Institution
University of Crete
Education•Rethymno, Greece•
About: University of Crete is a education organization based out in Rethymno, Greece. It is known for research contribution in the topics: Population & Galaxy. The organization has 8681 authors who have published 21684 publications receiving 709078 citations. The organization is also known as: Panepistimio Kritis.
Topics: Population, Galaxy, Cancer, Context (language use), Laser
Papers published on a yearly basis
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Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors 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.
Abstract: 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.
1,129 citations
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Paris Descartes University1, Cornell University2, University of Massachusetts Medical School3, Spanish National Research Council4, University of Rome Tor Vergata5, Boston Children's Hospital6, University of Pittsburgh7, National University of Cuyo8, National Scientific and Technical Research Council9, Albert Einstein College of Medicine10, University of California, San Francisco11, University of New Mexico12, University of Split13, Goethe University Frankfurt14, University of Helsinki15, University of Salento16, German Cancer Research Center17, Virginia Commonwealth University18, St. Jude Children's Research Hospital19, Discovery Institute20, Harvard University21, University of Tromsø22, Eötvös Loránd University23, Hungarian Academy of Sciences24, New York University25, University of Vienna26, Babraham Institute27, University of South Australia28, University of Texas Southwestern Medical Center29, Howard Hughes Medical Institute30, University of Oviedo31, University of Graz32, National Institutes of Health33, Queens College34, City University of New York35, University of Tokyo36, University of Zurich37, Novartis38, Austrian Academy of Sciences39, University of Groningen40, University of Cambridge41, University of Padua42, University of Oxford43, University of Bern44, University of Oslo45, University of Crete46, Foundation for Research & Technology – Hellas47, Francis Crick Institute48, Osaka University49, Icahn School of Medicine at Mount Sinai50
TL;DR: A panel of leading experts in the field attempts here to define several autophagy‐related terms based on specific biochemical features to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagic research.
Abstract: Over the past two decades, the molecular machinery that underlies autophagic responses has been characterized with ever increasing precision in multiple model organisms. Moreover, it has become clear that autophagy and autophagy-related processes have profound implications for human pathophysiology. However, considerable confusion persists about the use of appropriate terms to indicate specific types of autophagy and some components of the autophagy machinery, which may have detrimental effects on the expansion of the field. Driven by the overt recognition of such a potential obstacle, a panel of leading experts in the field attempts here to define several autophagy-related terms based on specific biochemical features. The ultimate objective of this collaborative exchange is to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagy research.
1,095 citations
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University of Lyon1, University of Burgundy2, Université de Sherbrooke3, The Chinese University of Hong Kong4, Pompeu Fabra University5, Stanford University6, Queen Mary University of London7, University of Crete8, Indian Institute of Technology Madras9, French Institute for Research in Computer Science and Automation10, German Cancer Research Center11, Mannheim University of Applied Sciences12, ETH Zurich13, Utrecht University14, Yonsei University15, University of Nice Sophia Antipolis16
TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
Abstract: Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
1,056 citations
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Dresden University of Technology1, Brigham and Women's Hospital2, University of California, San Francisco3, University of Düsseldorf4, University of Pisa5, Northwestern University6, Medical University of Vienna7, National and Kapodistrian University of Athens8, Medical University of South Carolina9, University of Cambridge10, University of Barcelona11, The Feinstein Institute for Medical Research12, Toronto Western Hospital13, University of California, Los Angeles14, Humboldt University of Berlin15, Copenhagen University Hospital16, University of Michigan17, University of the Basque Country18, University Health Network19, University of Crete20, University of Zagreb21, University of Paris-Sud22, University of Hong Kong23, University of Calgary24, Hospital for Special Surgery25, University of Pécs26, University of Padua27, Medical University of Graz28, National Institutes of Health29, New York University30, Université Paris-Saclay31, University Hospital Complex Of Vigo32, University of Occupational and Environmental Health Japan33, University of Porto34, Leeds Teaching Hospitals NHS Trust35, Cedars-Sinai Medical Center36, Istanbul Bilim University37, McMaster University38
TL;DR: To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism and the American College of Rheumatology (ACR).
Abstract: Objective To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). Methods This international initiative had four phases. 1) Evaluation of antinuclear antibody (ANA) as an entry criterion through systematic review and meta-regression of the literature and criteria generation through an international Delphi exercise, an early patient cohort, and a patient survey. 2) Criteria reduction by Delphi and nominal group technique exercises. 3) Criteria definition and weighting based on criterion performance and on results of a multi-criteria decision analysis. 4) Refinement of weights and threshold scores in a new derivation cohort of 1,001 subjects and validation compared with previous criteria in a new validation cohort of 1,270 subjects. Results The 2019 EULAR/ACR classification criteria for SLE include positive ANA at least once as obligatory entry criterion; followed by additive weighted criteria grouped in 7 clinical (constitutional, hematologic, neuropsychiatric, mucocutaneous, serosal, musculoskeletal, renal) and 3 immunologic (antiphospholipid antibodies, complement proteins, SLE-specific antibodies) domains, and weighted from 2 to 10. Patients accumulating ≥10 points are classified. In the validation cohort, the new criteria had a sensitivity of 96.1% and specificity of 93.4%, compared with 82.8% sensitivity and 93.4% specificity of the ACR 1997 and 96.7% sensitivity and 83.7% specificity of the Systemic Lupus International Collaborating Clinics 2012 criteria. Conclusion These new classification criteria were developed using rigorous methodology with multidisciplinary and international input, and have excellent sensitivity and specificity. Use of ANA entry criterion, hierarchically clustered, and weighted criteria reflects current thinking about SLE and provides an improved foundation for SLE research.
1,018 citations
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01 Jan 2011TL;DR: A novel solution to the problem of recovering and tracking the 3D position, orientation and full articulation of a human hand from markerless visual observations obtained by a Kinect sensor is presented.
Abstract: We present a novel solution to the problem of recovering and tracking the 3D position, orientation and full articulation of a human hand from markerless visual observations obtained by a Kinect sensor. We treat this as an optimization problem, seeking for the hand model parameters that minimize the discrepancy between the appearance and 3D structure of hypothesized instances of a hand model and actual hand observations. This optimization problem is effectively solved using a variant of Particle Swarm Optimization (PSO). The proposed method does not require special markers and/or a complex image acquisition setup. Being model based, it provides continuous solutions to the problem of tracking hand articulations. Extensive experiments with a prototype GPU-based implementation of the proposed method demonstrate that accurate and robust 3D tracking of hand articulations can be achieved in near real-time (15Hz).
1,009 citations
Authors
Showing all 8725 results
Name | H-index | Papers | Citations |
---|---|---|---|
Mercouri G. Kanatzidis | 152 | 1854 | 113022 |
T. J. Pearson | 150 | 895 | 126533 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |
William Wijns | 127 | 752 | 95517 |
Andrea Comastri | 111 | 706 | 49119 |
Costas M. Soukoulis | 108 | 644 | 50208 |
Elias Anaissie | 107 | 372 | 42808 |
Jian Zhang | 107 | 3064 | 69715 |
Emmanouil T. Dermitzakis | 101 | 294 | 82496 |
Andreas Engel | 99 | 448 | 33494 |
Nikos C. Kyrpides | 96 | 711 | 62360 |
David J. Kerr | 95 | 544 | 39408 |
Manolis Kogevinas | 95 | 623 | 28521 |
Thomas Walz | 92 | 255 | 29981 |
Jean-Paul Latgé | 91 | 343 | 29152 |