Un modèle à criticalité auto-régulée de la magnétosphère terrestre

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Nuclear Shell Model Calculations with Fundamental Nucleon-Nucleon Interactions

Some fundamental Nucleon-Nucleon interactions and their applications to finite nuclei are reviewed. Results for the few-body systems and from Shell-Model calculations are discussed and compared to point out the advantages and disadvantages of the different Nucleon-Nucleon interactions. The recently developed Drexel University Shell Model (DUSM) code is mentioned.Comment: 16 pages, 4 figures. To appear in Phys. Rep. 199

In vivo remodeling of fibroblast-derived vascular scaffolds implanted for 6 months in rats

There is a clinical need for tissue-engineered small-diameter (<6 mm) vascular grafts since clinical applications are halted by the limited suitability of autologous or synthetic grafts. This study uses the self-assembly approach to produce a fibroblast-derived decellularized vascular scaffold (FDVS) that can be available off-the-shelf. Briefly, extracellular matrix scaffolds were produced using human dermal fibroblasts sheets rolled around a mandrel, maintained in culture to allow for the formation of cohesive and three-dimensional tubular constructs, and decellularized by immersion in deionized water. The FDVSs were implanted as an aortic interpositional graft in six Sprague-Dawley rats for 6 months. Five out of the six implants were still patent 6 months after the surgery. Histological analysis showed the infiltration of cells on both abluminal and luminal sides, and immunofluorescence analysis suggested the formation of neomedia comprised of smooth muscle cells and lined underneath with an endothelium. Furthermore, to verify the feasibility of producing tissue-engineered blood vessels of clinically relevant length and diameter, scaffolds with a 4.6 mm inner diameter and 17 cm in length were fabricated with success and stored for an extended period of time, while maintaining suitable properties following the storage period. This novel demonstration of the potential of the FDVS could accelerate the clinical availability of tissue-engineered blood vessels and warrants further preclinical studies

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing