Rapid isolation, expansion and differentiation of osteoprogenitors from full-term umbilical cord blood

There is an urgent clinical requirement for appropriate bone substitutes that can be used for the repair and regeneration of diseased or damaged skeletal tissues. Cell-sourcing limitations in particular have affected progress, largely because of the shortage of accessible tissues capable of yielding sufficient numbers of viable osteoprogenitor cells. Previous work has suggested that umbilical cord blood (UCB) contains circulating progenitor cells (mesenchymal stem cells) capable of osteogenic differentiation, although a comparable number of reports refute this claim. From a screen of more than 20 different culture conditions, we have identified an optimal, simple, and reliable technique to generate, from full-term human UCB, stromal cells with the ability to undergo rapid osteogenic differentiation. By comparing different sorting and culture strategies, we demonstrated that early exposure of mononuclear UCB cells to medium conditioned by osteoblastic cells in the presence of osteogenic supplements and human plasma, markedly increased the frequency of stromal cell growth, the rate of osteogenic differentiation, and their attachment to and spreading on calcium phosphate scaffolds. These findings suggest that full-term UCB may act as an appropriate source of osteoprogenitor cells, which will impact significantly on the development of autologous tissue- engineered bone constructs

Scientific Computing Plan for the ECCE Detector at the Electron Ion Collider

The Electron Ion Collider (EIC) is the next generation of precision QCD facility to be built at Brookhaven National Laboratory in conjunction with Thomas Jefferson National Laboratory. There are a significant number of software and computing challenges that need to be overcome at the EIC. During the EIC detector proposal development period, the ECCE consortium began identifying and addressing these challenges in the process of producing a complete detector proposal based upon detailed detector and physics simulations. In this document, the software and computing efforts to produce this proposal are discussed; furthermore, the computing and software model and resources required for the future of ECCE are described

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector

Scientific computing plan for the ECCE detector at the Electron Ion Collider

The Electron Ion Collider (EIC) is the next generation of precision QCD facility to be built at Brookhaven National Laboratory in conjunction with Thomas Jefferson National Laboratory. There are a significant number of software and computing challenges that need to be overcome at the EIC. During the EIC detector proposal development period, the ECCE consortium began identifying and addressing these challenges in the process of producing a complete detector proposal based upon detailed detector and physics simulations. In this document, the software and computing efforts to produce this proposal are discussed; furthermore, the computing and software model and resources required for the future of ECCE are described