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

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

Exploring deliberate practice in medicine: how do physicians learn in the workplace?

Medical professionals need to keep on learning as part of their everyday work to deliver high-quality health care. Although the importance of physicians’ learning is widely recognized, few studies have investigated how they learn in the workplace. Based on insights from deliberate practice research, this study examined the activities physicians engage in during their work that might further their professional development. As deliberate practice requires a focused effort to improve performance, the study also examined the goals underlying this behaviour. Semi-structured interviews were conducted with 50 internal medicine physicians: 19 residents, 18 internists working at a university hospital, and 13 working at a non-university hospital. The results showed that learning in medical practice was very much embedded in clinical work. Most relevant learning activities were directly related to patient care rather than motivated by competence improvement goals. Advice and feedback were sought when necessary to provide this care. Performance standards were tied to patients’ conditions. The patients encountered and the discussions with colleagues about patients were valued most for professional development, while teaching and updating activities were also valued in this respect. In conclusion, physicians’ learning is largely guided by practical experience rather than deliberately sought. When professionals interact in diagnosing and treating patients to achieve high-quality care, their experiences contribute to expertise development. However, much could be gained from managing learning opportunities more explicitly. We offer suggestions for increasing the focus on learning in medical practice and further research

The SPECTRA Collaboration OMERACT Special Interest Group: Current Research and Future Directions

Objective High-resolution peripheral quantitative computed tomography (HR-pQCT) has the potential to improve radiographic progression determination in clinical trials and longitudinal observational studies. The goal of this work was to describe the current state of research presented at Outcome Measures in Rheumatology (OMERACT) 2016 and ensuing future directions outlined during discussion among attendees. Methods At OMERACT 2016, SPECTRA (Study grouP for xtrEme-Computed Tomography in Rheumatoid Arthritis) introduced efforts to (1) validate the HR-pQCT according to OMERACT guidelines, focusing on rheumatoid arthritis (RA), and (2) find alternatives for automated joint space width (JSW) analysis. The Special Interest Group (SIG) was presented to patient research partners, physicians/researchers, and SIG leaders followed by a 40-min discussion on future directions. Results A consensus definition for RA erosion using HR-pQCT was demonstrated through a systematic literature review and a Delphi exercise. Histopathology and perfusion studies were presented that analyzed the true characteristics of cortical breaks in HR-pQCT images, and to provide criterion validity. Results indicate that readers were able to discriminate between erosion and small vascular channels. Moderate reliability (ICC 0.206–0.871) of direct erosion size measures was shown, which improved (> 0.9) only when experienced readers were considered. Quantification of erosion size was presented for scoring, direct measurement, and volumetric approaches, as well as a reliability exercise for direct measurement. Three methods for JSW measurement were compared, all indicating excellent reproducibility with differences at the extremes (i.e., near-zero and joint edge thickness). Conclusion Initial reports on HR-pQCT are promising; however, to consider its use in clinical trials and longitudinal observational studies, it is imperative to assess the responsiveness of erosion measurement quantification

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Increasing vegetable intakes: rationale and systematic review of published interventions

Purpose While the health benefits of a high fruit and vegetable consumption are well known and considerable work has attempted to improve intakes, increasing evidence also recognises a distinction between fruit and vegetables, both in their impacts on health and in consumption patterns. Increasing work suggests health benefits from a high consumption specifically of vegetables, yet intakes remain low, and barriers to increasing intakes are prevalent making intervention difficult. A systematic review was undertaken to identify from the published literature all studies reporting an intervention to increase intakes of vegetables as a distinct food group. Methods Databases—PubMed, PsychInfo and Medline—were searched over all years of records until April 2015 using pre-specified terms. Results Our searches identified 77 studies, detailing 140 interventions, of which 133 (81 %) interventions were conducted in children. Interventions aimed to use or change hedonic factors, such as taste, liking and familiarity (n = 72), use or change environmental factors (n = 39), use or change cognitive factors (n = 19), or a combination of strategies (n = 10). Increased vegetable acceptance, selection and/or consumption were reported to some degree in 116 (83 %) interventions, but the majority of effects seem small and inconsistent. Conclusions Greater percent success is currently found from environmental, educational and multi-component interventions, but publication bias is likely, and long-term effects and cost-effectiveness are rarely considered. A focus on long-term benefits and sustained behaviour change is required. Certain population groups are also noticeably absent from the current list of tried interventions

Accelerating functional gene discovery in osteoarthritis

Osteoarthritis causes debilitating pain and disability, resulting in a considerable socioeconomic burden, yet no drugs are available that prevent disease onset or progression. Here, we develop, validate and use rapid-throughput imaging techniques to identify abnormal joint phenotypes in randomly selected mutant mice generated by the International Knockout Mouse Consortium. We identify 14 genes with functional involvement in osteoarthritis pathogenesis, including the homeobox gene Pitx1, and functionally characterize 6 candidate human osteoarthritis genes in mouse models. We demonstrate sensitivity of the methods by identifying age-related degenerative joint damage in wild-type mice. Finally, we phenotype previously generated mutant mice with an osteoarthritis-associated polymorphism in the Dio2 gene by CRISPR/Cas9 genome editing and demonstrate a protective role in disease onset with public health implications. We hope this expanding resource of mutant mice will accelerate functional gene discovery in osteoarthritis and offer drug discovery opportunities for this common, incapacitating chronic disease