11 research outputs found

    Improving pulse crops as a source of protein, starch and micronutrients

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    Pulse crops have been known for a long time to have beneficial nutritional profiles for human diets but have been neglected in terms of cultivation, consumption and scientific research in many parts of the world. Broad dietary shifts will be required if anthropogenic climate change is to be mitigated in the future, and pulse crops should be an important component of this change by providing an environmentally sustainable source of protein, resistant starch and micronutrients. Further enhancement of the nutritional composition of pulse crops could benefit human health, helping to alleviate micronutrient deficiencies and reduce risk of chronic diseases such as type 2 diabetes. This paper reviews current knowledge regarding the nutritional content of pea (Pisum sativum L.) and faba bean (Vicia faba L.), two major UK pulse crops, and discusses the potential for their genetic improvement

    Supplemental Material - Recommendations for the Management of Incidental Musculoskeletal Findings on MRI and CT

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    Supplemental Material for Recommendations for the Management of Incidental Musculoskeletal Findings on MRI and CT by Gina Di Primio, Gordon J. Boyd, Christopher I. Fung, Casey Hurrell, Gary L. Brahm, Jeffery R. Bird, Steven J. Co and Iain D. C. Kirkpatrick in Canadian Association of Radiologists Journal</p

    Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

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    Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally
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