9 research outputs found

    The plastic nature of the human bone-periodontal ligament-tooth fibrous joint.

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    This study investigates bony protrusions within a narrowed periodontal ligament space (PDL-space) of a human bone-PDL-tooth fibrous joint by mapping structural, biochemical, and mechanical heterogeneity. Higher resolution structural characterization was achieved via complementary atomic force microscopy (AFM), nano-transmission X-ray microscopy (nano-TXM), and microtomography (MicroXCT™). Structural heterogeneity was correlated to biochemical and elemental composition, illustrated via histochemistry and microprobe X-ray fluorescence analysis (μ-XRF), and mechanical heterogeneity evaluated by AFM-based nanoindentation. Results demonstrated that the narrowed PDL-space was due to invasion of bundle bone (BB) into PDL-space. Protruded BB had a wider range with higher elastic modulus values (2-8GPa) compared to lamellar bone (0.8-6GPa), and increased quantities of Ca, P and Zn as revealed by μ-XRF. Interestingly, the hygroscopic 10-30μm interface between protruded BB and lamellar bone exhibited higher X-ray attenuation similar to cement lines and lamellae within bone. Localization of the small leucine rich proteoglycan biglycan (BGN) responsible for mineralization was observed at the PDL-bone interface and around the osteocyte lacunae. Based on these results, it can be argued that the LB-BB interface was the original site of PDL attachment, and that the genesis of protruded BB identified as protrusions occurred as a result of shift in strain. We emphasize the importance of bony protrusions within the context of organ function and that additional study is warranted

    Machine Learning in High Energy Physics Community White Paper

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    peer reviewedMachine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit
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