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- Robbe, P, et al.
(författare)
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Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features
- 2022
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Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:11, s. 1675-
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Tidskriftsartikel (refereegranskat)abstract
- The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia.
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- Rauschmeier, R, et al.
(författare)
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Bhlhe40 function in activated B and TFH cells restrains the GC reaction and prevents lymphomagenesis
- 2021
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Ingår i: The Journal of experimental medicine. - : Rockefeller University Press. - 1540-9538 .- 0022-1007. ; 219:2
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Tidskriftsartikel (refereegranskat)abstract
- The generation of high-affinity antibodies against pathogens and vaccines requires the germinal center (GC) reaction, which relies on a complex interplay between specialized effector B and CD4 T lymphocytes, the GC B cells and T follicular helper (TFH) cells. Intriguingly, several positive key regulators of the GC reaction are common for both cell types. Here, we report that the transcription factor Bhlhe40 is a crucial cell-intrinsic negative regulator affecting both the B and T cell sides of the GC reaction. In activated CD4 T cells, Bhlhe40 was required to restrain proliferation, thus limiting the number of TFH cells. In B cells, Bhlhe40 executed its function in the first days after immunization by selectively restricting the generation of the earliest GC B cells but not of early memory B cells or plasmablasts. Bhlhe40-deficient mice with progressing age succumbed to a B cell lymphoma characterized by the accumulation of monoclonal GC B-like cells and polyclonal TFH cells in various tissues.
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- Avula, Ramana R., et al.
(författare)
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Adversarial Inference Control in Cyber-Physical Systems : A Bayesian Approach With Application to Smart Meters
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 24933-24948
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible ’Co-LivEn’ dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.
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