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- Kloske, C. M., et al.
(författare)
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APOE and immunity: Research highlights
- 2023
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Ingår i: Alzheimers & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 19:6, s. 2677-2696
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Forskningsöversikt (refereegranskat)abstract
- INTRODUCTIONAt the Alzheimer's Association's APOE and Immunity virtual conference, held in October 2021, leading neuroscience experts shared recent research advances on and inspiring insights into the various roles that both the apolipoprotein E gene (APOE) and facets of immunity play in neurodegenerative diseases, including Alzheimer's disease and other dementias. METHODSThe meeting brought together more than 1200 registered attendees from 62 different countries, representing the realms of academia and industry. RESULTSDuring the 4-day meeting, presenters illuminated aspects of the cross-talk between APOE and immunity, with a focus on the roles of microglia, triggering receptor expressed on myeloid cells 2 (TREM2), and components of inflammation (e.g., tumor necrosis factor alpha [TNF alpha]). DISCUSSIONThis manuscript emphasizes the importance of diversity in current and future research and presents an integrated view of innate immune functions in Alzheimer's disease as well as related promising directions in drug development.
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- Frisoni, G. B., et al.
(författare)
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The probabilistic model of Alzheimer disease: the amyloid hypothesis revised
- 2022
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Ingår i: Nature Reviews Neuroscience. - : Springer Science and Business Media LLC. - 1471-003X .- 1471-0048. ; 23, s. 53-66
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Tidskriftsartikel (refereegranskat)abstract
- The amyloid hypothesis has been the dominant model for the pathogenesis of Alzheimer disease for several decades. In this Perspective, Giovanni Frisoni and colleagues examine evidence for and against this hypothesis before outlining an alternative model, the probabilistic model of Alzheimer disease. The current conceptualization of Alzheimer disease (AD) is driven by the amyloid hypothesis, in which a deterministic chain of events leads from amyloid deposition and then tau deposition to neurodegeneration and progressive cognitive impairment. This model fits autosomal dominant AD but is less applicable to sporadic AD. Owing to emerging information regarding the complex biology of AD and the challenges of developing amyloid-targeting drugs, the amyloid hypothesis needs to be reconsidered. Here we propose a probabilistic model of AD in which three variants of AD (autosomal dominant AD, APOE epsilon 4-related sporadic AD and APOE epsilon 4-unrelated sporadic AD) feature decreasing penetrance and decreasing weight of the amyloid pathophysiological cascade, and increasing weight of stochastic factors (environmental exposures and lower-risk genes). Together, these variants account for a large share of the neuropathological and clinical variability observed in people with AD. The implementation of this model in research might lead to a better understanding of disease pathophysiology, a revision of the current clinical taxonomy and accelerated development of strategies to prevent and treat AD.
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- Kant, Shashi, et al.
(författare)
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Federated Learning Using Three-Operator ADMM
- 2023
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Ingår i: IEEE Journal on Selected Topics in Signal Processing. - : IEEE Signal Processing Society. - 1932-4553 .- 1941-0484. ; 17:1, s. 205-221
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Tidskriftsartikel (refereegranskat)abstract
- Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and has often direct access to rich datasets. In this paper, we argue that it is much more beneficial to leverage the rich data on the edge server then utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model in parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.
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