Pierre-and-Marie-Curie University

About: Pierre-and-Marie-Curie University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Raman spectroscopy. The organization has 34448 authors who have published 56139 publications receiving 2392398 citations.

Diagnosis of Arrhythmogenic Right Ventricular Cardiomyopathy/Dysplasia Proposed Modification of the Task Force Criteria

Abstract: Background— In 1994, an International Task Force proposed criteria for the clinical diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) that facilitated recognition and ...

Nonlinear scalar field equations, I existence of a ground state

Abstract: 1. The Main Result; Examples . . . . . . . . . . . . . . . . . . . . . . . 316 2. Necessary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 319 3. The Constrained Minimization Method . . . . . . . . . . . . . . . . . . 323 4. Further Properties of the Solution . . . . . . . . . . . . . . . . . . . . 328 5. The \"Zero Mass\" Case . . . . . . . . . . . . . . . . . . . . . . . . . 332 6. The Case of Dimension N = 1 (Necessary and Sufficient Conditions) . . . . . 335 Appendix. Technical Results . . . . . . . . . . . . . . . . . . . . . . . . 338

Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology

Abstract: [1] A new 2° resolution global climatology of the mixed layer depth (MLD) based on individual profiles is constructed. Previous global climatologies have been based on temperature or density-gridded climatologies. The criterion selected is a threshold value of temperature or density from a near-surface value at 10 m depth (ΔT = 0.2°C or Δσθ = 0.03 kg m−3). A validation of the temperature criterion on moored time series data shows that the method is successful at following the base of the mixed layer. In particular, the first spring restratification is better captured than with a more commonly used larger criteria. In addition, we show that for a given 0.2°C criterion, the MLD estimated from averaged profiles results in a shallow bias of 25% compared to the MLD estimated from individual profiles. A new global seasonal estimation of barrier layer thickness is also provided. An interesting result is the prevalence in mid- and high-latitude winter hemispheres of vertically density-compensated layers, creating an isopycnal but not mixed layer. Consequently, we propose an optimal estimate of MLD based on both temperature and density data. An independent validation of the maximum annual MLD with oxygen data shows that this oxygen estimate may be biased in regions of Ekman pumping or strong biological activity. Significant differences are shown compared to previous climatologies. The timing of the seasonal cycle of the mixed layer is shifted earlier in the year, and the maximum MLD captures finer structures and is shallower. These results are discussed in light of the different approaches and the choice of criterion.

Defining the clinical course of multiple sclerosis: The 2013 revisions

Abstract: Accurate clinical course descriptions (phenotypes) of multiple sclerosis (MS) are important for communication, prognostication, design and recruitment of clinical trials, and treatment decision-making. Standardized descriptions published in 1996 based on a survey of international MS experts provided purely clinical phenotypes based on data and consensus at that time, but imaging and biological correlates were lacking. Increased understanding of MS and its pathology, coupled with general concern that the original descriptors may not adequately reflect more recently identified clinical aspects of the disease, prompted a re-examination of MS disease phenotypes by the International Advisory Committee on Clinical Trials of MS. While imaging and biological markers that might provide objective criteria for separating clinical phenotypes are lacking, we propose refined descriptors that include consideration of disease activity (based on clinical relapse rate and imaging findings) and disease progression. Strategies for future research to better define phenotypes are also outlined.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.