The upcoming Surface Water and Ocean Topography (SWOT) mission will measure water surface heights and widths for rivers wider than 100 m. At its native resolution, SWOT height errors are expected to be on the order of meters, which prevent the calculation of water surface slopes and the use of slope‐dependent discharge equations. To mitigate height and width errors, the high‐resolution measurements will be grouped into reaches (∼5 to 15 km), where slope and discharge are estimated. We describe three automated river segmentation strategies for defining optimum reaches for discharge estimation: (1) arbitrary lengths, (2) identification of hydraulic controls, and (3) sinuosity. We test our methodologies on 9 and 14 simulated SWOT overpasses over the Sacramento and the Po Rivers, respectively, which we compare against hydraulic models of each river. Our results show that generally, height, width, and slope errors decrease with increasing reach length. However, the hydraulic controls and the sinuosity methods led to better slopes and often height errors that were either smaller or comparable to those of arbitrary reaches of compatible sizes. Estimated discharge errors caused by the propagation of height, width, and slope errors through the discharge equation were often smaller for sinuosity (on average 8.5% for the Sacramento and 6.9% for the Po) and hydraulic control (Sacramento: 7.3% and Po: 5.9%) reaches than for arbitrary reaches of comparable lengths (Sacramento: 8.6% and Po: 7.8%). This analysis suggests that reach definition methods that preserve the hydraulic properties of the river network may lead to better discharge estimates.
Space‐borne instruments can measure river water surface elevation, slope, and width. Remote sensing of river discharge in ungauged basins is far more challenging, however. This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission using a variant of the classical variational data assimilation method “4D‐Var.” The variational assimilation scheme simultaneously estimates discharge, river bathymetry, and bed roughness in the context of a 1.5 D full Saint‐Venant hydraulic model. Algorithms and procedures are developed to apply the method to fully ungauged basins. The method was tested on the Po and Sacramento Rivers. The SWOT hydrology simulator was used to produce synthetic SWOT observations at each overpass time by simulating the interaction of SWOT radar measurements with the river water surface and nearby land surface topography at a scale of approximately 1 m, thus accounting for layover, thermal noise, and other effects. SWOT data products were synthesized by vectorizing the simulated radar returns, leading to height and width estimates at 200 m increments along the river centerlines. The ingestion of simulated SWOT data generally led to local improvements on prior bathymetry and roughness estimates which allowed the prediction of river discharge at the overpass times with relative root mean squared errors of 12.1% and 11.2% for the Po and Sacramento Rivers, respectively. Nevertheless, equifinality issues that arise from the simultaneous estimation of bed elevation and roughness may prevent their use for different applications, other than discharge estimation through the presented framework.
Mesenchymal stromal cell (MSC)-derived exosomes play a promising role in regenerative medicine. Their trophic and immunomodulatory potential has made them a promising candidate for cardiac regeneration and repair. Numerous studies have demonstrated that MSC-derived exosomes can replicate the antiinflammatory, anti-apoptotic, and pro-angiogenic and antifibrotic effects of their parent cells and are considered a substitute for cell-based therapies. In addition, their lower tumorigenic risk, superior immune tolerance, and superior stability compared with their parent stem cells make them an attractive option in regenerative medicine. The therapeutic effects of MSC-derived exosomes have consequently been evaluated for application in cardiac regeneration and repair. In this review, we summarize the potential mechanisms and therapeutic effects of MSC-derived exosomes in cardiac regeneration and repair and provide evidence to support their clinical application.
The evolution of resistance to insecticides is well known to be closely associated with the overexpression of detoxifying enzymes. Although the role of glutathione S-transferase (GST) genes in insecticide resistance has been widely reported, the underlying regulatory mechanisms are poorly understood. Here, one GST gene (GSTu1) and its antisense transcript (lnc-GSTu1-AS) were identified and cloned, and both of them were upregulated in several chlorantraniliprole-resistant Plutella xylostella populations. GSTu1 was confirmed to be involved in chlorantraniliprole resistance by direct degradation of this insecticide. Furthermore, we demonstrated that lnc-GSTu1-AS interacted with GSTu1 by forming an RNA duplex, which masked the binding site of miR-8525-5p at the GSTu1-3′UTR. In summary, we revealed that lnc-GSTu1-AS maintained the mRNA stability of GSTu1 by preventing its degradation that could have been induced by miR-8525-5p and thus increased the resistance of P. xylostella to chlorantraniliprole. Our findings reveal a new noncoding RNA-mediated pathway that regulates the expression of detoxifying enzymes in insecticide-resistant insects and offer opportunities for the further understanding of the mechanisms of insecticide and drug resistance.
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