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- Koundouri, P., et al.
(författare)
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Methodology for Integrated Socio-economic Assessment of Multi-use Offshore Platforms.
- 2017
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Ingår i: In: Koundouri P. (eds) The Ocean of Tomorrow. Environment & Policy, vol 56. Springer, Cham. - 9783319557700 ; , s. 11-26
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents the methodology employed for the Integrated Socio-Economic Assessment (MISEA) of different designs of Multi-Use Offshore Platforms (MUOPs). The methodology allows for the identification, the valuationand the assessment of the potential impacts and their magnitude. The analysis considers a number of feasible designs of MUOP investments, and the likely responsesof those impacted by the investment project. The approach provides decision-makers with a valuable tool to assess whether a MUOP project increases the overall social welfare and hence should be undertaken. This is performed under alternative specifications regarding platform design, the discount rate and the stream of net benefits, if a Cost-Benefit Analysis (CBA) is to be followed or a sensitivity analysis of selected criteria in a Multi-Criteria Decision Analysis (MCDA) framework. Themethodology can support the implementation of policies aiming at achieving a goodenvironmental status of the EU’s marine waters and the protection of the resource base upon which marine-related economic and social activities depend.
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| 3. |
- Kübler, André, et al.
(författare)
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Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation.
- 2015
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Ingår i: Journal of Pathology. - : Wiley. - 0022-3417 .- 1096-9896. ; 235:3, s. 431-444
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Tidskriftsartikel (refereegranskat)abstract
- Active tuberculosis (TB) often presents with advanced pulmonary disease, including irreversible lung damage and cavities. Cavitary pathology contributes to antibiotic failure, transmission, morbidity and mortality. Matrix metalloproteinases (MMPs), in particular MMP-1 are implicated in TB pathogenesis. We explored the mechanisms relating MMP/TIMP imbalance to cavity formation in a modified rabbit model of cavitary TB. Our model results in consistent progression of consolidation to human-like cavities (100% by day 28) with resultant bacillary burdens (>10(7) CFU/g) far greater than those found in matched granulomatous tissue (10(5) CFU/g). Using a novel, breath-hold computerized tomography scanning and image analysis protocol. We show that cavities develop rapidly from areas of densely consolidated tissue. Radiological change correlated with a decrease in functional lung tissue as estimated by changes in lung density during controlled pulmonary expansion (R(2) =0.6356, p < 0.0001). We demonstrated that the expression of interstitial collagenase (MMP-1) is specifically greater in cavitary compared to granulomatous lesions (p < 0.01), and that TIMP-3 significantly decreases at the cavity surface. Our findings demonstrate that an MMP-1/TIMP imbalance, is associated with the progression of consolidated regions to cavities containing very high bacterial burdens. Our model provided mechanistic insight, correlating with human disease at the pathological, microbiological and molecular levels,. It also provides a strategy to investigate therapeutics in the context of complex TB pathology. We used these findings to predict a MMP/TIMP balance in active TB; and confirmed this in human plasma, revealing the potential of MMP/TIMP levels as key components of a diagnostic matrix aimed at distinguishing active from latent TB (PPV=92.9%; 95%CI 66.1-99.8%, NPV=85.6%; 95%CI 77.0-91.9%).
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| 5. |
- Rauniyar, Ashish, et al.
(författare)
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Federated learning for medical applications : A taxonomy, current trends, challenges, and future research directions
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unraveling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy. Further augmenting our discourse, recent literature has unveiled the inherent robustness and generalization of FL models compared to traditional data-driven medical applications. We hope that this review endeavors to serve as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.
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| 6. |
- Rauniyar, Ashish, et al.
(författare)
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Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:5, s. 7374-7398
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.
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