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Abstract Extracellular matrix (ECM) in the human tissue contains vesicles, which are defined as matrix‐bound nanovesicles (MBVs). MBVs serve as one of the functional components in ECM, recapitulating part of the regulatory roles and in vivo microenvironment. In this study, extracellular vesicles from culture supernatants (SuEVs) and MBVs are isolated from the conditioned medium or ECM, respectively, of 3D human mesenchymal stem cells. Nanoparticle tracking analysis shows that MBVs are smaller than SuEVs (100–150 nm). Transmission electron microscopy captures the typical cup shape morphology for both SuEVs and MBVs. Western blot reveals that MBVs have low detection of some SuEV markers such as syntenin‐1. miRNA analysis of MBVs shows that 3D microenvironment enhances the expression of miRNAs such as miR‐19a and miR‐21. In vitro functional analysis shows that MBVs can facilitate human pluripotent stem cell‐derived forebrain organoid recovery after starvation and promote high passage fibroblast proliferation. In macrophage polarization, 2D MBVs tend to suppress the pro‐inflammatory cytokine IL‐12 β , while 3D MBVs tend to enhance the anti‐inflammatory cytokine IL‐10. This study has the significance in advancing the understanding of the bio‐interface of nanovesicles with human tissue and the design of cell‐free therapy for treating neurological disorders such as ischemic stroke. 

Abstract Given a profinite group G of finite p -cohomological dimension and a pro- p quotient H of G by a closed normal subgroup N , we study the filtration on the Iwasawa cohomology of N by powers of the augmentation ideal in the group algebra of H . We show that the graded pieces are related to the cohomology of G via analogues of Bockstein maps for the powers of the augmentation ideal. For certain groups H , we relate the values of these generalized Bockstein maps to Massey products relative to a restricted class of defining systems depending on H . We apply our study to prove lower bounds on the p -ranks of class groups of certain nonabelian extensions of $\mathbb {Q}$ and to give a new proof of the vanishing of Massey triple products in Galois cohomology. 

Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.