298 research outputs found
Unravelling the construction of silver filigree spheres from a seventeenth century shipwreck using non-invasive imaging
Developing staining protocols for visualization of tissue-engineering scaffolds using micro computed tomography in native wet state
Intermittent applied mechanical loading induces subchondral bone thickening that may be intensified locally by contiguous articular cartilage lesions
Objectives: Changes in subchondral bone (SCB) and cross-talk with articular cartilage (AC) have been linked to osteoarthritis (OA). Using micro-computed tomography (micro-CT) this study: (1) examines changes in SCB architecture in a non-invasive loading mouse model in which focal AC lesions are induced selectively in the lateral femur, and (2) determines any modifications in the contralateral knee, linked to changes in gait, which might complicate use of this limb as an internal control. Methods: Right knee joints of CBA mice were loaded: once with 2weeks of habitual use (n=7), for 2weeks (n=8) or for 5weeks (n=5). Both left (contralateral) and right (loaded) knees were micro-CT scanned and the SCB and trabecular bone analysed. Gait analysis was also performed. Results: These analyses showed a significant increase in SCB thickness in the lateral compartments in joints loaded for 5weeks, which was most marked in the lateral femur; the contralateral non-loaded knee also showed transient SCB thickening (loaded once and repetitively). Epiphyseal trabecular bone BV/TV and trabecular thickness were also increased in the lateral compartments after 5 weeks of loading, and in all joint compartments in the contralateral knee. Gait analysis showed that applied loading only affected gait in the contralateral himd-limb in all groups of mice from the second week after the first loading episode. Conclusions: These data indicate a spatial link between SCB thickening and AC lesions following mechanical trauma, and the clear limitations associated with the use of contralateral joints as controls in such OA models, and perhaps in OA diagnosis
ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing
High resolution peripheral quantitative computed tomography (HR-pQCT) is an
imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a
wide range of applications, primarily focused on bone to improve our
understanding of musculoskeletal diseases, assess epidemiological associations,
and evaluate the effects of pharmaceutical interventions. Processing HR-pQCT
images has largely been supported using the scanner manufacturer scripting
language (Image Processing Language, IPL, Scanco Medical). However, by
expanding image processing workflows outside of the scanner manufacturer
software environment, users have the flexibility to apply more advanced
mathematical techniques and leverage modern software packages to improve image
processing. The ORMIR_XCT Python package was developed to reimplement some
existing IPL workflows and provide an open and reproducible package allowing
for the development of advanced HR-pQCT data processing workflows
Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance
High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search
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