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Reperfusion injury is one of the major causes of disability and death caused by ischemic stroke, and drug development focuses mainly on single neuron protection. However, different kinds of cells in the neurovascular units (NVUs), including neurons, microglia and vascular endothelial cells, are pathologically changed after cerebral ischemia–reperfusion injury, resulting in an urgent need to develop a drug delivery system to comprehensively protect the kinds of cells involved in the NVU. Herein, we have constructed a c(RGDyK) peptide modified, NF-κB inhibitor caffeic acid phenethyl ester (CAPE)-loaded and reactive nitrogen species (RNS) stimuli-responsive liposomal nanocarrier (R-Lipo-CAPE) to target ischemic lesions and then remodel the NVU to reduce the progression of cerebral ischemia–reperfusion injury. The R-Lipo-CAPE liposomes were approximately 170 nm with a zeta potential of -30.8 ± 0.2 mV. The in vitro CAPE release behavior from R-Lipo-CAPE showed an RNS-dependent pattern. For in vivo studies, transient middle cerebral artery occlusion/reperfusion (MCAO) model mice treated with R-Lipo-CAPE had the least neurological impairment and decreased brain tissue damage, with an infarct area of 13%, compared with those treated with saline of 53% or free CAPE of 38%. Furthermore, microglia in the ischemic brain were polarized to the tissue-repairing M2 phenotype after R-Lipo-CAPE treatment. In addition, R-Lipo-CAPE-treated mice displayed a prominent down-regulated expression of MMP-9 and restored expression of the tight junction protein claudin-5. This proof-of-concept indicates that R-Lipo-CAPE is a promising nanomedicine for the treatment of cerebral ischemia–reperfusion injury through the regulation of neurovascular units. Based on the complex mechanism and difficulty in treatment of cerebral ischemia-reperfusion injury, the overall regulation of neurovascular unit has become an extremely important target. However, little nanomedicine has been directed to remodel the neurovascular units in targeted cerebral ischemia-reperfusion injury therapy. Here, c(RGDyK) peptide modified reactive nitrogen species (RNS) stimuli-responsive liposomal nanocarrier loaded with a NF-κB inhibitor (CAPE), was designed to simultaneously regulate various cells in the microenvironment of cerebral ischemia-reperfusion injury to remodel the neurovascular units. Our in vitro and in vivo data showed that the intelligent nanocarrier exerted the ability of pathological signal stimuli-responsive drug release, cerebral ischemia-reperfusion injury site targeting and neurovascular units remodeling through reducing neuron apoptosis, regulating microglia polarization and repairing vascular endothelial cell. Overall, the intelligent liposomal drug delivery system was a promising and safe nanomedicine in the perspective of cerebral ischemia-reperfusion injury treatment. [Display omitted] [ABSTRACT FROM AUTHOR]

• Radar image and spectral images were fused for monitoring soil salinity. • Soil salinity inversion models were built based on Sentinel-1/2 fusion and machine learning. • GS was used to fuse images of Sentinel-1 and Sentinel-2. In the application of satellite remote sensing data to salinity inversion in bare soil, multispectral satellites enjoy richer wavelength bands compared with synthetic aperture radar (SAR), but their vulnerability to cloudy weather limits the inversion accuracy. In contrast, SAR has stronger penetration, is less affected by cloudy weather, and can work around the clock. Therefore, in order to monitor soil salinity over a longer period of time and under various climatic conditions, and to further improve the accuracy of salinity inversion, we fused Sentinel-1 and Sentinel-2 images and investigated the feasibility of SAR and multispectral satellite image fusion in salinity inversion in this paper. And we conducted experiments in Hetao Irrigation Area, Inner Mongolia, China. Gram-Schmidt(GS) was used to fuse Sentinel-1 and Sentinel-2 images at the same time, and maximum-normalization was carried out on the DN value of the fused image. Then some remote indices conducted with the normalized DN, together with high relevant fused single bands were input into Back Propagation(BP), Support Vector Machine(SVM), Random Forest(RF) as independent variables after two-tailed significance test and Variable importance in projection(VIP) selection, for soil salinity inversion model construction. Finally, the inversion results of the study area were evaluated with R2, RMSE, RPD, RSS, and RPIQ. The results of this study showed that the bare soil salinity inversion based on the fusion of VV(Sentinel-1) and Sentinel-2 had a higher accuracy than that on the fusion of VH (Sentinel-1) and Sentinel-2 image and that without the fusion. Among the three machine learning models, random forest (RF) achieved the most satisfying results (R2 = 0.801, RMSE = 0.686), followed by SVM(R2 = 0.624, RMSE = 0.875) and BP(R2 = 0.613, RMSE = 0.918). This study demonstrated the feasibility of fusing Sentinel-1 radar images with Sentinel-2 multispectral images to improve the accuracy of soil salinity inversion, and constructed the soil salinity inversion model based on machine learning and GS fusion. [ABSTRACT FROM AUTHOR]

Soil moisture and salinity are both important environmental variables for crop growth in agricultural production areas. Optical remote-sensing datasets from different sensors are available for estimating soil moisture and salinity from different spatial-temporal scales. Given the co-regulation of soil spectral reflectance (SR) by soil moisture and salinity, the simultaneous estimation of moisture and salinity in saline soil may result in great bias and uncertainty. To address this problem, soil samples were collected in the salinized area during irrigation. Synchronously, processed multi-spectral images were acquired from Sentinel-2 satellite. The spectrum mechanism responsive to soil moisture and salinity was verified by statistical tests, and its corresponding mathematical model (MSS model) was developed to identify the dominant factors affecting SR and to inverse moisture and salinity. The result showed that the effects of moisture and salinity were temporally constant (facilitation) and changing (from inhibition to facilitation), respectively, during the irrigation stages. The dominant factors in the variation of SR shifted from salinity and moisture-salinity interaction to moisture. Reliable accuracy was achieved in the moisture and salinity estimation using inverse MSS model. The profile from the series of estimations can further reveal the dynamic changes of soil moisture and salinity content during irrigation, and provide guidance for local irrigation management. • MSS model simulates response of soil spectrum to soil moisture and salinity. • Inverse MSS model achieves the simultaneous estimation at regional scale. • Spatial-temporal change of soil moisture and salinity is observed during irrigation. • Land-vegetation alternation causes soil salt aggregation in crop field. [ABSTRACT FROM AUTHOR]