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  1. Abstract

    Recent studies have reported worldwide vegetation suppression in response to increasing atmospheric vapor pressure deficit (VPD). Here, we integrate multisource datasets to show that increasing VPD caused by warming alone does not suppress vegetation growth in northern peatlands. A site-level manipulation experiment and a multiple-site synthesis find a neutral impact of rising VPD on vegetation growth; regional analysis manifests a strong declining gradient of VPD suppression impacts from sparsely distributed peatland to densely distributed peatland. The major mechanism adopted by plants in response to rising VPD is the “open” water-use strategy, where stomatal regulation is relaxed to maximize carbon uptake. These unique surface characteristics evolve in the wet soil‒air environment in the northern peatlands. The neutral VPD impacts observed in northern peatlands contrast with the vegetation suppression reported in global nonpeatland areas under rising VPD caused by concurrent warming and decreasing relative humidity, suggesting model improvement for representing VPD impacts in northern peatlands remains necessary.

     
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  2. Free, publicly-accessible full text available July 1, 2024
  3. 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. 
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    Free, publicly-accessible full text available May 24, 2024
  4. Retinal organoids are three-dimensional (3D) structures derived from human pluripotent stem cells (hPSCs) that mimic the retina’s spatial and temporal differentiation, making them useful as in vitro retinal development models. Retinal organoids can be assembled with brain organoids, the 3D self-assembled aggregates derived from hPSCs containing different cell types and cytoarchitectures that resemble the human embryonic brain. Recent studies have shown the development of optic cups in brain organoids. The cellular components of a developing optic vesicle-containing organoids include primitive corneal epithelial and lens-like cells, retinal pigment epithelia, retinal progenitor cells, axon-like projections, and electrically active neuronal networks. The importance of retinal organoids in ocular diseases such as age-related macular degeneration, Stargardt disease, retinitis pigmentosa, and diabetic retinopathy are described in this review. This review highlights current developments in retinal organoid techniques, and their applications in ocular conditions such as disease modeling, gene therapy, drug screening and development. In addition, recent advancements in utilizing extracellular vesicles secreted by retinal organoids for ocular disease treatments are summarized. 
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  5. The significant roles of extracellular vesicles (EVs) as intracellular mediators, disease biomarkers, and therapeutic agents, make them a scientific hotspot. In particular, EVs secreted by human stem cells show significance in treating neurological disorders, such as Alzheimer’s disease and ischemic stroke. However, the clinical applications of EVs are limited due to their poor targeting capabilities and low therapeutic efficacies after intravenous administration. Superparamagnetic iron oxide (SPIO) nanoparticles are biocompatible and have been shown to improve the targeting ability of EVs. In particular, ultrasmall SPIO (USPIO, <50 nm) are more suitable for labeling nanoscale EVs due to their small size. In this study, induced forebrain neural progenitor cortical organoids (iNPCo) were differentiated from human induced pluripotent stem cells (iPSCs), and the iNPCo expressed FOXG1, Nkx2.1, α-catenin, as well as β-tubulin III. EVs were isolated from iNPCo media, then loaded with USPIOs by sonication. Size and concentration of EV particles were measured by nanoparticle tracking analysis, and no significant changes were observed in size distribution before and after sonication, but the concentration decreased after labeling. miR-21 and miR-133b decreased after sonication. Magnetic resonance imaging (MRI) demonstrated contrast visualized for the USPIO labeled EVs embedded in agarose gel phantoms. Upon calculation, USPIO labeled EVs exhibited considerably shorter relaxation times, quantified as T2 and T2* values, reducing the signal intensity and generating higher MRI contrast compared to unlabeled EVs and gel only. Our study demonstrated that USPIO labeling was a feasible approach for in vitro tracking of brain organoid-derived EVs, which paves the way for further in vivo examination. 
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  6. The generalization of representations learned via contrastive learning depends crucially on what features of the data are extracted. However, we observe that the contrastive loss does not always sufficiently guide which features are extracted, a behavior that can negatively impact the performance on downstream tasks via “shortcuts”, i.e., by inadvertently suppressing important predictive features. We find that feature extraction is influenced by the difficulty of the so-called instance discrimination task (i.e., the task of discriminating pairs of similar points from pairs of dissimilar ones). Although harder pairs improve the representation of some features, the improvement comes at the cost of suppressing previously well represented features. In response, we propose implicit feature modification (IFM), a method for altering positive and negative samples in order to guide contrastive models towards capturing a wider variety of predictive features. Empirically, we observe that IFM reduces feature suppression, and as a result improves performance on vision and medical imaging tasks. The code is available at: https://github. com/joshr17/IFM. 
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  7. null (Ed.)