35 research outputs found

    High expression of MKP1/DUSP1 counteracts glioma stem cell activity and mediates HDAC inhibitor response

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    Abstract The elucidation of mechanisms involved in resistance to therapies is essential to improve the survival of patients with malignant gliomas. A major feature possessed by glioma cells that may aid their ability to survive therapy and reconstitute tumors is the capacity for self-renewal. We show here that glioma stem cells (GSCs) express low levels of MKP1, a dual-specificity phosphatase, which acts as a negative inhibitor of JNK, ERK1/2, and p38 MAPK, while induction of high levels of MKP1 expression are associated with differentiation of GSC. Notably, we find that high levels of MKP1 correlate with a subset of glioblastoma patients with better prognosis and overall increased survival. Gain of expression studies demonstrated that elevated MKP1 impairs self-renewal and induces differentiation of GSCs while reducing tumorigenesis in vivo. Moreover, we identified that MKP1 is epigenetically regulated and that it mediates the anti-tumor activity of histone deacetylase inhibitors (HDACIs) alone or in combination with temozolomide. In summary, this study identifies MKP1 as a key modulator of the interplay between GSC self-renewal and differentiation and provides evidence that the activation of MKP1, through epigenetic regulation, might be a novel therapeutic strategy to overcome therapy resistance in glioblastoma

    Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

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    A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

    Parsimonious Seismic Tomography with Poisson Voronoi Projections: Methodology and Validation

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    Ill‐posed seismic inverse problems are often solved using Tikhonov‐type regularization, that is, incorporation of damping and smoothing to obtain stable results. This typically results in overly smooth models, poor amplitude resolution, and a difficult choice between plausible models. Recognizing that the average of parameters can be better constrained than individual parameters, we propose a seismic tomography method that stabilizes the inverse problem by projecting the original high‐dimension model space onto random low‐dimension subspaces and then infers the high‐dimensional solution from combinations of such subspaces. The subspaces are formed by functions constant in Poisson Voronoi cells, which can be viewed as the mean of parameters near a certain location. The low‐dimensional problems are better constrained, and image reconstruction of the subspaces does not require explicit regularization. Moreover, the low‐dimension subspaces can be recovered by subsets of the whole dataset, which increases efficiency and offers opportunities to mitigate uneven sampling of the model space. The final (high‐dimension) model is then obtained from the low‐dimension images in different subspaces either by solving another normal equation or simply by averaging the low‐dimension images. Importantly, model uncertainty can be obtained directly from images in different subspaces. Synthetic tests show that our method outperforms conventional methods both in terms of geometry and amplitude recovery. The application to southern California plate boundary region also validates the robustness of our method by imaging geologically consistent features as well as strong along‐strike variations of San Jacinto fault that are not clearly seen using conventional methods
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