Charles, Tremel, Shirley, Trougakos, Ioannis, Tsao, Betty, Tschan, Mario, Tse, Hung-Fat, Tse, Tak Fu, Tsugawa, Hitoshi, Tsvetkov, Andrey, Tumbarello, David, Tumtas, Yasin, Tuñón, María, Turcotte, Sandra, Turk, Boris, Turk, Vito, Turner, Bradley, Tuxworth, Richard, Tyler, Jessica, Tyutereva, Elena, Uchiyama, Yasuo, Ugun-Klusek, Aslihan, Uhlig, Holm, Ułamek-Kozioł, Marzena, Ulasov, Ilya, Umekawa, Midori, Ungermann, Christian, Unno, Rei, Urbe, Sylvie, Uribe-Carretero, Elisabet, Üstün, Suayib, Uversky, Vladimir, Vaccari, Thomas, Vaccaro, Maria, Vahsen, Björn, Vakifahmetoglu-Norberg, Helin, Valdor, Rut, Valente, Maria, Valko, Ayelén, Vallee, Richard, Valverde, Angela, Van Den Berghe, Greet, van der Veen, Stijn, Van Kaer, Luc, van Loosdregt, Jorg, van Wijk, Sjoerd, Vandenberghe, Wim, Vanhorebeek, Ilse, Vannier-Santos, Marcos, Vannini, Nicola, Vanrell, M Cristina, Vantaggiato, Chiara, Varano, Gabriele, Varela-Nieto, Isabel, Varga, Máté, Vasconcelos, M Helena, Vats, Somya, Vavvas, Demetrios, Vega-Naredo, Ignacio, Vega-Rubin-de-Celis, Silvia, Velasco, Guillermo, Velázquez, Ariadna, Vellai, Tibor, Vellenga, Edo, Velotti, Francesca, Verdier, Mireille, Verginis, Panayotis, Vergne, Isabelle, Verkade, Paul, Verma, Manish, Verstreken, Patrik, Vervliet, Tim, Vervoorts, Jörg, Vessoni, Alexandre, Victor, Victor, Vidal, Michel, Vidoni, Chiara, Vieira, Otilia, Vierstra, Richard, Viganó, Sonia, Vihinen, Helena, Vijayan, Vinoy, Vila, Miquel, Vilar, Marçal, Villalba, José, Villalobo, Antonio, Villarejo-Zori, Beatriz, Villarroya, Francesc, Villarroya, Joan, Vincent, Olivier, Vindis, Cecile, Viret, Christophe, Viscomi, Maria Teresa, Visnjic, Dora, Vitale, Ilio, Vocadlo, David, Voitsekhovskaja, Olga, Volonté, Cinzia, Volta, Mattia, Vomero, Marta, Von Haefen, Clarissa, Vooijs, Marc, Voos, Wolfgang, Vucicevic, Ljubica, Wade-Martins, Richard, Waguri, Satoshi, Waite, Kenrick, Wakatsuki, Shuji, Walker, David, Walker, Mark, Walker, Simon, Walter, Jochen, Wandosell, Francisco, Wang, Bo, Wang, Chao-Yung, Wang, Chen, Wang, Chenran, Wang, Chenwei, Wang, Cun-Yu, Wang, Dong, Wang, Fangyang, Wang, Feng, Wang, Fengming, Wang, Guansong, Wang, Han, Wang, Hao, Wang, Hexiang, Wang, Hong-Gang, Wang, Jianrong, Wang, Jigang, Wang, Jiou, Wang, Jundong, Wang, Kui, Wang, Lianrong, Wang, Liming, Wang, Maggie Haitian, Wang, Meiqing, Wang, Nanbu, Wang, PengWei, Wang, PeiPei, Wang, Ping, Wang, Qing Jun, Wang, Qing, Wang, Qing Kenneth, Wang, Qiong, Wang, Wen-Tao, Wang, Wuyang, Wang, Xinnan, Wang, Xuejun, Wang, Yan, Wang, Yanchang, Wang, Yanzhuang, Wang, Yen-Yun, Wang, Yihua, Wang, Yipeng, Wang, Yu, wang, yuqi, Wang, Zhe, Wang, Zhenyu, Wang, Zhouguang, Warnes, Gary, Warnsmann, Verena, Watada, Hirotaka, Watanabe, Eizo, Watchon, Maxinne, Wawrzyńska, Anna, Weaver, Timothy, Wegrzyn, Grzegorz, Wehman, Ann, Wei, Huafeng, Wei, Lei, Wei, Taotao, Wei, Yongjie, Weiergräber, Oliver, Weihl, Conrad, Weindl, Günther, Weiskirchen, Ralf, Wells, Alan, Wen, Runxia, Wen, Xin, Werner, Antonia, Weykopf, Beatrice, Wheatley, Sally, Whitton, J Lindsay, Whitworth, Alexander, Wiktorska, Katarzyna, Wildenberg, Manon, Wileman, Tom, Wilkinson, Simon, Willbold, Dieter, Williams, Brett, Williams, Robin, Williams, Roger, Williamson, Peter, Wilson, Richard, Winner, Beate, Winsor, Nathaniel, Witkin, Steven, Wodrich, Harald, Woehlbier, Ute, Wollert, Thomas, Wong, Esther, Wong, Jack Ho, Wong, Richard, Wong, Vincent Kam Wai, Wong, W Wei-Lynn, Wu, An-Guo, Wu, Chengbiao, Wu, Jian, Wu, Junfang, Wu, Kenneth, Wu, Min, Wu, Shan-Ying, Wu, Shengzhou, Wu, Shu-Yan, Wu, Shufang, Wu, William, Wu, Xiaohong, Wu, Xiaoqing, Wu, Yao-Wen, Wu, Yihua, Xavier, Ramnik, Xia, Hongguang, Xia, Lixin, Xia, Zhengyuan, Xiang, Ge, Xiang, Jin, Xiang, Mingliang, Xiang, Wei, Xiao, Bin, Xiao, Guozhi, Xiao, Hengyi, Xiao, Hong-tao, Xiao, Jian, Xiao, Lan, Xiao, Shi, Xiao, Yin, Xie, Baoming, Xie, Chuan-Ming, Xie, Min, Xie, Yuxiang, Xie, Zhiping, Xie, Zhonglin, Xilouri, Maria, Xu, Congfeng, Xu, En, Xu, Haoxing, Xu, Jing, Xu, Jinrong, Xu, Liang, Xu, Wen Wen, Xu, Xiulong, Xue, Yu, Yakhine-Diop, Sokhna, Yamaguchi, Masamitsu, Yamaguchi, Osamu, Yamamoto, Ai, Yamashina, Shunhei, Yan, Shengmin, Yan, Shian-Jang, Yan, Zhen, Yanagi, Yasuo, Yang, Chuanbin, Yang, Dun-Sheng, Yang, Huan, Yang, Huang-Tian, Yang, Hui, Yang, Jin-Ming, Yang, Jing, Yang, Jingyu, Yang, Ling, Yang, Liu, Yang, Ming, Yang, Pei-Ming, Yang, Qian, Yang, Seungwon, Yang, Shu, Yang, Shun-Fa, Yang, Wannian, Yang, Wei Yuan, Yang, Xiaoyong, Yang, Xuesong, Yang, Yi, Yang, Ying, Yao, Honghong, Yao, Shenggen, Yao, Xiaoqiang, Yao, Yong-Gang, Yao, Yong-Ming, Yasui, Takahiro, Yazdankhah, Meysam, Yen, Paul, Yi, Cong, Yin, Xiao-Ming, Yin, Yanhai, Yin, Zhangyuan, Yin, Ziyi, Ying, Meidan, Ying, Zheng, Yip, Calvin, Yiu, Stephanie Pei Tung, Yoo, Young, Yoshida, Kiyotsugu, Yoshii, Saori, Yoshimori, Tamotsu, Yousefi, Bahman, Yu, Boxuan, Yu, Haiyang, Yu, Jun, Yu, Li, Yu, Ming-Lung, Yu, Seong-Woon, Yu, Victor, Yu, W Haung, Yu, Zhengping, Yu, Zhou, Yuan, Junying, Yuan, Ling-Qing, Yuan, Shilin, Yuan, Shyng-Shiou, Yuan, Yanggang, Yuan, Zengqiang, Yue, Jianbo, Yue, Zhenyu, Yun, Jeanho, Yung, Raymond, Zacks, David, Zaffagnini, Gabriele, Zambelli, Vanessa, Zanella, Isabella, Zang, Qun, Zanivan, Sara, Zappavigna, Silvia, Zaragoza, Pilar, Zarbalis, Konstantinos, Zarebkohan, Amir, Zarrouk, Amira, Zeitlin, Scott, Zeng, Jialiu, Zeng, Ju-deng, Žerovnik, Eva, Zhan, Lixuan, Zhang, Bin, Zhang, Donna, Zhang, Hanlin, Zhang, Hong, Zhang, Honghe, Zhang, Huafeng, Zhang, Huaye, Zhang, Hui, Zhang, Hui-Ling, Zhang, Jianbin, Zhang, Jianhua, Zhang, Jing-Pu, Zhang, Kalin, Zhang, Leshuai, Zhang, Lin, Zhang, Lisheng, Zhang, Lu, Zhang, Luoying, Zhang, Menghuan, Zhang, Peng, Zhang, Sheng, Zhang, Wei, Zhang, Xiangnan, Zhang, Xiao-Wei, Zhang, Xiaolei, Zhang, Xiaoyan, Zhang, Xin, Zhang, Xinxin, Zhang, Xu Dong, Zhang, Yang, Zhang, Yanjin, Zhang, Yi, Zhang, Ying-Dong, Zhang, Yingmei, Zhang, Yuan-Yuan, Zhang, Yuchen, Zhang, Zhe, Zhang, Zhengguang, Zhang, Zhibing, Zhang, Zhihai, Zhang, Zhiyong, Zhang, Zili, Zhao, Haobin, Zhao, Lei, Zhao, Shuang, Zhao, Tongbiao, Zhao, Xiao-Fan, Zhao, Ying, Zhao, Yongchao, Zhao, Yongliang, Zhao, Yuting, Zheng, Guoping, Zheng, Kai, Zheng, Ling, Zheng, Shizhong, Zheng, Xi-Long, Zheng, Yi, Zheng, Zu-Guo, Zhivotovsky, Boris, Zhong, Qing, Zhou, Ao, Zhou, Ben, Zhou, Cefan, ZHOU, Gang, Zhou, Hao, Zhou, Hong, Zhou, Hongbo, Zhou, Jie, Zhou, Jing, Zhou, Jiyong, Zhou, Kailiang, Zhou, Rongjia, Zhou, Xu-jie, Zhou, Yanshuang, Zhou, Yinghong, Zhou, Yubin, Zhou, Zheng-Yu, Zhou, Zhou, Zhu, Binglin, Zhu, Changlian, Zhu, Guo-Qing, Zhu, Haining, Zhu, Hongxin, Zhu, Hua, Zhu, Wei-Guo, Zhu, Yanping, Zhu, Yushan, Zhuang, Haixia, Zhuang, Xiaohong, Zientara-Rytter, Katarzyna, Zimmermann, Christine, Ziviani, Elena, Zoladek, Teresa, Zong, Wei-Xing, Zorov, Dmitry, Zorzano, Antonio, Zou, Weiping, Zou, Zhen, Zou, Zhengzhi, Zuryn, Steven, Zwerschke, Werner, Brand-Saberi, Beate, Dong, X Charlie, Kenchappa, Chandra Shekar, Li, Zuguo, Lin, Yong, Oshima, Shigeru, Rong, Yueguang, Sluimer, Judith, Stallings, Christina, Tong, Chun-Kit, Ahmad, S. Tariq, Alim Al-Bari, M. Abdul, Bechara, Luiz R.G., Behrens, Georg M.N., Bhuiyan, Md. Shenuarin, Broaddus, V. Courtney, Buchan, J. Ross, Burón, M. Isabel, Carter, A. Brent, Chan, Matthew T.V., Choi, Augustine M.K., D’Adamo, Stefania, D’Amelio, Marcello, D’Arcangelo, Daniela, D’Lugos, Andrew, D’Orazi, Gabriella, De Meyer, Guido R.Y., Delpino, M. Victoria, Distler, Jörg H.W., Dixon, Ian M.C., Dobson, Renwick C.J., 2nd Dorn, Gerald, Eissa, N. Tony, Engelsen, Agnete S.T., Fairlie, W. Douglas, Ferreira, Julio C.B., H.B., Ranjitha, Hanson, Phyllis I., Hejtmancik, J. Fielding, Ho, Idy H.T., Hobbs, G. Aaron, Hoet, Peter H.M., Huang, Michael L.H., Iyer, Anand Krishnan V., Johnson, Gail V.W., Joosten, Leo A.B., Karim, Md. Razaul, Kaufmann, Stefan H.E., Ko, Ben C.B., Leck, Lionel Y.W., Lima, Thania R.R., Livingston, J. Andrew, Martin, Alexandre P.J., Montes, L. Ruth, Murphy, J. Patrick, Ng, Charlene C.W., Nicolao, M. 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Charlie, Ain Shams University [ASU], Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University [JGU], Medical University Graz, Centre de Recherche des Cordeliers [CRC (UMR_S_1138 / U1138)], Institut Pasteur d'Iran, Leibniz Institute of Plant Biochemistry [IPB], The University of Texas M.D. Anderson Cancer Center [Houston], Institut de pharmacologie moléculaire et cellulaire [IPMC], Lipides - Nutrition - Cancer [Dijon - U1231] [LNC], Centre méditerranéen de médecine moléculaire [C3M], Institut de Biologie Valrose [IBV], Petites Molécules de neuroprotection, neurorégénération et remyélinisation, Signalisation Hormonale, Physiopathologie Endocrinienne et Métabolique, Institut Cochin [IC UM3 (UMR 8104 / U1016)], Institut NeuroMyoGène [INMG], Paris-Centre de Recherche Cardiovasculaire [PARCC (UMR_S 970/ U970)], Marqueurs cardiovasculaires en situation de stress [MASCOT (UMR_S_942 / U942)], Institut de Recherche sur le Cancer et le Vieillissement [IRCAN], Centre de Recherche en Cancérologie de Marseille [CRCM], Centre for Integrative Biology - CBI [Inserm U964 - CNRS UMR7104 - IGBMC], Oncogenesis, Stress, Signaling [OSS], Institut Necker Enfants-Malades [INEM - UM 111 (UMR 8253 / U1151)], Institut de Biologie Intégrative de la Cellule [I2BC], Microbes, Intestin, Inflammation et Susceptibilité de l'Hôte [M2iSH], Centre de Recherches en Cancérologie de Toulouse [CRCT], Physiopathologie et traitement des maladies du foie, Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL], Institut de Génétique et de Biologie Moléculaire et Cellulaire [IGBMC], Laboratoire Bio-PeroxIL. Biochimie du peroxysome, inflammation et métabolisme lipidique [Dijon] [BIO-PEROXIL], Centre de recherche sur l'Inflammation [CRI (UMR_S_1149 / ERL_8252 / U1149)], Unité de génétique et biologie des cancers [U830], Laboratoire d'Optique et Biosciences [LOB], Institut des Maladies Métaboliques et Casdiovasculaires [UPS/Inserm U1297 - I2MC], Différenciation et communication neuronale et neuroendocrine [DC2N], Institut de Recherche en Cancérologie de Montpellier [IRCM - U1194 Inserm - UM], Centre d'Immunologie de Marseille - Luminy [CIML], Physiopathologie et imagerie des troubles neurologiques [PhIND], Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée [LBFA], Imagine - Institut des maladies génétiques (IHU) [Imagine - U1163], Institut Mondor de Recherche Biomédicale [IMRB], Franco-czech Laboratory for clinical research on obesity, University of Michigan [Ann Arbor], University of Michigan System, Institut de pharmacologie moléculaire et cellulaire (IPMC), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Université Côte d'Azur (UCA), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Centre méditerranéen de médecine moléculaire (C3M), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA), Institut de Biologie Valrose (IBV), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Sud - Paris 11 (UP11), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Nutrition, Métabolisme, Aquaculture (NuMéA), Université de Pau et des Pays de l'Adour (UPPA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut NeuroMyoGène (INMG), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Paris-Centre de Recherche Cardiovasculaire (PARCC - UMR-S U970), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Institut de Recherche sur le Cancer et le Vieillissement (IRCAN), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bourgogne Franche-Comté [COMUE] (UBFC), Centre de Recherche en Cancérologie de Marseille (CRCM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), Centre for Integrative Biology - CBI (Inserm U964 - CNRS UMR7104 - IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institute of genetics and molecular and cellular biology-Centre National de la Recherche Scientifique (CNRS), Chemistry, Oncogenesis, Stress and Signaling (COSS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Necker Enfants-Malades (INEM - UM 111 (UMR 8253 / U1151)), Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Microbes, Intestin, Inflammation et Susceptibilité de l'Hôte (M2iSH), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre de Recherche en Nutrition Humaine d'Auvergne (CRNH d'Auvergne)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Centre International de Recherche en Infectiologie - UMR (CIRI), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherches en Cancérologie de Toulouse (CRCT), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie et Génomique des Poissons (LPGP), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 (CIIL), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Laboratoire Bio-PeroxIL. Biochimie du peroxysome, inflammation et métabolisme lipidique [Dijon] (BIO-PEROXIL), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Centre de recherche sur l'Inflammation (CRI (UMR_S_1149 / ERL_8252 / U1149)), Institut Jean-Pierre Bourgin (IJPB), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire d'Optique et Biosciences (LOB), École polytechnique (X)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Différenciation et communication neuronale et neuroendocrine (DC2N), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre d'Immunologie de Marseille - Luminy (CIML), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée (LBFA), Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epithelial biology and disease - Liliane Bettencourt Chair of Developmental Biology (Equipe Inserm U1163), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Biomécanique cellulaire et respiratoire (BCR), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Charles University [Prague]-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported by the National Institute of General Medical Sciences [GM131919]., Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Marqueurs cardiovasculaires en situation de stress (MASCOT (UMR_S_942 / U942)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Université Sorbonne Paris Nord, Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie des Adaptations Nutritionnelles (PhAN), Université de Nantes (UN)-Institut National de la Recherche Agronomique (INRA), Département Plateforme (PF I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Gustave Roussy (IGR), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Chinese Academy of Medical Sciences [Suzhou, Chine] (CAMS), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Department of Women's and Children's Health [Stockholm, Sweden], Centre National de la Recherche Scientifique (CNRS)-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École polytechnique (X), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, FHU OncoAge - Pathologies liées à l’âge [CHU Nice] (OncoAge), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Institut de Pharmacologie Moléculaire et Cellulaire [UNIV Côte d'Azur] (UPMC), Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Life Sciences Institute [Ann Arbor, MI, USA], University of Michigan System-University of Michigan System, European Institute of Oncology IRCCS [Milan, Italy] (EIO), Ain Shams University (ASU), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Réseau International des Instituts Pasteur (RIIP), Leibniz Institute of Plant Biochemistry (IPB), Hebrew University of Jerusalem, Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Agro Dijon, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, 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), Oncogenesis, Stress, Signaling (OSS), Institut des Maladies Métaboliques et Casdiovasculaires (UPS/Inserm U1297 - I2MC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Charles University [Prague] (CU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Klionsky, D. J., Abdel-Aziz, A. K., Abdelfatah, S., Abdellatif, M., Abdoli, A., Abel, S., Abeliovich, H., Abildgaard, M. H., Abudu, Y. P., Acevedo-Arozena, A., Adamopoulos, I. E., Adeli, K., Adolph, T. E., Adornetto, A., Aflaki, E., Agam, G., Agarwal, A., Aggarwal, B. B., Agnello, M., Agostinis, P., Agrewala, J. N., Agrotis, A., Aguilar, P. V., Ahmad, S. T., Ahmed, Z. M., Ahumada-Castro, U., Aits, S., Aizawa, S., Akkoc, Y., Akoumianaki, T., Akpinar, H. A., Al-Abd, A. M., Al-Akra, L., Al-Gharaibeh, A., Alaoui-Jamali, M. A., Alberti, S., Alcocer-Gomez, E., Alessandri, C., Ali, M., Alim Al-Bari, M. A., Aliwaini, S., Alizadeh, J., Almacellas, E., Almasan, A., Alonso, A., Alonso, G. D., Altan-Bonnet, N., Altieri, D. C., Alvarez, E. M. C., Alves, S., Alves da Costa, C., Alzaharna, M. M., Amadio, M., Amantini, C., Amaral, C., Ambrosio, S., Amer, A. O., Ammanathan, V., An, Z., Andersen, S. U., Andrabi, S. A., Andrade-Silva, M., Andres, A. M., Angelini, S., Ann, D., Anozie, U. C., Ansari, M. Y., Antas, P., Antebi, A., Anton, Z., Anwar, T., Apetoh, L., Apostolova, N., Araki, T., Araki, Y., Arasaki, K., Araujo, W. L., Araya, J., Arden, C., Arevalo, M. -A., Arguelles, S., Arias, E., Arikkath, J., Arimoto, H., Ariosa, A. R., Armstrong-James, D., Arnaune-Pelloquin, L., Aroca, A., Arroyo, D. S., Arsov, I., Artero, R., Asaro, D. M. L., Aschner, M., Ashrafizadeh, M., Ashur-Fabian, O., Atanasov, A. G., Au, A. K., Auberger, P., Auner, H. W., Aurelian, L., Autelli, R., Avagliano, L., Avalos, Y., Aveic, S., Aveleira, C. A., Avin-Wittenberg, T., Aydin, Y., Ayton, S., Ayyadevara, S., Azzopardi, M., Baba, M., Backer, J. M., Backues, S. K., Bae, D. -H., Bae, O. -N., Bae, S. H., Baehrecke, E. H., Baek, A., Baek, S. -H., Baek, S. H., Bagetta, G., Bagniewska-Zadworna, A., Bai, H., Bai, J., Bai, X., Bai, Y., Bairagi, N., Baksi, S., Balbi, T., Baldari, C. T., Balduini, W., Ballabio, A., Ballester, M., Balazadeh, S., Balzan, R., Bandopadhyay, R., Banerjee, S., Banreti, A., Bao, Y., Baptista, M. S., Baracca, A., Barbati, C., Bargiela, A., Barila, D., Barlow, P. G., Barmada, S. J., Barreiro, E., Barreto, G. E., Bartek, J., Bartel, B., Bartolome, A., Barve, G. R., Basagoudanavar, S. H., Bassham, D. C., Bast, R. C., Basu, A., Batoko, H., Batten, I., Baulieu, E. E., Baumgarner, B. L., Bayry, J., Beale, R., Beau, I., Beaumatin, F., Bechara, L. R. G., Beck, G. R., Beers, M. F., Begun, J., Behrends, C., Behrens, G. M. N., Bei, R., Bejarano, E., Bel, S., Behl, C., Belaid, A., Belgareh-Touze, N., Bellarosa, C., Belleudi, F., Bello Perez, M., Bello-Morales, R., Beltran, J. S. D. O., Beltran, S., Benbrook, D. M., Bendorius, M., Benitez, B. A., Benito-Cuesta, I., Bensalem, J., Berchtold, M. W., Berezowska, S., Bergamaschi, D., Bergami, M., Bergmann, A., Berliocchi, L., Berlioz-Torrent, C., Bernard, A., Berthoux, L., Besirli, C. G., Besteiro, S., Betin, V. M., Beyaert, R., Bezbradica, J. S., Bhaskar, K., Bhatia-Kissova, I., Bhattacharya, R., Bhattacharya, S., Bhattacharyya, S., Bhuiyan, M. S., Bhutia, S. K., Bi, L., Bi, X., Biden, T. J., Bijian, K., Billes, V. A., Binart, N., Bincoletto, C., Birgisdottir, A. B., Bjorkoy, G., Blanco, G., Blas-Garcia, A., Blasiak, J., Blomgran, R., Blomgren, K., Blum, J. S., Boada-Romero, E., Boban, M., Boesze-Battaglia, K., Boeuf, P., Boland, B., Bomont, P., Bonaldo, P., Bonam, S. R., Bonfili, L., Bonifacino, J. S., Boone, B. A., Bootman, M. D., Bordi, M., Borner, C., Bornhauser, B. C., Borthakur, G., Bosch, J., Bose, S., Botana, L. M., Botas, J., Boulanger, C. M., Boulton, M. E., Bourdenx, M., Bourgeois, B., Bourke, N. M., Bousquet, G., Boya, P., Bozhkov, P. V., Bozi, L. H. M., Bozkurt, T. O., Brackney, D. E., Brandts, C. H., Braun, R. J., Braus, G. H., Bravo-Sagua, R., Bravo-San Pedro, J. M., Brest, P., Bringer, M. -A., Briones-Herrera, A., Broaddus, V. 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P., Whitton, J. L., Whitworth, A. J., Wiktorska, K., Wildenberg, M. E., Wileman, T., Wilkinson, S., Willbold, D., Williams, B., Williams, R. S. B., Williams, R. L., Williamson, P. R., Wilson, R. A., Winner, B., Winsor, N. J., Witkin, S. S., Wodrich, H., Woehlbier, U., Wollert, T., Wong, E., Wong, J. H., Wong, R. W., Wong, V. K. W., Wong, W. W. -L., Wu, A. -G., Wu, C., Wu, J., Wu, K. K., Wu, M., Wu, S. -Y., Wu, S., Wu, W. K. K., Wu, X., Wu, Y. -W., Wu, Y., Xavier, R. J., Xia, H., Xia, L., Xia, Z., Xiang, G., Xiang, J., Xiang, M., Xiang, W., Xiao, B., Xiao, G., Xiao, H., Xiao, H. -T., Xiao, J., Xiao, L., Xiao, S., Xiao, Y., Xie, B., Xie, C. -M., Xie, M., Xie, Y., Xie, Z., Xilouri, M., Xu, C., Xu, E., Xu, H., Xu, J., Xu, L., Xu, W. W., Xu, X., Xue, Y., Yakhine-Diop, S. M. S., Yamaguchi, M., Yamaguchi, O., Yamamoto, A., Yamashina, S., Yan, S., Yan, S. -J., Yan, Z., Yanagi, Y., Yang, C., Yang, D. -S., Yang, H., Yang, H. -T., Yang, J. -M., Yang, J., Yang, L., Yang, M., Yang, P. -M., Yang, Q., Yang, S., Yang, S. -F., Yang, W., Yang, W. Y., Yang, X., Yang, Y., Yao, H., Yao, S., Yao, X., Yao, Y. -G., Yao, Y. -M., Yasui, T., Yazdankhah, M., Yen, P. M., Yi, C., Yin, X. -M., Yin, Y., Yin, Z., Ying, M., Ying, Z., Yip, C. K., Yiu, S. P. T., Yoo, Y. H., Yoshida, K., Yoshii, S. R., Yoshimori, T., Yousefi, B., Yu, B., Yu, H., Yu, J., Yu, L., Yu, M. -L., Yu, S. -W., Yu, V. C., Yu, W. H., Yu, Z., Yuan, J., Yuan, L. -Q., Yuan, S., Yuan, S. -S. F., Yuan, Y., Yuan, Z., Yue, J., Yue, Z., Yun, J., Yung, R. L., Zacks, D. N., Zaffagnini, G., Zambelli, V. O., Zanella, I., Zang, Q. S., Zanivan, S., Zappavigna, S., Zaragoza, P., Zarbalis, K. S., Zarebkohan, A., Zarrouk, A., Zeitlin, S. O., Zeng, J., Zeng, J. -D., Zerovnik, E., Zhan, L., Zhang, B., Zhang, D. D., Zhang, H., Zhang, H. -L., Zhang, J., Zhang, J. -P., Zhang, K. Y. B., Zhang, L. W., Zhang, L., Zhang, M., Zhang, P., Zhang, S., Zhang, W., Zhang, X., Zhang, X. -W., Zhang, X. D., Zhang, Y., Zhang, Y. -D., Zhang, Y. -Y., Zhang, Z., Zhao, H., Zhao, L., Zhao, S., Zhao, T., Zhao, X. -F., Zhao, Y., Zheng, G., Zheng, K., Zheng, L., Zheng, S., Zheng, X. -L., Zheng, Y., Zheng, Z. -G., Zhivotovsky, B., Zhong, Q., Zhou, A., Zhou, B., Zhou, C., Zhou, G., Zhou, H., Zhou, J., Zhou, K., Zhou, R., Zhou, X. -J., Zhou, Y., Zhou, Z. -Y., Zhou, Z., Zhu, B., Zhu, C., Zhu, G. -Q., Zhu, H., Zhu, W. -G., Zhu, Y., Zhuang, H., Zhuang, X., Zientara-Rytter, K., Zimmermann, C. M., Ziviani, E., Zoladek, T., Zong, W. -X., Zorov, D. B., Zorzano, A., Zou, W., Zou, Z., Zuryn, S., Zwerschke, W., Brand-Saberi, B., Dong, X. C., Kenchappa, C. S., Lin, Y., Oshima, S., Rong, Y., Sluimer, J. C., Stallings, C. L., Tong, C. -K., and Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Programmed cell death, Settore BIO/06, Autophagosome, Autolysosome, [SDV]Life Sciences [q-bio], lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Autophagy-Related Proteins, Review, Computational biology, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, Settore MED/04, 03 medical and health sciences, stress, Chaperone-mediated autophagy, ddc:570, Autophagy, LC3, Animals, Humans, cancer, Settore BIO/10, flux, lysosome, macroautophagy, neurodegeneration, phagophore, vacuole, Set (psychology), Molecular Biology, 030102 biochemistry & molecular biology, business.industry, Interpretation (philosophy), Autophagosomes, Cell Biology, Multicellular organism, 030104 developmental biology, Knowledge base, Biological Assay, Lysosomes, business, Biomarkers, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: Contains fulltext : 232759.pdf (Publisher’s version ) (Closed access) In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
Published: 2021

22. Research on the influence of the VREs’ penetration on the capacity of transmission lines

Author: Chen Ning, Liang Xu, Caixia Wang, Shi Zhiyong, Yuan Wei, Fan Hao, Qinmiao Li, Qionghui Li, and Xiaoning Ye
Subjects: Wind power, business.industry, Environmental economics, Grid, Investment (macroeconomics), Renewable energy, Environmental sciences, Electric power transmission, Transmission (telecommunications), Energy transformation, Environmental science, Maximum power transfer theorem, GE1-350, business
Abstract: Under the trend of global carbon emission reduction and energy transformation, variable renewable energies (VREs mainly wind power and solar) will develop rapidly. Many countries have put forward ambitious VREs development plans, and a high penetration of VREs will have a significant impact on the grid. This paper focuses on the analysis of the influence of the VREs’ penetration on the capacity of transmission lines, establishes an analysis model based on mixed integer optimization and power transfer distribution factors (FTDFs), and uses this model to carry out a quantitative study on a typical system. Through the analysis of the research results, as the penetration rate of VREs increases, the transmission power of the transmission lines and the investment cost of the transmission lines will increase, and the line utilization rate will decrease. Utility companies should pay attention to the impact of future development of VREs on grid investment costs and recovery.
Published: 2021

23. Energy Storage Application Technology and Operation Model on the Customer Side in China and Abroad

Author: Shi Zhiyong, Ye Xiaoning, Li Qinmiao, Wang Caixia, Chen Ning, and Yuan Wei
Subjects: Rate of return, Environmental sciences, Electric power system, Resource (project management), Tariff, GE1-350, Business, Environmental economics, China, Unitary state, Energy storage, Energy (signal processing)
Abstract: As a superior flexible resource in a new power system with new energy as the main body, customer-side energy storage has great potential for future development. It expounds the application technology and operation model of customer-side energy storage in the United States and Germany, analyzes the operation model of china's customer-side energy storage and calculates internal rates of return of general commercial and industrial customers with a unitary tariff and large industrial customers with two-part tariff, and puts forward suggestions to promote the development of china’s customer-side energy storage.
Published: 2021

24. Design of the Trans-provincial New Energy Spot Transaction Mode and Analysis of the Mode’s Effects on New Energy Consumption

Author: Wang Caixia, Xu Liang, Fan Hao, Li Qionghui, Yuan Wei, and Chen Ning
Subjects: Consumption (economics), Spot contract, Computer science, business.industry, Reliability engineering, Environmental sciences, Production (economics), GE1-350, Electricity, Electric power, business, Database transaction, Energy (signal processing), Communication channel
Abstract: Trans-provincial new energy transactions consist of medium- and long-term transactions and spot transactions. Trans-provincial spot transactions have been mainly executed in areas where there is some difficulty with new energy consumption. To further improve the system’s capability to accommodate new energy, this paper designs a replacement transaction mechanism for electricity delivered through the same channel according to the status quo of trans-provincial new energy spot transactions and the reality of China’s electric power market. A production simulation-based decision analysis model for trans-provincial new energy consumption is established so as to analyse quantitatively the effects of the proposed transaction mode on new energy consumption. A typical case is selected out from a certain province as the delivery end, and the testing results show that the trans-provincial spot transaction mode and the quantitative analysis model proposed in the paper can improve the new energy consumption and utilization rate of the delivery end significantly.
Published: 2021

25. Trend in blood lead levels in Taiwanese adults 2005-2017

Author: Hsiao-Chen Ning, Cheng-Hao Weng, Yu-Shao Chiang, Tzung-Hai Yen, Ching-Wei Hsu, Nai-Chia Fan, Wen-Hung Huang, I-Kuan Wang, Chun-Wei Chuang, Chun-Wan Fang, and Ya-Ching Huang
Subjects: Male, Metallic Lead, Fossil Fuels, Physiology, Biochemistry, Geographical Locations, chemistry.chemical_compound, Chronic Kidney Disease, Medicine and Health Sciences, Medicine, Lead (electronics), Cognitive impairment, Materials, Cognitive Impairment, Multidisciplinary, Cognitive Neurology, Middle Aged, Prognosis, Body Fluids, Leaded petrol, Chemistry, Blood, Neurology, Nephrology, Physical Sciences, Blood Chemistry, Engineering and Technology, Female, Anatomy, Gasoline, Research Article, Chemical Elements, Glomerular Filtration Rate, Adult, Asia, Adolescent, Cognitive Neuroscience, Science, Materials Science, Taiwan, Renal function, Fuels, Young Adult, Environmental health, Renal Diseases, Humans, Lead tests, Retrospective Studies, Renal Physiology, Creatinine, business.industry, Biology and Life Sciences, Environmental Exposure, Lead Poisoning, Energy and Power, Lead, chemistry, Blood chemistry, People and Places, Cognitive Science, Geometric mean, business, Follow-Up Studies, Neuroscience
Abstract: This study examined the trend of blood lead levels (BLLs) in Taiwanese adults and analyzed the variations in the BLL between Linkou (northern) and Kaohsiung (southern) hospital branches. Between 2005 and 2017, 3,804 adult participants received blood lead tests at the Linkou (n = 2,674) and Kaohsiung (n = 1,130) branches of Chang Gung Memorial Hospital. The geometric mean of BLL was 2.77 μg/dL. The adult participants from the Kaohsiung branch were not only age older (49.8±14.1 versus 39.4±14.2 years; P 5 μg/dL. Therefore, this study confirmed a continuous decreasing trend in the BLL in Taiwan after banning leaded petrol in 2000.
Published: 2021

26. Review and Outlook of the Space Operation and Control Project Development and Technology

Author: Yasheng Zhang, Chen Ning, and Wenhua Cheng
Subjects: Engineering, Development (topology), Aerospace electronics, business.industry, Control system, Control (management), Systems engineering, Key (cryptography), Space (commercial competition), Project management, business, Space debris
Abstract: Space operation and control technology has become an important indicator of a country's space force. First, this article summarizes and reviews the development of space operation and control projects of various countries in the world. Then, the composition of space operation and control system and the relevant key technologies are sorted out. Finally, the outlook of space operation and control is prospected.
Published: 2020

27. Memory efficient decoder architectures for quasi-cyclic LDPC codes

Author: Dai, Yongmei, Chen, Ning, and Yan, Zhiyuan
Subjects: Algorithms -- Usage, Error-correcting codes -- Design and construction, Coding theory -- Research, Circuit design -- Evaluation, Algorithm, Circuit designer, Integrated circuit design, Business, Computers and office automation industries, Electronics, Electronics and electrical industries
Abstract: In this paper, we first propose parallel turbo-sum-product (PTSP) and turbo-shuffled-sum-product (TSSP) decoding algorithms for partly parallel decoder architectures of quasi-cyclic (QC) low-density parity-check (LDPC) codes. Our proposed algorithms not only achieve faster convergence and better error performance than the sum-product (SP) decoding algorithm, but also need less memory in implementation. Then we propose a partly parallel decoder architecture based on our PTSP algorithm and implement it using FPGA. Our PTSP decoder architecture achieves significantly higher throughput and requires less memory than previously proposed decoder architectures with the same FPGA and LDPC code. Finally, to further reduce the memory requirement, we also propose a partly parallel decoder architecture based on our TSSP algorithm. Index Terms--Low-density parity-check (LDPC) codes, quasi-cyclic (QC) codes, shuffled decoding, sum-product (SP) decoding, turbo decoding.
Published: 2008

28. Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

Author: Chen Ning, Xin Zhang, and Xin Wang
Subjects: Multi kernel, Computer Networks and Communications, Infrared, Computer science, business.industry, Pattern recognition, Artificial intelligence, Transfer of learning, business, Information Systems
Published: 2020

29. Evaluation the diagnostic accuracy of albuminuria detection in semi-quantitative urinalysis

Author: Nan-Chang Lai, Chia-Ju Yang, Ding-Ping Chen, Ying-Hao Wen, and Hsiao-Chen Ning
Subjects: 0301 basic medicine, medicine.medical_specialty, Urinalysis, Urinary system, Clinical Biochemistry, Siemens, Urology, Taiwan, Urine, urologic and male genital diseases, Biochemistry, Sensitivity and Specificity, 03 medical and health sciences, 0302 clinical medicine, Renal Dialysis, parasitic diseases, medicine, Albuminuria, Humans, medicine.diagnostic_test, business.industry, Biochemistry (medical), General Medicine, Dipstick, medicine.disease, 030104 developmental biology, 030220 oncology & carcinogenesis, Creatinine, Microalbuminuria, medicine.symptom, business, Semi quantitative
Abstract: Background Taiwan has the highest end-stage renal disease prevalence in the world, and the costs on the maintenance of dialysis imposes a great financial burden on National Health Insurance. Routine urinalysis provides an opportunity for the early detection of microalbuminuria. We evaluated the accuracy of semi-quantitative chemical methods from Siemens Novus Pro12 dipstick for albumin-creatinine ratio (ACR). Methods We collected 1029 random urine samples and performed urinary analytic tests by Siemens Novus with Pro12 dipsticks and also calculated the urinary ACR. The reference method was performed by Hitachi LST008, a quantitative assay. The percentage of exact agreement in ACR was 81.9% between Siemens Novus and Hitachi LST008. The percentage of agreement within 1 level between the 2 methods was 98.5%. When ACR > 30 mg/g was defined as the threshold for positive results, the sensitivity, specificity, positive, and negative predictive values for microalbuminuria were 87.2%, 91.6%, 91.5%, and 87.3%, respectively. There were 778 cases with negative results of urinary protein, analyzed by conventional dipsticks. 149 of 778 (19.2%) cases were positive, measured by Pro12 dipsticks, and 111 of 149 (74.5%) cases were confirmed positive ACR by Hitachi LST008. Conclusions Urinary ACR measured by Siemens Novus with Pro12 dipsticks was shown to be a reliable test for detection of microalbuminuria.
Published: 2020

30. SAR Target Recognition via Enhanced Kernel Sparse Representation of Monogenic Signal

Author: Wenbo Liu, Chen Ning, Gong Zhang, and Xin Wang
Subjects: Synthetic aperture radar, Computer science, business.industry, Feature vector, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Sparse approximation, Target acquisition, Kernel (image processing), Discriminative model, Artificial intelligence, Kernel Fisher discriminant analysis, business
Abstract: A novel synthetic aperture radar (SAR) target recognition algorithm based upon the enhanced kernel sparse representation of monogenic signal is presented in this work. It contains two parts. First, to capture spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the KPCA as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimension of the kernel feature space can be effectively reduced. At last, the l1-norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached by the rule of minimizing total reconstruction error. Experiments on the public moving and stationary target acquisition and recognition dataset demonstrate that the proposed method achieves high recognition accuracy for SAR automatic object recognition.
Published: 2019

31. Image Dehazing Using Degradation Model and Group-Based Sparse Representation

Author: Xin Zhang, Qiong Wang, Hangcheng Zhu, Chen Ning, and Xin Wang
Subjects: Group based, Computer science, business.industry, Visibility (geometry), Quantitative Evaluations, Pattern recognition, Artificial intelligence, Sparse approximation, business, Image restoration, Light scattering, Degradation (telecommunications), Image (mathematics)
Abstract: Handling images captured in hazy weather conditions is a big challenge, for they usually have poor visibility because of the light scattering as well as absorption effects. This paper addresses an integration of a degradation model constructor and a group-based sparse representation (GSR) strategy for single image dehazing. The degradation model is constructed based on a classical physical model, i.e., dichromatic atmospheric scattering model. Then, the new degradation model is integrated into the group-based sparse representation framework. Finally, the single image dehazing problem is regarded as an image restoration problem, which can be well optimized by GSR. The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations, and results indicate considerable improvement over existing algorithms.
Published: 2019

32. Characteristics of the genotype and phenotype in Chinese primary hyperoxaluria type 1 populations

Author: Chen Ning, Fang-Zhou Zhao, Wenying Wang, Lei Tang, Jun Li, and Chun-Ming Li
Subjects: Nephrology, Male, medicine.medical_specialty, Poor prognosis, China, Adolescent, Genotype, Urology, Urinary system, Mutant, 030232 urology & nephrology, Gastroenterology, Primary hyperoxaluria, 03 medical and health sciences, 0302 clinical medicine, Genotype-phenotype distinction, Asian People, Urolithiasis, Internal medicine, Medicine, Humans, Age of Onset, Child, Transaminases, Retrospective Studies, Calcium Oxalate, business.industry, Infant, Retrospective cohort study, medicine.disease, Prognosis, Phenotype, Child, Preschool, Hyperoxaluria, Primary, Mutation, Disease Progression, Kidney Failure, Chronic, Female, business, Follow-Up Studies
Abstract: The aim of our study is to explore the relationship between genotype and phenotype in Chinese PH1 patients and determine the putative mutation hotspot regions. This was a retrospective study regarding 13 Chinese PH1 patients. And all sporadic published researches of Chinese PH1 populations were searched and enrolled based on the inclusive standard. All patients presented with multiple urolithiasis or nephrolithiasis. Urinary oxalate values demonstrated an obvious and extensive variability, ranging from 1.01 to 3.85 mmol/1.73 m2. Molecular diagnosis showed that 13 mutant types were detected. Infantile form patient (pt.) 10 and five patients (pts. 5, 7, 8, 9, 12) carrying c.815_816insGA or c.33_34insC demonstrated a worse prognosis, of whom pt. 5 progressed into ESRD 4 years later and died of chronic kidney failure. Based on the integrated Chinese mutation data, two variants (c.815_816insGA and c.33_34insC) were determined as the most common mutations. Besides, c.1049G>A was initially identified in a Chinese patient. Conclusions: heterogeneity between genotype and phenotype was observed and described in Chinese PH1 patients. c.815_816insGA and c.33_34insC which were recognized as AGXT mutation hotspot regions in China implied a poor prognosis. And c.1049G>A was not determined as the race-specific mutation of Pakistani.
Published: 2019

33. Multicomponent distributed acoustic sensing: Concept and theory

Author: Ivan Lim Chen Ning and Paul Sava
Subjects: 010504 meteorology & atmospheric sciences, business.industry, Acoustics, Data reconstruction, Physics::Optics, Distributed acoustic sensing, 010502 geochemistry & geophysics, 01 natural sciences, Geophysics, Optics, Geochemistry and Petrology, Fiber optic sensor, Tensor, business, Image resolution, 0105 earth and related environmental sciences, Mathematics
Abstract: Distributed acoustic sensing (DAS) data are increasingly used in geophysics. Lower in cost and higher in spatial resolution, DAS data are appealing, especially in boreholes in which optical fibers are readily available. DAS has the potential to become a permanent reservoir monitoring tool with a reduced sensing time interval. To accomplish this goal, it is critical that DAS can record all wave modes to fully characterize reservoir properties. This goal can be achieved by recording the complete strain tensor consisting of 6C. Conventional DAS provides projections of these components along the optical fiber by observing deformation along the fiber. To obtain the entire 6C strain tensor, we have developed an approach using multiple strain projections measured along optical fibers with judiciously chosen geometry specifically. We evaluate designs combining multiple helical configurations or a single helical configuration together with a straight optical fiber that allow access to multiple strain projections. We group multiple strain projections in a given spatial window to perform reconstruction of the entire strain tensor in a least-squares sense under the assumption that the seismic wavelength is larger than the analysis window size. We determine how optimal optical fiber parameters can be selected using a scan of the entire configuration space and analyzing the condition number associated with the geometry of the optical fibers. We develop our method through synthetic experiments using realistic fiber geometry and wavefields of arbitrary complexity.
Published: 2018

34. A novel visual saliency detection method for infrared video sequences

Author: Zhang Yuzhen, Xin Wang, and Chen Ning
Subjects: business.industry, Computer science, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Video sequence, 02 engineering and technology, Condensed Matter Physics, 01 natural sciences, Luminance, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, 010309 optics, Kadir–Brady saliency detector, Salient, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Gestalt psychology, Spatial cues, Contrast (vision), 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Visual saliency, media_common
Abstract: Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the “important” content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.
Published: 2017

35. Modeling the effects of urbanization on grain production and consumption in China

Author: Lu Wencong, Qian Wen-xin, and Chen Ning-lu
Subjects: Agriculture (General), urbanization, Plant Science, Biochemistry, Agricultural economics, grain self-sufficiency, S1-972, Globalization, Food Animals, Urbanization, 0502 economics and business, Economics, China, Consumption (economics), Food security, Ecology, business.industry, Partial equilibrium, 05 social sciences, 04 agricultural and veterinary sciences, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Position (finance), Animal Science and Zoology, 050202 agricultural economics & policy, business, Agronomy and Crop Science, grain security, Food Science
Abstract: The impact of rapid urbanization on food security of China has received considerable attention. It is not clear whether China can strike a balance between urbanization and food security, especially grain security. There have been numerous studies examining the effects of urbanization on grain production or consumption, but few studies have yet analyzed grain balances. Based on the Chinese World Agricultural Regional Market Equilibrium Model (CWARMEM), this paper explores the impacts of urbanization on national and regional grain balances through different scenarios. The CWARMEM is a global partial equilibrium multimarket model which captures the differences between urban and rural residents as well as the effects of globalization. The results show that urbanization has a small negative effect on maintaining grain self-sufficiency. Despite of that, China is able to achieve the objective of grain security set by its policymakers. Moreover, urbanization changes regional grain balances across China: The position of Northeast China and North China, as two dominant grain suppliers of China, will be weaken; Central China and East China will increase dependence on other grain suppliers; the grain surplus of Northwest China will increase slightly. Besides, in terms of grain category, urbanization helps China achieve self-sufficiency in food grain (rice and wheat), while expands deficit of feed grain (maize).
Published: 2017

36. Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Author: Chunyan Zhang, Chen Ning, Yuzhen Zhang, Xin Wang, and Guofang Lv
Subjects: Article Subject, General Mathematics, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 01 natural sciences, Measure (mathematics), Image (mathematics), 010309 optics, Background noise, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Contrast (vision), Computer vision, media_common, Mathematics, business.industry, lcsh:Mathematics, General Engineering, Process (computing), Pattern recognition, Sparse approximation, lcsh:QA1-939, Kadir–Brady saliency detector, lcsh:TA1-2040, Salient, Computer Science::Computer Vision and Pattern Recognition, 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Engineering (General). Civil engineering (General), business
Abstract: For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.
Published: 2017

37. Mutational analysis of AGXT gene in Chinese patients with primary hyperoxaluria type 1

Author: Jun Li, Wenying Wang, Ye Tian, and Chen Ning
Subjects: Mutational analysis, Genetics, Primary hyperoxaluria, business.industry, Urology, Pediatrics, Perinatology and Child Health, Medicine, business, medicine.disease, Gene
Published: 2020

38. Researches on Modeling and Experiment of Li-ion Battery PTC Self-heating in Electric Vehicles

Author: Pu-en Wu, Chen-ning Zhang, Junqiu Li, and Xin Jin
Subjects: Battery (electricity), business.industry, Chemistry, 020209 energy, Nuclear engineering, Electrical engineering, 02 engineering and technology, 021001 nanoscience & nanotechnology, Ion, Power (physics), Energy(all), 0202 electrical engineering, electronic engineering, information engineering, 0210 nano-technology, business, Self heating, Internal heating, Temperature coefficient, Charge and discharge
Abstract: Positive Temperature Coefficient(PTC) self-heating method of battery and experiment researches are conducted in this paper; the model of discharge internal heating and the model of PTC heating for battery are established; analyses of the internal and external heat characteristics of the battery and self-heating temperature field distribution of the battery are carried out. The accuracy of those models are verified by implementing the self-heating experiments. The discharge tests are carried out under the extreme low temperature and the results show that charge and discharge rate, capacity recovery are much better than that of external-supplied power heating.
Published: 2016

39. Strength of internally ring-stiffened tubular DT-joints subjected to brace axial loading

Author: Chen Ning, Pan Xiaorong, Xiaoyi Lan, Luo Zhifeng, Fan Wang, and Xiaofeng Xu
Subjects: Engineering, Chord (geometry), business.industry, Tension (physics), Numerical analysis, Metals and Alloys, Hinge, 020101 civil engineering, 02 engineering and technology, Building and Construction, Structural engineering, Brace, 0201 civil engineering, Stress (mechanics), 020303 mechanical engineering & transports, 0203 mechanical engineering, Mechanics of Materials, Plastic hinge, Cylinder stress, Composite material, business, Civil and Structural Engineering
Abstract: This paper presents the results of numerical and theoretical studies to obtain static strength equations for internally ring-stiffened circular hollow section (CHS) tubular DT-joints. An extensive study of 1264 unstiffened and ring-stiffened DT-joints subjected to brace axial compression or tension was conducted. The numerical analysis shows that failure mechanism of crown- and saddle-stiffened DT-joints under brace axial loading is formation of plastic hinges in the stiffener and chord wall yielding near the brace-chord intersection. Based on the identified failure mechanism, theoretical models and corresponding equations for predicting the stiffener strength in crown- and saddle-stiffened DT-joints subjected to brace axial compression or tension were proposed. The accuracy of the proposed stiffener strength equations was evaluated by an error analysis. A chord stress function was proposed to consider chord axial stress effect on the stiffened DT-joint strength. In conjunction with existing unstiffened DT-joint strength formulae and considering chord axial stress effect, a strength equation for crown- and saddle-stiffened DT-joints subjected to brace axial loading was proposed.
Published: 2016

40. CFD Study on the Flow Field and Power Characteristics in a Rushton Turbine Stirred Tank in Laminar Regime

Author: Chen Ning, Xiang Kefeng, Li Liangchao, and Xiang Beiping
Subjects: Computer simulation, business.industry, General Chemical Engineering, Industrial chemistry, Mechanical engineering, Laminar flow, 02 engineering and technology, Power number, Computational fluid dynamics, 021001 nanoscience & nanotechnology, Flow field, Power (physics), Rushton turbine, Physics::Fluid Dynamics, 020401 chemical engineering, Environmental science, 0204 chemical engineering, 0210 nano-technology, business
Abstract: A computational fluid dynamics (CFD) simulation was performed to study the hydrodynamics characteristics in a Rushton turbine stirred tank in laminar regime. The effects of operating condition, working medium and geometrical parameter on the flow field and power number characteristics were investigated. It is found that the two-loop flow pattern is formed in laminar regime when the impeller is not very close to tank bottom, while its shape and size vary with Reynolds number and impeller diameter. For a given geometrical configuration, the flow pattern, power number and dimensionless velocity profile are mainly depended on Reynolds number, and do not change with working medium and scale-up for a constant Reynolds number. When impeller off-bottom clearance is too low and Reynolds number is relatively high, the fluid flow would transit from two-loop flow pattern to sing-loop flow pattern as that occurs in turbulent regime. Power number falls for larger impeller in laminar regime. Surprisingly, in laminar regime, power number in the baffled tank with small impeller is almost identical to that in the unbaffled tank.
Published: 2019

41. Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

Author: Chun-Hsien Chen, Yi Ju Tseng, Li Chuan Lu, Nan Chang Lai, Kai-Yao Huang, Ming Hsiu Tsai, Jang Jih Lu, Hsin-Yao Wang, Tzong-Yi Lee, Hsiao Chen Ning, and Chung Chih Hung
Subjects: Adult, Male, 0301 basic medicine, Parasitic infection, Urinalysis, Cost-Benefit Analysis, Science, Trichomonas Infections, Trichomonas vaginalis detection, Urine, medicine.disease_cause, Logistic regression, Machine learning, computer.software_genre, Article, Pattern Recognition, Automated, Machine Learning, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Trichomonas vaginalis, Humans, Medicine, Diagnosis, Computer-Assisted, Routine analysis, Retrospective Studies, Multidisciplinary, Receiver operating characteristic, medicine.diagnostic_test, business.industry, Diagnostic markers, Middle Aged, Models, Theoretical, Translational research, 030104 developmental biology, ROC Curve, Risk factors, Area Under Curve, Female, Artificial intelligence, Detection rate, business, computer, 030217 neurology & neurosurgery
Abstract: Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.
Published: 2019

42. Research of High-precision Virtual Instrument Test Platform for Linear Displacement Sensor

Author: Li Dong, Huang Lei, and Chen Ning
Subjects: 0303 health sciences, 030306 microbiology, business.industry, Computer science, Interface (computing), Process (computing), Modular design, Displacement (vector), Metrology, 03 medical and health sciences, 0302 clinical medicine, Software, Component (UML), Calibration, 030212 general & internal medicine, business, Simulation
Abstract: High-precision virtual instrument acquisition platform of linear displacement sensor is an instrument that has standard interface. It helps to insert different displacement sensor into the platform. It is framed into high precision, integrated, modular and testing various types of displacement sensor system. The platform utilizes the virtual instrument technology which is assembled by high speed computer, data collection system and software. The platform can provide standard interface for different types of linear displacement sensor, identify the sensor type, collect data and process data automatically. Therefore, it becomes a part of displacement sensor calibration system. Intelligent acquisition platform of linear displacement sensor is a part of displacement measuring system, but it is also a part of displacement sensor calibration system. So, the influence of platform accuracy must be ignored. From the theoretical analysis and experimental verification, we can see that the uncertainty component of platform is 0.033%, the uncertainty is less than 1/3 of secondary instrument.
Published: 2019

43. Kernel Joint Sparse Representation Based SAR Automatic Target Recognition

Author: Gong Zhang, Wenbo Liu, Chen Ning, and Xin Wang
Subjects: Synthetic aperture radar, business.industry, Computer science, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Sparse approximation, Kernel principal component analysis, ComputingMethodologies_PATTERNRECOGNITION, Automatic target recognition, Kernel (image processing), 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, Kernel Fisher discriminant analysis, business, 021101 geological & geomatics engineering, Reproducing kernel Hilbert space
Abstract: Synthetic aperture radar (SAR) automatic target recognition (ATR) is an important task in computer vision. This paper proposes a novel SAR ATR method based on kernel joint sparse representation. First, a monogenic feature extraction method is adopted to capture multiscale spatial and spectral properties of targets. Second, a kernel joint sparse representation classifier (KJSRC) is designed. In this KJSRC, to make the data linearly separable and more discriminative, we integrate the kernel principal component analysis and kernel Fisher discriminant analysis to produce an augmented pseudo-transformation matrix, which maps the features from the input space to a reproducing kernel Hilbert space. Third, different from single task learning, our method builds different linear regression models for each kernel-mapped monogenic feature. Through optimizing l 1 /l q -norm regularization problem, the representation coefficients across multitask representation models are estimated. Finally, the query label is estimated according to total reconstruction error minimization rule from all tasks. Experimental results show that our method achieves high recognition accuracy for SAR ATR.
Published: 2019

44. Effective Heterogeneous Image Matching by Multiple Cues Integration

Author: Huaqiong Zhai, Ma Zhenli, Chen Ning, and Xin Wang
Subjects: Consensus algorithm, Matching (statistics), Image matching, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Pattern recognition, 02 engineering and technology, Ir image, k-nearest neighbors algorithm, Image (mathematics), Feature (computer vision), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Point (geometry), Artificial intelligence, business, 021101 geological & geomatics engineering
Abstract: This paper proposes an effective heterogeneous image matching algorithm by multiple cues integration. Given two kinds of heterogeneous images, i.e., infrared (IR) and visible images, we calculate the negative image of the IR image at first. Then, to enhance the matching performance, both the IR image and its negative image will be matched with the visible image respectively. Subsequently, for each image in the image groups, multiple cues, called DOG-SIFT, H-SC, and LPQ are proposed to extract and describe the feature points. Based on these points, the nearest neighbor ratio method is used for initial matching, and then the random sample consensus algorithm is adopted to eliminate the false matching point pairs. At last, by fusing the matching results of different image groups, we can get the final result. Experimental results show that the proposed method can achieve good performance for heterogeneous image matching.
Published: 2019

45. A Multi-sensor Image Matching Method Based on KAZE-HOG Features

Author: Lin Duan, Yue Fan, Xin Wang, and Chen Ning
Subjects: Matching (statistics), Computer science, business.industry, 020208 electrical & electronic engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Sample (graphics), k-nearest neighbors algorithm, Robustness (computer science), Feature (computer vision), Computer Science::Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Bilateral filter, Noise (video), Artificial intelligence, business, Rotation (mathematics)
Abstract: This paper presents a multi-sensor image matching algorithm based on KAZE-HOG features. First, the multi- sensor images to be matched are preprocessed by bilateral filtering so as to remove noise. Then, a novel feature descriptor, namely KAZE-HOG is designed to extract as well as describe the feature points. Third, a nearest neighbor algorithm is used to match the obtained feature points of the images. Finally, a M-estimator sample consensus (MSAC) algorithm is utilized to remove the mismatches, so that the precise matching results can be gotten. Experimental results show that the proposed method has strong robustness, and it has good capability to resist scale and rotation transformations. As a result, it can achieve precise matching results for multi-sensor images.
Published: 2019

46. Pulmonary delivery alters the disposition of raloxifene in rats

Author: Hui Cai, Xijing Chen, Han Xing, Yang Lu, Ying Kong, Ning Li, Dexuan Kong, Chen Ning, Di Zhao, and Chang Ren
Subjects: Selective Estrogen Receptor Modulators, Pharmaceutical Science, Biological Availability, Absorption (skin), Pharmacology, 030226 pharmacology & pharmacy, Excretion, Rats, Sprague-Dawley, 03 medical and health sciences, 0302 clinical medicine, Drug Delivery Systems, Pharmacokinetics, medicine, Distribution (pharmacology), Animals, Raloxifene, Tissue Distribution, Chronic toxicity, business.industry, Bioavailability, Rats, Selective estrogen receptor modulator, 030220 oncology & carcinogenesis, Raloxifene Hydrochloride, Female, business, medicine.drug, Half-Life
Abstract: Objective Pulmonary delivery is an effective way to improve the bioavailability of drugs with extensive metabolism. This research was designed to study the different pharmacokinetic behaviours of small molecule drug after pulmonary delivery and intragastric (i.g) administration. Methods Raloxifene, a selective estrogen receptor modulator with low oral bioavailability (~2%), was chosen as the model drug. Studies were conducted systematically in rats, including plasma pharmacokinetics, excretion, tissue distribution and metabolism. Key findings Results showed that raloxifene solution dosed by intratracheal (i.t) administration exhibited relatively quick plasma elimination (t1/2 = 1.78 ± 0.14 h) and undetected absorption process, which was similar with intravenous injection. Compared with i.g administration, the bioavailability increased by 58 times, but the major route of excretion remained faecal excretion. Drug concentration on the bone and the target efficiency were improved by 49.6 times and five times, respectively. Benefited from quick elimination in the lung, chronic toxicity might be ignored. Conclusions Pulmonary administration improved the bioavailability of raloxifene and further increased the distribution on the target organ (bone), with no obvious impact on its excretory pattern.
Published: 2019

47. Repeated Intravenous Dose Toxicity of Di-Isononyl Phthalate in Male Sprague-Dawley Rats

Author: Jiang Tao, Xue Yanping, and Chen Ning
Subjects: Intravenous dose, chemistry.chemical_compound, chemistry, business.industry, Toxicity, Phthalate, Sprague dawley rats, Medicine, Pharmacology, business
Published: 2019

48. Rigid-flexible coupled dynamics analysis of 3-revolute-prismatic-spherical parallel robot based on multi-software platform

Author: Haitao Luo, Chen Ning, Jiao Lichuang, Wu Tingke, and Jia Fu
Subjects: 0209 industrial biotechnology, business.industry, Computer science, Mechanical Engineering, lcsh:Mechanical engineering and machinery, Dynamics (mechanics), Parallel manipulator, Control engineering, 02 engineering and technology, Kinematics, Co-simulation, Revolute joint, 020303 mechanical engineering & transports, 020901 industrial engineering & automation, Software, 0203 mechanical engineering, Robot, lcsh:TJ1-1570, business, Computer technology
Abstract: Kinematics and dynamics are the most important and basic tool for robot research. With the help of computer technology and the respective advantages of three kinds of software, a new method of co-simulation of parallel robot based on multi-platform is proposed, and the mechanical model of multi-body system of 3-revolute-prismatic-spherical parallel robot is established. According to the mechanical analysis of the parallel robot, the rigid-flexible coupling analysis method is adopted. The displacement error shows a periodic change with a period of 4.2 s and the maximum error is [Formula: see text]. The dangerous part of the structure is the root of the lower link, and its maximum stress is 202.64 MPa less than the yield strength of the material. The multi-software platform co-simulation improves the accuracy of the dynamic response analysis of the part under dynamic load, and provides an important theoretical basis for the design and optimization of the parallel robot.
Published: 2019

49. Transient Modeling and Verification of Current Source Photovoltaic Virtual Synchronous Generator

Author: LI Xutao, Chen Ning, Qu Linan, Jiao Long, and LI Lingling
Subjects: Frequency response, Virtual synchronous generator, Computer science, business.industry, Photovoltaic system, Electrical engineering, Transient (oscillation), Voltage regulation, Current source, Grid, business, Field (computer science)
Abstract: With the promotion of virtual synchronization technology, photovoltaic (PV) virtual synchronous generator (VSG) gradually develops from micro-grid field to bulk power grid. Active control of frequency response and voltage regulation of VSG can support the grid operation friendly. Based on control principle of current source PV VSG, the PV VSG transient model was established in this paper, which was validated to be accurate based on multi-test condition data, and it is provided basic support for stability analysis of VSG connected with bulk power grid.
Published: 2018

50. An old risk in the new era: SQL injection in cloud environment

Author: Wang Zhi-jian, Wang Pei, Cao Xiaoning, Zhu Yue, Zhang Lei, Chen Ning, Wang Meiling, and Fu Xiao
Subjects: business.industry, Computer science, Software as a service, Applied Mathematics, Data_MISCELLANEOUS, Cloud computing, Information security, Virtualization, computer.software_genre, Computer security, Computer Science Applications, Management Information Systems, SQL injection, Web application, business, computer
Abstract: After haunting all the software engineers for more than 26 years since it was discovered and classified in 2002, SQL injection still poses a most serious threat to developers, maintainers and users of web applications even into the brand new cloud era. SaaS, PaaS and IaaS virtualisation technologies which are widely used by cloud computing seemed to fail the enhancement of security against such an attack. We strive to study the mechanism and principles of SQL injection attack in order to help the information security personnel to understand and manage such risks.
Published: 2021

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