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| http://dx.doi.org/10.1109/mitp.2018.011301733 | DOI Listing |
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The Holobiont concept in ruminant physiology - more of the same, or something new and meaningful to food quality, food security, and animal health?
J Dairy Sci
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Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States.
The holobiont concept has emerged as an attempt to recognize and describe the myriad interactions and physiological signatures inherent to a host organism, as impacted by the microbial communities that colonize and/or co-inhabit the environment within which the host resides. The field acknowledges and draws upon principles from evolution, ecology, genetics, and biology, and in many respects has been "pushed" by the advent of high throughput DNA sequencing and, to a lesser extent, other "omics"-based technologies. Despite the explosion in data generation and analyses, much of our current understanding of the human and ruminant "holobiont" is based on compositional forms of data and thereby, restricted to describing host phenotypes via associative or correlative studies.
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Cornwall Intellectual Disability Equitable Research (CIDER), Cornwall Partnership NHS Foundation Trust, Truro, UK; and CIDER, Peninsula School of Medicine, University of Plymouth, UK.
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- The Department of Health and Social Care released a report in November 2023, highlighting the negative impacts of solitary confinement on people with learning disabilities and autism, but the recommendations are largely not supported by evidence-based practices.
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Background: The development and potential of artificial intelligence (AI) is remarkable. Its application in all medical disciplines, including rheumatology, is attracting attention. To what extent AI is already used in clinical routine in rheumatology is unknown.
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Sci Rep
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Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea.
In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging.
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