| 1. |
- Conlon, Thomas M, et al.
(författare)
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Inhibition of LTβR signalling activates WNT-induced regeneration in lung
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 588:7836, s. 151-156
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Tidskriftsartikel (refereegranskat)abstract
- Lymphotoxin β-receptor (LTβR) signalling promotes lymphoid neogenesis and the development of tertiary lymphoid structures1,2, which are associated with severe chronic inflammatory diseases that span several organ systems3-6. How LTβR signalling drives chronic tissue damage particularly in the lung, the mechanism(s) that regulate this process, and whether LTβR blockade might be of therapeutic value have remained unclear. Here we demonstrate increased expression of LTβR ligands in adaptive and innate immune cells, enhanced non-canonical NF-κB signalling, and enriched LTβR target gene expression in lung epithelial cells from patients with smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke. Therapeutic inhibition of LTβR signalling in young and aged mice disrupted smoking-related inducible bronchus-associated lymphoid tissue, induced regeneration of lung tissue, and reverted airway fibrosis and systemic muscle wasting. Mechanistically, blockade of LTβR signalling dampened epithelial non-canonical activation of NF-κB, reduced TGFβ signalling in airways, and induced regeneration by preventing epithelial cell death and activating WNT/β-catenin signalling in alveolar epithelial progenitor cells. These findings suggest that inhibition of LTβR signalling represents a viable therapeutic option that combines prevention of tertiary lymphoid structures1 and inhibition of apoptosis with tissue-regenerative strategies.
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| 2. |
- Menkveld, Albert J., et al.
(författare)
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Nonstandard Errors
- 2024
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Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
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Tidskriftsartikel (refereegranskat)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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| 3. |
- Moore, Josh, et al.
(författare)
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OME-Zarr : A cloud-optimized bioimaging file format with international community support
- 2023
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Ingår i: Histochemistry and Cell Biology. - : Springer Nature. - 1432-119X .- 0948-6143. ; 160:3, s. 223-251
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Tidskriftsartikel (refereegranskat)abstract
- A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.
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| 4. |
- Sukhija, Bhavya, et al.
(författare)
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GOSAFEOPT : Scalable safe exploration for global optimization of dynamical systems
- 2023
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Ingår i: Artificial Intelligence. - : Elsevier BV. - 0004-3702 .- 1872-7921. ; 320
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Tidskriftsartikel (refereegranskat)abstract
- Learning optimal control policies directly on physical systems is challenging. Even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. This work proposes GOSAFEOPT as the first provably safe and optimal algorithm that can safely discover globally optimal policies for systems with high-dimensional state space. We demonstrate the superiority of GOSAFEOPT over competing model-free safe learning methods in simulation and hardware experiments on a robot arm.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
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